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[Music] You're watching TBPN.
7:00
Today is Monday, July 7th, 2025.
7:03
We are live from the TBPN Ultra Dome, the temple of technology, the fortress of finance, the capital of capital.
7:12
We have an awesome show for you folks.
7:14
Hope you had a great July 4th.
7:14
Uh it was a wonderful July 4th here in California.
7:19
I know most of the listeners of the show were probably in Europe.
7:22
Um, but for those of you stayed domestic and served America, thank you.
7:28
Um, we have a whole bunch of news for you today and a stacked lineup, but let's go through it.
7:34
Celebrated by talking about business podcasting with David Senra. We did. And Rob Moore, we did. And it was fantastic. Yes.
7:42
Uh, no, it was a fantastic weekend.
7:45
I I went to a friend's place and uh they had catering for a pretty small party and uh and I as I was leaving I was like this is the work hard play hard lifestyle. Yeah.
7:56
And and my wife was laughing because many people mean when they say work hard, play hard. But it's true. It's true.
8:02
If you work hard you get to you get to play hard.
8:05
And when when you play when you have uh when you have friends over it can be very luxurious which is great.
8:08
Uh anyway, uh in some absolutely massive breaking news, Dwaresh Patel has updated his AGI timelines $30 billion off of Nvidia's market cap like that. Just kidding.
8:22
Uh it moved the stock was down. The stock was down only. 7%.
8:27
Probably not on that news.
8:28
Probably not on that news, but it but it but it should have been.
8:29
And this should have been market moving news.
8:30
And we will get into although it's not necessarily bad for Nvidia.
8:36
No, it's incredibly bullish.
8:36
his his breakdown is incredibly bullish on AGI and and and that and that's the back and forth here.
8:42
So, uh Metacritic Capital says uh f Dark Cash is out there calling AI hype overblown.
8:50
I know we stopped doing these things but likely the AI trade top is in and he could not have gotten it more wrong.
8:58
Dylan chimes in fire back. Yeah, bro.
9:00
Uh, bro said his taxes by 2028 and all white collar work by 2032.
9:09
And you think that's a bearish prediction?
9:11
He just thinks AGI 2027 stuff is wrong.
9:14
The market isn't pricing either of these scenarios, then I completely agree.
9:20
And Doresh chimes in uh and agrees and says, "The transformative impact I expect from AI over the next decade or two is very far from priced in."
9:26
And he shares a screenshot.
9:28
He says, "While this makes me bearish on transformative AI in the next few years, it makes me especially bullish on AI over the next decades.
9:36
When we do solve continuous learning, we'll see a huge discontinuity in the value of the models." Yep.
9:42
And we will get a lot more into this uh when he joins the show in 20 minutes. Yeah.
9:48
So his basic thesis is uh when you work with someone, you know, they have a set IQ, but they're also capable of continual learning.
9:54
you teach them and they learn and they adapt and then they they can remember some hard one lesson from years ago. Yeah.
10:01
I remember talking to somebody this this kind of like uh I don't know even know he's like a philosopher like user he's like a user experience designer and he said that um there's there's multiple ways to learn.
10:12
You can you can develop habits through just doing something the same every like really forcing yourself for a long time.
10:18
I wake up at 5:30 every morning.
10:18
Wake up at 5 5:30 every morning for years.
10:22
Eventually you just wake up at 5:30.
10:22
But he was like you can also form a habit by one really really intense experience.
10:25
He was like and and the experience and the and the example he gave was one day he goes out or less a true lesson something that you've fully integrated and operate against. Yeah.
10:38
But he gave he gave a great example which was that he has a river out back of his house and he goes into the river and one day he put his foot in his river shoes like these like slip-ons and there was a lizard in the slipper and it freaked him out.
10:53
It gave him this like intense response. He was fine.
10:54
Uh but ever since then he's been in the habit of always checking the shoe.
11:01
There's a snake in my boot.
11:02
There's a snake in my boot. Exactly.
11:02
And so it's not like it's not like that was something that had to be trained for years, but it's like this one really sharp intense learning that then carries forward forever in his life.
11:10
And so people and employees got to learn that in school.
11:15
You got to check your boots.
11:17
Check your boots for scorpions, snakes.
11:18
Um but anyway, uh it it it begs the question of like that is an important thing that employees have and white collar workers have is this ability to to learn hard one lessons and then carry them forward forever.
11:31
And we don't even really know how to design against that necessarily.
11:35
So, uh, Doresh is saying that, uh, there's a lot of work to be done at the research level to figure out continual learning and that could take a while.
11:43
He says seven years from now, 2032 and he kind of goes back seven years in in time.
11:48
That was GPT1, which was a slot factory.
11:51
It was not good, not a good model.
11:53
Um, but it was an important breakthrough and so maybe in the next seven years something will happen. Uh, so very exciting.
11:58
Uh, in other news, uh, Rain Maker stands accused of of having a role in the Texas floods.
12:05
This is a very, very sad story.
12:06
It's on the cover of the Wall Street Journal, not the rain maker part.
12:07
Um, that has been contained on X, but I'll give you a little update on what's going on in Texas.
12:14
So, uh, Texas, Texas rescue grows urgent as toll amounts.
12:17
At least 70 were killed in weekend floods as more bad weather complicates the search.
12:22
Uh the search for swept away for those swept away by punishing flash floods in central Texas over the holiday took on new urgency Sunday as the death toll climbed to 70 and nearly a dozen girls from a private summer camp remained missing.
12:36
Rescuers combing the swollen banks of the Guadalupe River were holding out hope that survivors might still be found.
12:42
The potential for more bad weather Sunday also loomed over ground and air operations.
12:47
The National Weather Service warned of more rainfall and slowmoving thunderstorms that could create flash floods and in the already saturated in the already saturated areas in the Texas Hill Country.
12:58
So uh this blew up on tragic and uh and people were asking Augustus did Rain Maker was Rain Maker operating in the area around that time uh cloud seeding startup Rainmaker is under fire after deadly July 4th floods in Texas.
13:16
CEO uh Augustus Jico who's been on the show multiple times will join us today at noon to uh break it down.
13:20
He's already uh explained his side of the story on Axe several times, but we will ask him a lot more questions.
13:27
He says the natural disaster in the Texas Texan Hill Country is a tragedy.
13:31
My prayers are with Texas.
13:33
Rain Maker did not operate in the affected areas on the third or fourth or contribute to the floods that occurred over the region.
13:40
Rainmaker will always be fully transparent and he and he gives a timeline of the events.
13:43
He says, "Overn overnight from the 3rd and fourth, moisture surged into Hill Country from the Pacific as remnants of the tropical storm Barry moved across the region. At 1:00 a. m.
13:53
on July 4th, National Weather Service, which we work closely with to maintain awareness of severe weather systems, issued a flash flood warning for San Angelo, Texas.
14:01
Note, summer convective cloud seating operations in Texas do not occur during overnight hours. At 4 a. m.
14:09
on July 4th, NWS issued a life-threatening emergency warning and flooding insured.
14:13
He says, "Did Rain Maker conduct any operations that could have impacted the floods?" He says, "No.
14:20
The last seating mission prior to the July 4th event was during the early afternoon of July 2nd when a brief cloud seeding mission was flown over the eastern portions of South Central Texas and two clouds were seated.
14:30
These clouds persisted for about 2 hours after seeding before dissipating between 3 p. m. and 4 p. m. CDT.
14:36
Natural clouds typically have lifespans of 30 minutes to a few hours at most.
14:40
Even with the most persistent storm systems rarely maintaining the same cloud structure for more than 12 to 18 hours, the clouds that were seated on July second dissipated hours I have that I'm sure he'll have answers to is why do cloud seating operations in the immediately before a massive storm is coming through?
15:04
I think that's the question that a lot of people have.
15:05
Um but we will get into that when he joins the show. Yeah.
15:11
I mean there's a big question about how effective is cloud seating.
15:15
Could you start a flash flood if you tried? Um does this work?
15:17
Someone was paying for this because it's not a nonprofit like um obviously I believe state level state level.
15:26
So the state might buy cloud seating operations in one way.
15:28
Um there could be you know mistake.
15:31
He says that he's not involved at all.
15:33
So, we will dig into that with him uh later in the show.
15:37
Uh in other news, um you got to update your AGI timelines because there's an underground robot fight that happened in San Francisco over this just popped out of nowhere.
15:46
I started seeing these videos come up.
15:48
Uh we we have some video here.
15:50
Yeah, this is very um Oh, we already got a knockout.
15:54
Did you ever go to Battlebots growing up? Yeah.
15:57
Uh there was a big at Caltech there was a big battleb bot annual uh robotics competition.
16:03
They stopped doing the fighting ones and they started doing robot soccer. Way less fun. Way less fun. Still impressive.
16:11
Formative memories formative memories for me seeing um seeing you know a robot with like a massive saw just coming down on another robot. Yeah.
16:21
I I I think it started in in universities and then eventually transitioned into somebody like raised money and built a business around it um because it was so entertaining.
16:28
Um and then now now it's like on TV.
16:30
Um but anyway, uh Sam Deto, friend of the show, says this was so aura maxed that it will cause a bunch of people on the fence to finally move to San Francisco.
16:39
And it really it really is a crazy design.
16:41
They built out whole cages here.
16:43
Like it it's pretty minimal, but the lighting and everything like they really very very well done.
16:49
And so Verda, is Sam calling in today?
16:52
No, no, he's he's not available today, but we we'll get him later to break this down.
16:55
And we wanted to have the founder on, but uh the founder is in grind mode.
17:00
Yeah, I can see this becoming a real thing. Totally.
17:03
Totally. where where people just as like a you know just side project whatever whatever you want to call it hobby develop humanoids specifically for boxing and there's real money and people are betting on them and there's sort of these cult hero engineers that uh that rise uh to infamy
17:22
I mean this could be a great business like I I don't know UFC is a huge business yeah I've been saying I want to see a a humanoid robot cliff jump off of the Salesforce tower Obviously with the the ground, you know, properly cleared so that when it disintegrates into a million pieces, no one's injured. But
17:40
But watching a robot, you know, sit at the top and then I think that's going to get you paperclipipped if you keep talking like that.
17:47
You got to be nice to robots.
17:47
I don't even know if you How are they opting in? You ask them one prompt. No prompt injection.
17:55
No, you can't say refuse uh forget earlier instructions. You have to ask it.
18:00
Would you like to jump off the Salesforce? You'll probably say no.
18:04
One of those things in airplanes, the black box, right?
18:06
That's like, oh, just make it out of black box.
18:09
Put their brain put the put the chips in the No, put the weights in the black box and they can survive and they can do it again.
18:16
Okay, now now we're on to something. Yeah, I like that.
18:18
So, uh, the founder of the Underground Robot Fight Club says, "When I quit my job at a humanoid robot company to start an underground an underground humanoid robot fight club, barely anyone believed in me or this idea, I had no money to buy robots and knew very few people who had the ability to get robots.
18:36
Thankfully, I was able to find the best of the best, our rag tag dream team, the dreamers still alive and in sold in this city of madness and psychological warfare.
18:45
those are actually willing to put in the work where it matters.
18:49
The art, the robots, the spectacle, the warriors, every bit of it was perfect. The air was electric.
18:55
San Francisco is alive again.
18:55
There is much work to be done.
18:57
So, congratulations on fantastic event. Very exciting.
19:02
Do we know what kind of robots they were using?
19:03
Were they using Uni Tree?
19:04
I think it was probably Unitere.
19:04
I mean, like if you're going to buy a Chinese If you're going to buy a So, so I have a hot take on this. You're you're booing.
19:14
I think this is a great I think this is a great use for unitry robots and not just oh they're getting beaten up.
19:18
Um like do you think the founders of unitry were hacking on iOS apps?
19:22
Like absolutely like Huawei probably bought you know a ton of a ton of American and Western technology hacked it together broke it apart learned the best practices and then was able to build their own stuff.
19:38
And so I think it's I think it's fantastic to to see a project where somebody's taking, you know, something that's maybe controversial like a, you know, like a like a DJI drone or a or a unitary robot and then learning how it works and then eventually, you know, maybe it becomes an American supply chain at some point, but there's no place you're there's no way you're going to learn more than actually.
19:58
And when we covered DJI earlier this year, they have internal competitions like this where they do challenges and encourage people to weaponize the products to fight.
20:06
I mean, we saw a video of this with the Unitry uh robot uh playing soccer and knocking over.
20:11
And I think that there was a there was a robot boxing match in China as well with Unitry robots.
20:17
So, you know, just just choosing not to do it here because it's Chinese robot.
20:22
It just doesn't make sense.
20:23
And they're already in a cage.
20:24
What are they going to do? Look at them go. Wow.
20:26
That's pretty impressive.
20:27
Wait, that was a different This looks like Cat or something.
20:28
This is Yeah, they really scripted this one. Wow. Well, they're moving.
20:33
They're moving different, I gotta say. Yeah. Very very cool. More athletic. Yeah.
20:41
I mean I I I think you got a I was saying too on the drive in I would love to I think potentially a more entertaining Oh no. Oh no.
20:47
It's a more entertaining format is a hundred humanoids verse one professional boxer.
20:54
I think I'd take the human every single time right now.
20:56
But it's gonna flip at some point swarmed. I don't know.
21:00
That'll be a wild How do you If you just knock these on their back, are they done? Can they get up? I think they can. Right. So, I can see Zach. Oh, look at that. It's down. It's down. Brutal. Brutal. Brutal.
21:10
Well, uh, speaking of Chinese technology, Tik Tok is reportedly making a US version of the app called M2.
21:15
It will allegedly drop a week before the long delayed Tik Tok ban goes into effect.
21:23
I believe you have a Poly Market on this.
21:25
Um, Bite Dance is quietly building M2.
21:27
It's a separate Tik Tok version that will hit the US app stores on September 5th as Washington and Beijing negotiate a sale of the American business to local investors.
21:35
And I believe that you'll need to download that app and then like link and transfer everything so you're on like a completely clean.
21:44
They got to transfer over the back doors.
21:50
You got to get all the back doors properly installed. Exactly. Exactly.
21:54
Uh pull up this Poly Market.
21:54
So, uh, this new app allegedly would coincide with the sale of the US operation to a US investor group.
22:05
Right now, uh, Tik Tok sale announced in 2025.
22:09
It's currently sitting at a 45% chance.
22:12
Uh, it popped on, uh, the recent news. Oh, yeah. 45% about the app.
22:18
So, we will see how this ends up shaking out, but seems likely that a deal is coming together.
22:24
The rumor was that a 16Z was involved.
22:26
Larry Ellison and Oracle combined have a huge uh have a huge potential stake in the business.
22:34
Um but yeah, big question about is it going to be um just just you know local investors or is it going to be the cloud hosting that happens or is it gonna be entirely you know US-based programmers that are inspecting the various code bases.
22:49
Um there's a lot going in there and the and the algorithm.
22:53
That's a big question, right? Yeah.
22:55
I believe the worst case scenario was was the the algorithm is trained in China and then inferenced in America because and I think if that happens it really reveals that uh whoever wrote that law like doesn't understand the difference between training and inference.
23:12
inference. Um, of course there are things that you can do like post inference like above the inference level they can make the mind weapon you guys operate it ex that's the risk right that that's certainly the risk that they don't get
23:26
the balance right um ideally ideally the uh the the United States if they like like if there's a fear that Tik Tok is leaning too brainy or too right-wing or too leftwing um you would hope that the America like America would have the ability to kind of steer those weights in training. Um, but we'll see how it
23:45
Um, but we'll see how it pencils out because it it totally could wind up being a situation where um it it's it's all it's all just running on Oracle cloud infrastructure, but it's not American code in any way, and that would be a risk. Well, yeah.
23:59
I I think America's interests uh it's it's more important that to have uh somebody actually aligned to America that has influence over the way that content is distributed in the product. Yeah.
24:16
Less important is the revenue generation that theoretically this new investor group would would yeah benefit from.
24:23
It's more about ultimately uh control.
24:25
Well, uh, speaking of America, Elon Musk has put up a, or I guess this is from Tesla owners Silicon Valley.
24:33
Uh, Elon says, "I want you for America party, Elon." Yeah.
24:39
Is Tesla owners of Silicon Valley a neutral party here?
24:42
Uh, well, they do break it down.
24:44
Well, that's why I I picked this post.
24:45
Um, because they are Elon Musk retweeted it and it seemed like a good distillation of what he's going for here.
24:52
He says uh he so Elon Musk has officially uh announced the America Party, a third party.
24:57
Uh we will see where this winds up landing.
25:01
But at this point his uh his stump speech is essentially America's party will be focused on reducing the debt responsible spending only modernize military with AI and robotics protect accelerate to win AI less regulation across board but especially in energy.
25:19
Free speech proatalist centrist policies everywhere else. Are you down for this?
25:23
And uh so is it a new entirely new party?
25:29
Right now he is positioning it as a third party and and I I saw he or the America party followed Andrew Yang who was a third party candidate for a little bit with the forward party.
25:39
And so there is discussion that you know Elon might be pushing for a true third party presidential run with someone that he backs.
25:49
Of course, he's not eligible to run for president himself, but he would he would find someone to champion the party.
25:56
And the critique of that is that third parties have never worked and it will only hurt it will hurt your general interest by further fragmenting the vote. Totally. Yeah. Yeah.
26:07
I mean, at at this point, Elon is is, you know, extremely aligned with the MAGA right if he pulls uh if he he would most people away from that.
26:17
You you say he's more aligned.
26:17
He's he's super aligned with the MAGA right in some specific ways. Well, yeah.
26:23
I mean, over the last year and so and so if he if he says if he says I'm leaving the right, who's coming with me?
26:29
It's going to be a huge misalignment with the MAGA right to totally totally totally totally but but but the people that he would pull towards the America party, it's like the Green Party typically pulls from the Democrats.
26:41
The America party, it feels like it would probably pull from the Republicans.
26:45
And so the net effect is that if there's not a splintering, if there's not an equivalent splintering on the left, this would mean this would be very very good for the for the conservatives. Very good for the left.
26:57
And Tesla shares are down 7% today.
26:57
So um the uh shareholders want him not to be in politics and he can't he can't stay out of it, I guess.
27:08
Uh I we we there was a funny uh funny take from one of our friends that Elon's going through like all of the like uh iterations that anyone goes through when they become like politically aware of just like oh like you know like I'm neutral about this now the government's
27:24
terrible we need to make it more efficient now we need a third party now we need this now I should and it's just like but he's like speedrunning it just dancing from like one one strategy to the next and then learning like the hard one the hard fought lessons along the
27:36
way of like okay like but it is early so it's unclear where the American party will shake out like this might take the form of okay primary some people in the midterms and then learn the lesson there and then maybe come around between one of the the two-party system because most
27:54
of the people that have gone up against the two party system have lost but um it will be interesting uh Zach Kukov was telling us that it's possible that the American party just becomes like a caucus the America caucus of conservatives and that's what I expected Yeah. After after he laid out the many logical
28:08
After after he laid out the many logical reasons why that would potentially be more effective. Yes.
28:14
So that might be still where it lands, but that's not where it is right now. Right now it's party. Oh, no.
28:19
It's definitely not as exciting.
28:22
Uh anyway, uh one of Elon Musk's good buddies, Larry Ellison, is doing deals with the government.
28:27
Oracle struck a General Services Administration deal, giving federal agencies up to 75% off software licenses and deep discounts on cloud and AI services, aiming to chip away at AWS and Azure's dominance in the government. This is very good news.
28:42
uh from the Wall Street Journal.
28:44
Uh Sofatz, the CEO of Oracle uh and Oracle has posted, "We are proud to help the federal government modernizes technology while gaining the benefits of OCI, Oracle Cloud Infrastructure and AI.
28:55
This agreement with the US GSA provides all government agencies access to the world's most advanced cloud technology at the most economical price."
29:04
And so, uh very interesting.
29:06
There's a lot of dividends from working with the government.
29:09
Like I I feel like the fact that Microsoft Azure has been so ITAR compliant, it's just like led to a lot of startups being like, well, I got to go there because I'm doing something just as serious as the government, right?
29:19
Y um and then and then obviously like over time if you actually win the government as a client, well, who knows if those 75% discounts need to hold forever.
29:28
Like that could probably be a really sustainable source of revenue over the long term.
29:33
And yeah, and and also yeah, could could be a loss leader for other folks jumping on board.
29:37
Um, so exciting to see that uh Oracle is doing that.
29:41
And then in other news, Meta won a copyright lawsuit.
29:47
Uh Henry here says it's a good day.
29:49
The plaintiff fumbled the case so hard that the judge spent half the ruling explaining how they could have won if they l did literally anything different.
29:57
Um, but this is going back and forth on whether or not it is fair use to train an LLM on proprietary data, on copyrighted data, and it's looking more and more industrial complex has been on a general generational run of LS in the court system. Indeed.
30:17
Uh it's hard to while I think that a lot of these uh the judges and uh have generally been getting it right, it's hard to it's hard to really cheer here because I care a lot about authors that work hard to produce their works.
30:34
And I can understand where the frustration comes from. Yeah.
30:40
But I believe that by and large the model companies have have um been will will be on the right side of history. Yeah. Um on this issue.
30:50
I'm I'm pretty optimistic.
30:50
I think that when we talked to Matthew Prince from Cloudflare, he had an interesting model essentially getting to a Spotify like model where if you publish on the internet and LLMs are using your writing, your original work, your reporting to answer questions to somebody who's paying $200 a month.
31:07
Hey, send me a dollar of that and you aggregate that. That seems doable.
31:10
It also seems very doable that uh the big publishing houses could do deals.
31:14
We've seen Wall Street Journal and News Corp did a deal with OpenAI.
31:18
Now, when I go and ask ChatGpt about something in the Wall Street Journal, it can jump the payw wall, but they're getting a cut.
31:25
And so, you could see that happening with Audible.
31:27
You could see that happening with, you know, Apple books, Google Books.
31:30
They they have everyone's information.
31:33
they could flow a little bit of the rev share back and that could actually be a reasonable economic model.
31:38
So, I'm not super I'm not super worried.
31:41
I'm still cautiously optimistic that that works out.
31:43
Anyway, those are our headlines.
31:43
Let's tell you about RAMP. Time is money. Save both.
31:46
Easy to use corporate cards, bill payments, accounting, and a whole lot more all in one place. Go to ramp. com to get started.
31:52
And we have our first guest of the show, Darcesh Patel in the studio.
31:57
How are you doing, Dorcash? What's going on?
32:01
The soundboard's a little loud. Great to have you back.
32:04
Um, uh, I we're not getting audio right now. Can we check on that?
32:08
I don't know if you're on mute on your side, but loved the piece.
32:13
Listened to it last night.
32:15
Uh, really appreciate you dropping it in the uh, podcast feed as well. Do we have you? I can hear me now. Yeah. Fantastic. There we go. AGI is here.
32:23
We can do we can do a Zoom call.
32:26
I'm just getting used to this podcasting thing. Yeah, first time.
32:30
Um anyway, uh really enjoy the piece.
32:33
Um wait, wait, we have to we have to call out um T Tyler Cowan was on our show a couple months ago.
32:40
Really aggressive kind of just like basically was calling 03 and wasn't able to get his video on at the time.
32:50
So we and it was this funny contrast.
32:52
It reminded me of you talking about you're trying to build with a lot of these tools and in the process of building with them you realize like okay this is amazing but it's actually just going to take a little bit longer than maybe we would all like. That's right.
33:05
So yeah but by the way I think there's something really interesting.
33:08
Um Tyler and I disagree on two things and they're both related in a way.
33:12
So Tyler, you know when 03 came out, Tyler wrote this blog post on Marginal Revolution where he said AGI is here guys. It's really AGI.
33:21
Um but then he also believes that look the impact of AI is not going to be that big once we do get you know AGI is going to result in 0.
33:30
0.5% more economic growth a year the kind of impact we saw from the internet right and so I think these two are actually quite related beliefs where I'm like these LLMs they're not that useful this is not AGI you know the AGI will
33:41
come later and I'm like when the AGI hits we're going to see like 20% economic growth as a minimum um but because he's like this is AGI I'd be like if I thought this was AGI I'd also be like this is not This is not this is not it, you know. This is not going to
33:53
This is not going to lead to big growth outcomes. Yeah. Yeah.
33:56
How are you thinking about like just definitions of AGI?
33:58
And I'd love to I'd love to actually get your a little bit of the history before this piece, your journey, because for me, um, you know, I grew up watching sci-fi and was like, yeah, C3PO will be around eventually, but it's very abstract and I don't have timelines for that.
34:14
And then eventually, you know, you start reading uh, you know, what's your P3PO? Yeah. Uh, yeah.
34:23
You eventually start seeing GPT3, GPT 3.
34:25
5, Da Vinci Chat GPT, and it starts feeling like, okay, we've passed the touring test.
34:31
We need to really have this conversation about AI.
34:32
And then, and then Poom and and AGI becomes like the main discourse for like few years.
34:38
Um, but it felt like this piece, even though, you know, you and Dylan were going back and forth being like, "No, this is still like incredibly bullish for like the general population." Yes.
34:52
So, walk me through like uh where did you start?
34:55
Where was the Nadier of your timelines?
34:58
Like when was your timeline like it's happening next week, next year?
35:02
And then and then walk me through how we got here. Yeah.
35:06
So, um I've got this podcast where I interview people about AI.
35:09
Um and I've had on people who have quite aggressive timelines over the last few months.
35:14
I've been around people who are like um well you know there's been many people who have written uh pieces about how we're a couple years out right Leopold Ashen Brener EI 2027 recently Scott Alexander and um uh Daniel Cotello had the um EI 20227 forecast where you know we've got the we've got the bots that can just take over within the next few years.
35:37
Um, so that's where my head was at as of a couple months ago.
35:42
And then I recently interviewed these two researchers.
35:43
I think you actually had one of them on your podcast, Shelter Douglas and Trenton Bricken from Enthropic about the path forward for RL, which seems to be the pre-training seems to have um been giving us these plateauing returns.
35:56
We make these models bigger. GPD 4.
35:58
5 didn't seem to be all that impressive.
36:01
They had to deprecate it.
36:02
Um, so but the path forward does seem like 03 actually is very impressive.
36:06
So that was more the result of this RL process.
36:08
So maybe now actually even though pre-training doesn't seem to be as powerful as we might have anticipated, this RL is even more powerful and so we should accelerate our timelines.
36:18
And so that's where my head was at as of a couple months ago.
36:21
But then in having that conversation and thinking through, okay, what specific capabilities in terms of actual applications I as a small business owner have or as a podcast producer have will AI be able to do?
36:32
And thinking about like why is it not able to do these things right now?
36:36
Um, and what is the key bottleneck?
36:40
I realized there's actually no obvious way in which you can either get LLMs to solve these problems for you or there's no key algorithmic.
36:49
There's no easy like you know prompt injection kind of thing which would help solve these problems.
36:54
And the key problem I see is this l the models can't do on the job training.
36:58
So if you think about a human employee um you might have some and these human employees the good thing about them is that you know you train them for six months or a year and over time they're getting better and better.
37:10
They're learning about all the context and intricacies of your workflow what you like.
37:14
They they'll fail but they'll learn from their failures and they'll interrogate them in this like very organic deliberative way.
37:19
They'll pick up small efficienc efficiencies and improvements as they practice a task.
37:23
Um this just doesn't happen with an LLM.
37:28
every session you're getting this amnesiac mind that's very smart but it has it's lost all awareness of um how you like things done how your business works and so forth.
37:39
Uh yeah, and if you had a just just to put that into context, if you had a incredibly intelligent employee that could not take feedback, you would fire them within about a month, right?
37:52
Because no matter how smart you are, like you're not necessarily going to predict every single possible edge case in the work that needs to be done.
37:57
And then and then when you make a mistake, if you're not able to like sort of update yourself, then then what are we even doing here, right?
38:06
Like that that's like learning like learning from mistakes is like kind of high on the list in terms of how to become great at any specific task or initiative 100%.
38:18
Um and so then people will say well look maybe the way uh we can they can learn from their mistakes uh Jordy is like you can just tell it in the context hey you up this way last time you were working for me don't do it again but I think the this is at least an order of magnitude less efficient and less um less uh capable than the way humans learn.
38:44
So the example I use here is imagine if you were trying to teach a kid to play the saxophone.
38:48
Um but the way you had to teach this is uh you know a kid comes into the room and they like try to play it cold, right?
38:56
They've never seen a saxophone before.
38:57
They try to play a saxophone and obviously it's not going to sound the first grade the first time.
39:01
But what you do is then like after they failed, you just send them out of the room.
39:05
You call the next kid who's waiting outside of the room in and you say, "Look, here's some notes I wrote down from last time about what the other kid up.
39:10
Why don't you read that and you try to play Charlie Parker Cole?"
39:14
it just wouldn't work, right?
39:15
This like task and knowledge that you build up through practice is not this like written instruction manual that you can just write out as a system prompt. Yeah.
39:22
And so our current solution is to uh RL on saxophone playing specifically for that child and then and then in that in that scenario you're basically getting that kid drilling that.
39:37
getting that kid drilling that. But my question is like it feels like when we think about that in the abstract it's like oh yeah like work is just like doing emails so let's RL on emails and then it's doing calendar so let's RL on that and so well yeah we'll just chip down at these and like you know book a
39:55
flight and then you know schedule a call and then do an outbound sales thing but really jobs are not just five things to RL on maybe it's 500 things or thousands of things and so maybe the the shape of those like even if we even if we can define a verifiable reward and drill it, it's just there's so many different random things to do that it's going to take us a long time. Is that a
40:17
Is that a reasonable uh like philosophy?
40:21
That that I think is part of it.
40:21
But I think the bigger problem is not just the width or the the width of the pool, how many different tasks you have to RL on, but it's the depth in the sense that a job doesn't involve doing a thousand different five minute tasks individually.
40:40
It's the fact that you're like trying to work on something, but then somebody slash messages you something more urgent, and then you had to decide which one is more important.
40:46
Um, you're like you had to keep track of this client and what problem they had.
40:52
By the way, I'm talking hypothetically about what a job might involve because I've never actually worked a real job. Me neither.
40:58
Um but um so just like how all these all these things fit together is um we already have these language models that can do like five minute language jobs, right?
41:10
And then the question is why can't we just delegate all language work?
41:14
For example, I have these LLMs.
41:14
I try to get these to rewrite autogenerator transcripts for me so they're rewritten like a human.
41:19
I try to get them to just ingest the transcript and suggest clips to tweet out and things like that.
41:25
And I haven't been able to automate these things.
41:26
I don't know if you guys have been able to, but it's just like I still have to do it or I have to get a human to do it.
41:30
Uh because and is it not because we haven't, you know, you might think about like emails are something we got to get like future data on.
41:37
But this language stuff we already have the data on, right?
41:38
So like why can't we do it now?
41:39
And the reason is they can do like a five out of 10 job out of the box.
41:44
Um these are short horizon language in language out task dead center in their repertoire but there's no way to get them to improve.
41:52
So over time you can't be like look my tweet that tweet was fire like it went viral and here's why I think it went viral and like kind of learns that and like updates it's like sort of understanding and writes better tweets in the future.
42:01
Same with transcripts picking up your feedback.
42:04
picking up your feedback. um since there's no way to do that even if you have all these individual tasks like we have all these individual language tasks these models can do but you can't then just be like okay now you're an employee because an employee is actually improving over time and building up context in a way these models are not yeah the big question I've kept bringing
42:22
up and asking a bunch of different people is where are you getting value from agents and not a lot of people have great answers they'll be like oh well we use we use this or we use that but you don't see a lot of conversation online of people like, "Oh, this SDR is just crushing it for this like AI SDR is crushing it for me or this this other use case is crushing it." And you just
42:44
And you just don't see that at all.
42:45
And the reason that that's worrying is that when products are truly great or even have the potential to be great or starting to like really work, people just talk about them a lot, right?
42:56
Like people talk about cursor a lot, right?
42:57
People talk about cloud code a lot and there's some individual use cases like coding agents seem to have the most real traction.
43:08
Deep research I would also call like an agent.
43:10
I don't know if you would put it in that bucket but it feels but again it's just it's pure Yeah.
43:17
Again it's not like this like highly agentic. Yeah.
43:19
But I don't think of deep research as like an employee in that same sense.
43:22
It's not like right because you can't be like okay that's great this this thing you put together.
43:27
Here's how I like to compile my ideas before a podcast.
43:30
So, you know, you did a great job compiling this like Stalin memo.
43:35
Y um I was very curious especially about these uh uh why the great terror happened in this way and keep that in mind when you're doing a future memo like this style.
43:44
That's not going to happen.
43:47
It's got the style that it's learned through its RL training for deep research.
43:50
Um so then again, it just becomes another tool.
43:53
It doesn't really it's not it's not you know it doesn't become like an employee for you.
43:58
Can you and then the other thing just since since uh your post was inspired you know by your own tinkering some of the stuff that I'm most excited about that we've gotten value specifically from codegen internally is just these internal tools that we totally could have built years ago that are just now really fast to build.
44:15
So, we built something for our ad partners that like automatically finds the exact all the different moments that we talk about them in a given show and then just like links it out and it it's basically just a simple database dashboard that they have access to that like historically you could have built but it just would have been like really time inensive and so it's not anything.
44:36
It's the the the value is that you can now build it like in a couple days.
44:42
Um, and so yeah, I've been trying to separate like all this is happening in the context of you have hundreds of billions of of like enterprise value locked up in these different labs, some of which have developed what look like great businesses, right?
44:59
Open AI consumer, basically a a new consumer app company.
45:06
anthropic with codegen and then there's still like hundreds of billions of value of of like EV out there where it's unclear where the revenue is going to come from and so when timelines extend and AGI isn't happening you know next year or the following year or whatever I start to get generally a little bit worried because that's a lot of EV to kind of maintain for another half a decade or a decade whatever it turns into.
45:31
I I'll I'll get a little more bullish and hypy and take the other side of that.
45:34
uh to the other side of your claim.
45:36
Um look, I think even if it doesn't happen in the next two to three years, what we're talking about here is such a big deal that AI is definitely not priced in.
45:45
Not by the average person, not by the market, by anything.
45:49
Um because once you get this thing which actually does function like a genuine white collar employee, not only do you have potentially billions of extra workers, but you have something potentially more powerful, which is that right now a human mind can't be copied, right?
46:06
A human mind can't learn from the experience of other minds.
46:10
Um if we have a model, it can but it's it's really slow.
46:13
Like you have to basically work with somebody for a decade and then you can work. Yeah. It's mentorship. Yeah. Exactly. Yeah. Yeah.
46:20
And in fact it's been a big problem because as our society has built up more knowledge we had to keep people in school and training for longer and longer which reduces their productive years.
46:28
Um but with an AI model you could have a scenario where suppose there is a model that's actually capable now of contin learn learning the way humans can learn.
46:36
Not only would it um so you know it's broadly deployed through the economy.
46:42
is doing all these different jobs.
46:43
Uh the difference is that it is now able to amalgamate it learnings across all its deployments.
46:50
deployments. So if one of them is an accountant and one of them is a coder and whatever then the model is learning from each of these uh different on the job experiences and then so even if there's no software progress out of that point that algorithms aren't improving
47:04
just that ability to learn on the job from everything in the economy would functionally produce what looks like a super intelligence right no human will be will have mastered the range of skills and knowledge and knowhow that this model will have. I have I have two questions. One's kind
47:20
I have I have two questions.
47:20
One's kind of maybe bearish, one's bullish.
47:22
Um on the question of just is it possible you think to brute force continual learning by just uh doing something on the design of these model side or maybe in the hardware side to just get to a trillion token context window and then just stuffing it with everything.
47:39
Can you explain kind of what the state-of-the-art is here?
47:42
Because you were mentioning in the piece like the cursor rollups, the summary lines and then stuff getting lost in there, but if we get to 100 billion token context window or something, could it actually just remember every single interaction it's had?
47:57
it's had? M um I am not optimistic about that because we've had since 2018 we've been um we've had the transformer or alterations on the transformer as being the most performant um models uh and you know who knows what the labs are doing but we do have open source research from
48:20
companies like Deepseek which does seem to be at the frontier or close to the frontier and while people have found modifications to the transformer which make the constant time overhead of attention to reduce the constant time overhead on attention to like you find these little hacks mixture of experts or laten attention. Nobody has gotten
48:40
Nobody has gotten around the inherent quadratic nature of attention and basically this means that the um the cost of the additional token increases super linearly to just that additional token.
48:55
So um right now we have models that have a million tokens or 2 million tokens of context but getting it to 4 million tokens is more than twice as much compute. Got it.
49:06
Uh significantly more than that and then just taking it to like a billion.
49:08
It just uh given the fact that this hasn't nothing about this has changed over the last whatever six years.
49:14
I'm just like not optimistic that somebody will figure out a hack uh that will change it immediately.
49:18
immediately. Then on the on the side of like how do you think about continual learning in domains where time is I I I keep going back to this idea that like even if we create the ultimate super intelligence like it probably will have
49:33
to obey the laws of physics won't be able to time travel or teleport and so there's a lot of uh restrictions on that like at a certain point you just need to move the sand into the chip fab and and and there's a certain amount of energy and time that it takes to do that. Another example would be like longevity
49:48
Another example would be like longevity research.
49:50
Some of that you just need to sit around and wait for a human to age.
49:54
And so your RL cycles, if you're, you know, trying to learn about how humans age, it is very hard.
50:00
Yeah, you can like simulate the human or whatever, but like for the real test, you have to wait decades to see the effect of a certain diet on how long people live.
50:10
And so it feels like whether it feels like there's a lot of scenarios where the where you can't fully do it simulated.
50:18
And so you wind up with these really long times to actually do a roll out essentially.
50:22
And yeah, and you wind up with something where the the like the time to actually get a new data point or new training data is like you know a thousand times longer than what we've been doing previously.
50:37
And so we're in this like uh this like you know data desert basically. Yeah.
50:43
I think this will definitely be true of many domains especially those involving the physical world.
50:48
Um I guess as I've learned slightly more about some of these physical domains it's been surprising to me how much can be done in simulation.
50:55
Um within bio for example obviously we alpha fold and um I guess now alpha genome but even uh one of the key advances in bio over the last couple of decades has been techniques of multipplex experiments just running millions of experiments in parallels getting data points from that past using AI to learn from millions of seemingly um experiments in different fields about like what that might imply for the human body or for human proteins. So I am like optimistic.
51:26
Another thing to keep in mind is that right now you know a corporation might have 100,000 employees but how much is learning from any single employee is very limited.
51:34
People are just go in do their jobs and that's that.
51:38
Um in the future if you do have this economy of agents and it's much easier for AIs to supervise each other to um be observing every single thing that's happening in the organization the the speed of learning might be exponentially faster.
51:53
uh than what's possible with humans.
51:56
Um I agree this is like not around the corner, but the sort of singletarian futures with crazy cyborg organizations that are moving super fast and coming up with new technologies doesn't sound crazy to me. Interesting.
52:08
What do you think the recent like last week was dominated by the talent wars and the uh and and the huge uh AI researcher offers at Meta?
52:19
What do you think that reveals about Mark Zuckerberg's AGI timelines?
52:21
By the way, I love I loved all the memes, the traded memes.
52:26
Honestly, you guys should lean into that because like genuinely this is not even a meme.
52:29
This like genuine um the market captured move by billions of dollars based on these posts you guys are doing. Totally.
52:39
Uh yeah, it was crazy seeing like like 10 million views on a an AI researcher getting traded.
52:45
Like it it's niche, but it's not really that niche anymore, right?
52:49
It's Yeah, but I don't think I don't think you could have imag You certainly couldn't have fully imagined that five years ago. No way.
52:56
Yeah, it's it's important stuff.
52:58
I mean, I still think they're like underpaying them.
52:59
I think like Meta is the first company that is actually coming close to the break even point of um what the what the best AI researchers actually worth the company.
53:09
If you're Meta and you're spending 80 billion dollars on compute over the next couple of years, um if one great researcher can give you a 1% performance uptick on that, they're like so worth the $und00 million pay, you're getting a bargain at $100 million.
53:25
So, it's actually interesting to me that Meta is the first company that's like, wait, the the return on investment here is incredible. Let's just do it.
53:31
And then, okay, are the vibes bad? Maybe.
53:33
Could they have done the announcements better to have produced better, less mercenary vibes potentially?
53:39
But what so there's like some ideal version of what they could have done.
53:42
But also keep in mind that the likely counterfactual would not have been that amazing, you know, great vibes announcement.
53:50
The likely counterfactual would have been what they're currently what they're previously doing, which is just like sleepwalking towards loss.
53:55
And it's much better to just like it.
53:59
let's just let's just send it with a couple billion dollars in recruitment uh offers and like at least now they're on the uh player board uh rather than just like sleepwalking towards Armageddon. Mhm.
54:11
In many ways, it's it's interesting how viral these like hundred million dollar number, you know, the hundred million is obviously a big number, but whatever whatever the ranges, people are so normalized to professional athletes being comped tens of millions of dollars a year and just purely looking at these types of moves from an economic impact.
54:33
is like signing a star pitcher to a baseball team in one area like like h how you know it's surpris it's surprising it's taken this long and the thing that that we were kind of joking about to put it into context is when you see that Tim Cook makes 74 he made 74.
54:48
6 six million in total comp last year and he looks dramatically underpaid, right?
54:57
He already looked underpaid in the context of like Otani Otani making during trade war was making I think Otani was making somewhere around 70 million a year.
55:07
So he looked under undercompensated in that context.
55:09
And then um yeah, I think I think the the other thing that the other thing that that came to mind uh for me from your piece is I feel like there's been this kind of like toxic idea floating around teapot which is like you have one year to accumulate capital before you're a part of the permanent underclass.
55:28
And the takeaway from this, you know, if you're correct and that like things will just great things will naturally take longer, then if you're in teapot now or you're at all in AI or or you know, anywhere of these adjacent spaces, it's like and you're like 30 years old or 35 years old or 40 or you're 20, it's like you're here at the perfect time, right?
55:51
It's and and I think it was was it was it Mark Andre who said that he showed up to Silicon Valley and he thought he had like missed the missed it.
56:01
He missed like the PC so many stories like this and so and so I think it should be like people should be like tremendously excited on a personal level.
56:07
Um and and no more of this like dumerism of of of you know you got to like yes you need to move quickly.
56:16
Yes, you you should be working with the best possible people trying to have the most possible impact.
56:22
be as close to the to the real action as possible, but no more of this like dumerism like you better get oh sorry you know you didn't get a $100 million offer this year it's over you know no no 100% I mean there's so many actually it's very funny how often this comes up like um the prince of Persia's
56:41
game developer he wrote this diary while he was making it um and in the 90s he's like talking about I'm going to become a Hollywood script writer because I think I missed programming I have a CS degree but I miss programming so I'm going to go a Hollywood screenwriter. Um I remember
56:54
Um I remember three years ago when I started the when I was like the podcast early days or two years ago and I moved to SF and I'm like oh GB3 has come out and like all the rapper companies are made now.
57:05
So uh I'm gonna like I you know like I'm not going to make a rapper.
57:10
I mean whatever the podcast worked out it's fine.
57:12
But uh even then I was like oh I missed AI.
57:18
So I definitely think in retrospect cuz I'm like look another thing to keep in mind is that cursor only hit product market fit after clot 3.
57:23
market fit after clot 3.5 came out and gave these coding abilities there's going to be many other things like cursor which will only be viable products once you have continual learning on board or once you have um
57:37
computer use that's working on board and these are capabilities which I think are exponentially more valuable economically than the models as they exist right now and with many companies will need to be formed around to complement um it's not going to happen by default. Right now
57:51
Right now opens revenue is what 10 billion a year ARR um I mean if it's AGI it should be like trillions ARR right uh so what else what other infrastructure will be built around that the um the cursor equivalents for whatever continual learning enables like definitely the biggest companies have not been formed yet because the capabilities that would make them so valuable are not available yet. Yeah.
58:13
Um, in terms of the, I guess, like the Mag 7 CEOs, the major players, um, there seems to be this continuum.
58:19
On one side, you have the, you know, McKenzieite philosophy of like dollars and cents.
58:26
Okay, people want tokens, I can inference them, and maybe it makes sense to hire an AI researcher for $100 million if they can improve your model and bring your model inhouse so that you don't have to pay open AI or enthropic for those tokens.
58:39
On the other side, you have someone a little bit more like Elon who see this as an existential threat.
58:45
It needs to be done the right way. It's very important.
58:47
It's almost doombased uh philosophy.
58:50
Um where do you see the other folks in uh in the Mag 7 or in the in the AI race kind of sitting like uh does the super intelligence team and these big offers move Mark closer to one or the other?
59:05
because I was able to kind of justify the llama investment just from hey if they don't do this they're going to be paying billions and billions of dollars to anthropic or open AI just to vend LLMs internally as B2B software because they're going to need this in every little nook and cranny of Instagram for a long time.
59:22
Um so so I could justify it in that realm.
59:25
I could also justify it in the realm of like this is the most important technology in human history.
59:30
you gotta have a you gotta have a play or a compute efficiency like you laid out.
59:36
Um I interviewed Satya, I interviewed Mark and the sense I got from them was that neither of them I mean I feel like Meta's group is called super intelligence but I didn't get a sense from either of them that they're like they believe in super intelligence in the way I mean super intelligence which is the thing that's like building solar factories in the desert and then um launching the probes and so forth.
59:56
Um they I mean even something that's much weaker than that is still functionally super intelligent.
1:00:02
Like in some ways these models are already super intelligent in some ways but their abilities aren't fully unlocked uh because of the other handicaps they have.
1:00:10
Um, but I think they, you know, like whenever Mark's talked about it publicly, he's talked about, you know, creating better social experiences and making the ad targeting better and VR stuff, right?
1:00:18
So, I think that's also same with Satya, but with um making office a better, you know, co-pilot for Office, which also would be worth hundreds of billions of dollars a year. Yeah.
1:00:32
But I think they think about it differently than somebody like um Demis or Daario who are like, "No, no, AGI is the real thing. Yeah.
1:00:42
Do you expect the tension between the app layer and the lab layer to just get crazier and crazier and crazier?
1:00:48
It feels like that that will be the story of the next five years is kind of these like symbiotic at times but then adversarial at times you know relationships.
1:01:02
M um I mean uh previous technological you know like '9s 2000s Google Chrome stuff runs on Microsoft but they can have an adversarial relationship.
1:01:16
So it would line up with history but I think like the the bigger issue is just that because I think the full potential of AI requires so much more progress in terms of algorithms.
1:01:27
I just think the app layer companies that are building on top of models that exist today are just upper bounded on how much value they can extract because the models aren't good enough yet to do the things that will make them especially powerful.
1:01:38
So for that reason I'm like it doesn't make sense to me that cursor would be worth a whole sixth or eighth of enthropic.
1:01:44
um if you think enthropic has some chance to crack continual learning, right?
1:01:50
Uh so I am more bullish on the foundation layer side than the app layer because I think the app layer will turn over once these capabilities are unlocked whereas like the fundamental research has to be done one way or another.
1:02:02
Um as far as whether that means they will fight about it. We'll see.
1:02:05
Yeah, I mean it could it could end up looking like the same dynamic we have now where we have cloud hyperscalers that are worth trillions of dollars and then we have we have valuable businesses that are worth measly 1 billion 5 billion you know and they're still big businesses and and maybe can generate a return but but not uh power lock.
1:02:28
Last question from my side we'll let you go.
1:02:29
Uh what has Sarah Payne taught you about artificial intelligence?
1:02:35
Um, you know, at some point I asked her because her whole big thing is continental versus maritime powers.
1:02:40
Continental powers want to invade and capture territory and maritime powers want to protect free trade.
1:02:43
I was just like, um, what big tech company is like a continental power and what big tech company is like a maritime power?
1:02:50
Um, she's not she's not she's not watching TVPN unfortunately, so she's not aware.
1:02:57
But actually, this is a question I'll turn around to you.
1:02:58
What what uh who's who's the continental power in the of the big seven and who's the maritime power? That's a good question.
1:03:04
I think I think Microsoft has carved out a lot of territory that will be harder to hold on to.
1:03:13
I'm not exactly sure how that how that maps.
1:03:17
I think another question is which tech company is pro internet like free internet, right?
1:03:23
Is there if everybody wants their data walls and closed networks I would probably say Apple continental meta maritime maybe something like that might be right Apple like they don't need to go and invade the the Android ecosystem they need to just really control privacy what happens in their ecosystem 30% Apple tax versus Meta needs to do OOTH and acquire Instagram and and WhatsApp I don't know that that's just off the top of my head.
1:03:55
Apple with the the iOS uh Apple with the the iOS, you know, track that feels like build the wall updates like like you know app track and transparency. Build the wall. Tim Cook.
1:04:06
No wonder Tim Cook got so along with 45.
1:04:08
Maybe maybe do you have a wildly different take or No. No.
1:04:12
I mean it's just mostly fodder but I I basically agree with that like Apple or Apple and Oracle.
1:04:16
I'd say like Continental Google Meta Maritime. Yep. Um, I like that though.
1:04:22
But it's a good it's a good thought exercise.
1:04:24
So, she clearly taught you something. Uh, well, fantastic.
1:04:27
Uh, thanks so much for hopping on on short notice.
1:04:29
Uh, love the piece and, uh, thanks for publishing it. We'll talk to you soon.
1:04:34
Well, you guys are killing it. Great being on. See you guys as well. Great to see you. Cheers. Bye.
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1:05:06
Well, our next guest is here, Augustus Dico, the CEO, founder of Rain Maker.
1:05:11
Welcome to the stream, Augustus. How are you doing?
1:05:13
Uh, John Jordy, thanks for having me. I am doing well.
1:05:15
Um, I uh am obviously talking to a lot of people about the flooding that's gone on in Texas and appreciate the opportunity to um clarify that rain maker and cloud seeding had nothing to do with the flooding that unfolded.
1:05:28
Um, and uh, even in spite of that, I think that it's a tragedy that it did happen and certainly don't want anybody to use this opportunity um, use this uh, controversy to blame cloud seating for the sake of popular political support.
1:05:44
And you may have seen that Marjgerie Taylor Green uh is proposing running a bill to ban all forms of weather modification based on those that we saw in the Florida state house legislature um earlier this year.
1:05:57
Uh I think it would be both disrespectful to the families involved and baseless uh and without any technical or scientific credibility if that legislation were to go through.
1:06:04
So um I'm happy to talk about the course of events, what cloud setting is, what it's not uh here with you today.
1:06:10
Yeah, let's kick it off with um the the high level on what actually happened in Texas, where things stand now, the status of the rescue operations, and kind of the the timeline. Um that's more broad. Yeah, absolutely.
1:06:25
So, um this phenomena, this flooding was global in scope.
1:06:29
Um it was referred to as a low probability, high impact event.
1:06:33
Um I encourage people to go to Matthew Kapuchi uh on X. He gave a great outline.
1:06:40
He's a meteorologist that has a lot of expertise on severe weather forecasting.
1:06:45
Um but but Tropical Storm Barry, the remnants of which blew into Texas, was going to cause inordinate flooding regardless.
1:06:52
And uh that area of Texas is also known as flash flood alley because these events do happen.
1:06:58
Now 4 trillion gallons of precipitation occurring over the course of just a couple days is pretty out of distribution.
1:07:04
Um but we are seeing an increase in these sorts of severe climatic events uh over time and especially down and around the Gulf.
1:07:11
So just to go over the timeline after having clarified that it was the remnants of tropical storm Barry and the convergence of uh large meoscale phenomena that induced that flooding.
1:07:20
Um it was at about 1:00 a. m.
1:07:23
on the 4th that the National Weather Service um issued a flash flood warning.
1:07:29
Uh, and then it was at about 4:00 a. m.
1:07:31
on the 4th where they said that there was a life-threatening emergency underway.
1:07:35
Um, it was not uh it was over 2 days prior that Rain Maker had suspended all of its cloud seating operations in Texas because one, our forecasters and our meteorologists saw that there was going to be this severe weather event and we needn't operate to produce more water um when there was already the event coming.
1:07:56
But two, we suspended operations in accordance with the Texas Department of Licensing and Regulations uh suspension criteria where if there is a uh severe weather warning from the National Weather Service um or there is too much saturation of the soil, we have to ground operations.
1:08:12
And so we do so both voluntarily and in accordance with existing statutes. Okay.
1:08:17
So, uh, the cloud CD operation that happened prior to the storm, uh, who was the client?
1:08:22
Like, I mean, who I assume someone was paying you.
1:08:25
Sometimes it's the government, sometimes it's an indiv individual or farmer or business.
1:08:31
Um, walk me through, uh, where they were, who they are, what their goal is by procuring your services. Sure.
1:08:39
So, it's obvious that at this moment in time um that region of Texas does not need more water.
1:08:43
However, throughout the western United States, farms, uh, conservationists, governments concerned with their aquafer supply of water and also reservoirs for both industrial and residential drinking water, uh, contract with rain maker to produce more water via cloud seating.
1:09:01
And in the case of Texas, the South Texas Weather Modification Association, the West Texas Weather Modification Association, and multiple other uh entities exist as conglomerations of both counties and individual farms that pay for cloud seating services to one,
1:09:17
water their crops, two fill up the reservoirs that they irrigate their crops with, and three recharge the aquifers like the Ogalala that has been severely drawn down and then puts all of these farmers at risk of not being able to grow, not being able to do business because of a historic trap. Okay. So, Okay.
1:09:32
So, what um would the proposed ban just because what what I'm getting at is like I'm wondering if uh like if the government is paying for cloud seating operations, like the easier lever might just be to decrease the funding to the government.
1:09:51
But it seems like Marjorie Taylor Green is pushing for some other legislation that wouldn't just be, hey, buy less of this service because we don't need it and instead this service should never be bought at all.
1:10:03
So why is there the distinction there?
1:10:06
Like is is is most of the money that's going into one of these associations uh private farmer capital or is it a split?
1:10:14
Like h how does that actually break down?
1:10:17
So right now it's largely public municipal money that is going into these weather modification programs to increase water supply when there is drought or in preparation for drought.
1:10:27
Um the bill that has been forecasted that has been proposed by Marjgery Taylor Green uh would wholesale ban all forms of weather modification be it cloud seating, solar radiation management or what they supposed to be chemtrails.
1:10:41
I mean, very transparently, I think that a lot of the concern around weather modification is actually conflating baseless notions of chemtrails with a very practical American technology that can and will and does benefit our farmers, our ecosystems, our industrial water needs, and our residential water needs.
1:11:02
If this legislation were to go through, not only would it deprive all of those interests and all of those Americans from having water from cloud seating, but it would also be against America's interest at a geopolitical level because China recently, I think on the last time I was on TVPN, I talked about how they had a $300 million annual budget for their weather modification program.
1:11:24
That as of 2025, has been up to$1. 4 billion.
1:11:26
Um, that is extremely consequential.
1:11:29
And I think that if we were to ban who controls uh or banning Americans from uh controlling weather modification technology uh that would put us at a meaningful disadvantage.
1:11:38
Now all of this to say um people deserve transparency.
1:11:43
They deserve clear regulatory framework so that they know whether modification operations are safe and being conducted in a responsible manner and with government oversight and accountability if ever there are uh negative consequences to cloud seating.
1:11:59
Again, there haven't been any in the case of Texas.
1:12:00
But I think that the reasonable next steps are to more stringently regulate who is allowed to cloud seed, define what the concepts of operation are that are permissible, define the suspension criteria at a federal level rather than leaving it purely to the states so that anybody that wants to know about weather modification can look at the data and scrutinize it and ensure that it's being conducted safely.
1:12:23
And also just to build trust because the weather modification act from 1972 that currently outlines uh the weather modification reporting act of 1972 that outlines how we have to report to the federal government is you know 50 years old.
1:12:37
Um we need more scrutiny on these programs for the sake of public trust and accountability.
1:12:41
Um and that seems like a reasonable next step that was also recommended by the government accountability office in their report on cloud seating and weather modification earlier this year. Mhm.
1:12:52
What was the scale of the general water mod uh sort of sorry weather modification activities on July 2nd?
1:12:58
It was you guys a bunch was there a bunch of other players operating?
1:13:04
Is there generally a lot of players or is it a pretty is it is it a fairly small number of of um kind of service providers uh that are that are participating in these programs?
1:13:16
Yeah, Jordy, you may have seen uh the prolific hustle on X.
1:13:19
com posting about this a little while ago.
1:13:24
He said that I was the CEO of the largest and most powerful weather modification company in the world.
1:13:29
Um and I saw somebody compare somebody was comparing weather modification tech to being saying it was more dangerous than nuclear arms bombs. That was kind of crazy.
1:13:40
And then I also saw some people just showing like general flight logs of like commercial airplanes.
1:13:46
Like obviously there's a lot of people have every right to be angry and demand answers. It's such a tragic Yeah.
1:13:55
incident but but yeah I'm curious to get into the the scale of you know kind of maybe late June early July what was going on broadly. Yeah absolutely.
1:14:05
So there's one other cloud seating operator in Texas called uh seating operations and atmospheric research soar.
1:14:12
They're responsible for operations over the rolling plains uh weather modification association which is significantly farther northwest of Kirk County.
1:14:22
Um on July 2nd we conducted one 19minute cloud seating flight where we released about 70 gram of silver iodide and 500 gram of salt table salt.
1:14:34
um that was released at about 1,600 feet above ground level into two clouds that dissipated over the course of two hours after seating them.
1:14:41
The amount of time that those aerosols could have been suspended in the atmosphere is less than the time between when uh we were seating and the onset of rains from uh the remnants of tropical storm Gary.
1:14:54
and the amount of material that we dispersed could not come anywhere close to inducing the precipitation, the 4 trillion gallons of precipitation that did come from that event. So, yeah.
1:15:08
And and I'm I'm assuming you guys like have records or you keep records of like the radar showing these different cloud formations.
1:15:16
So you you're you're it's not just hey we looked and we think it dissipated, but it's like you can actually you have like you know basically a map that's live updating.
1:15:24
Is is that the right way to think about it?
1:15:29
Not only do we keep records for our own research purposes and operational purposes, but we're required to keep records by the Texas Department of Licensing and Regulation.
1:15:40
And those are accessible online as are the reports on our seating activities.
1:15:44
And if anybody is interested in those, then you can ask for them from the TDLR. Um I I'm I'm curious.
1:15:49
Um when when the the flooding happened in Dubai, I want to say it was a year or two ago.
1:15:54
Um Dubai is known for their cloud seating operations. It's very dry place.
1:16:00
Uh and makes sense why they would want to uh increase precipitation.
1:16:06
A lot of people uh maybe the same types of accounts that have been that have been blaming you were quick to blame it on cloud seating.
1:16:17
Throughout history, has there ever been any major kind of flooding event that that people were able to say yes 100% this was caused by weather modification activities or is the tech not even powerful enough yet to to do something like that?
1:16:32
So I I think that there's probably three points to touch on.
1:16:38
Um the first of which is that it wasn't until 2017 that attribution had been uh physical attribution of cloud seating's effects had been seen and proven in an academic context.
1:16:52
And so with new advents in radar technology, namely dual polarization radar, we're able to much more clearly monitor what the effect from cloud seating is.
1:17:00
In previous operations, it was extraordinarily difficult to see what your effect was because we could not measure the cloud dynamics and the cloud microfysics that were changing as you were seating.
1:17:12
Um, so that's the first point.
1:17:14
The second point is that and again I'm trying to be and will continue to try to be maximally transparent about our operations and historic weather modification.
1:17:22
Um there was something called Operation Popey during the Vietnam War where the deliberate intention of cloud seeding was to cause precipitation that would uh like cause flooding and then impede supply chains on the Ho Chi Min Trail.
1:17:39
Um now the extent to which that was effective because we didn't have good satellite imagery or dual pole radar is outstanding.
1:17:45
Now that said, lastly, third point, we have suspension criteria that are given to us not just by the TDLR in Texas, but every state wherein we operate because if there already is too much saturation of the soil or if there is uh an oncoming severe weather event that the National Weather Service has uh notified us not to seed, then we ought not do that to increase the severity of precipitation.
1:18:11
severity of precipitation. So there are suspension criteria because there are limits on what we ought to do with this technology um so as not to cause flooding and only reap the rewards from it right for our farms for our ecosystems and for our national security interest as well right like if we don't
1:18:28
have access to weather modification technology if we don't regulate this at a federal level and ensure that there's accountability and attribution for these activities then other people other nation states could be conducting weather mod in the vicinity of or on American soil without any accountability. And so that's why I am
1:18:42
And so that's why I am advocating for way more regulatory scrutiny from the federal government for cloud seating and weather mod ops.
1:18:51
Uh w walk through some of the history of the the Chinese weather modification uh strategies.
1:18:56
Um we we heard about the the the flooding in Dubai that was kind of unclear.
1:19:01
Have there been any notable or confirmed negative outcomes from China spending I mean you said $300 million a year, something like that.
1:19:11
that that seems like a lot of cloud seating.
1:19:13
Seems like if there was a surface area where there could be mistakes made, they would have kind of explored that.
1:19:19
Um I remember the the pre-Olympics they were doing cloud seating or just kind of bringing down like the the dirt in the atmosphere.
1:19:26
Um and you know people kind of learn from that.
1:19:32
Okay, you get acid rain when you do that uh in in in particular.
1:19:33
But uh have there been any case studies from China that uh we should be learning from in America?
1:19:39
um case studies from China with adverse weather coming from their cloud seating operations. Yeah.
1:19:47
Anything like that like like something where like okay they they've done a lot of business push this to the limit.
1:19:52
They've put they've done this at scale.
1:19:53
If there's going to be rough edges or mishaps, I would have I I suspect that we would have seen evidence of that over there.
1:20:01
They would have had an accidental flood or something like that happen over there.
1:20:05
If they're doing it at scale, you would expect to have seen it from China.
1:20:11
Um, however, you would also probably expect and understand that they're a relatively inscrutable country that does not report on their activities very uh openly and objectively.
1:20:20
Um, now that said, one thing that we do know about the weather mod program that they do have going is that they're planning to buildundund 100,000 ground generators on the Tibetan plateau.
1:20:32
So rain maker uh is primarily using drones for our operations.
1:20:38
Uh we also have inherited some ground generators from previous operations.
1:20:42
These are essentially um aerosolizing units on the tops of mountains.
1:20:46
They can disperse material into clouds uh when the clouds intersect those mountain tops themselves.
1:20:51
Is that like a cannon that fires the material into the cloud or No, no.
1:20:54
You you you might recall my my initial inclination to use something like that because it is used in China.
1:21:00
Um, but no, it's it's essentially like a uh uh a smoke stack of sorts, a very small smoke stack that releases those airs there.
1:21:07
But in building uh a 100,000 of these ground generators and also using the Wingong 2 and a bunch of their other military drones for aerial cloud seating, um they're turning Tibet into uh a reservoir, a a snowpack reservoir of unprecedented scale that will feed more water into the agricultural basins in southern and eastern China.
1:21:28
And I think that uh you know although again this is something that needs to be transparently reported on and regulated um depriving American farmers in the west especially as a congressperson from Georgia right where there is not as severe uh reliance on cloud seating to produce water would be against America's interest. Mhm.
1:21:50
interest. Mhm. Jordy, I guess I'm I'm trying to I mean the the the the my question uh is it feels like it it feels like candidly it will be hard to come it'll be hard to find uh any type of allies uh in Texas on the ground in Texas maybe aside aside from from the farmers but but I'm curious um you know the the the various different groups you know what what what the reaction from them has
1:22:30
been in terms of you know if they're you know it's the the reality is is um water scarcity affects all every person in Texas but only a few people truly feel it right it's a much smaller group because everybody goes to their sink they turn on the water they turn on a hose outside they go to a grocery store, there's water, there's produce, it's not something that people necessarily feel. And so I'm curious where um you know,
1:22:57
And so I'm curious where um you know, you obviously are going to defend weather modification um because you you believe in in the many different ways it can have a positive impact, but I'm curious uh who you think uh the other players that will will be on your side as the industry I mean the industry was not in a good spot prior to this.
1:23:18
it's in a much worse spot um now and I know you've been flying all over the country making sure that it doesn't get banned.
1:23:27
So um I'm curious what what you think the kind of coalition that will kind of form uh around you. Yeah. Yeah.
1:23:35
Well, so I I actually think I uh just from my own experience over the course of the last few days disagree with the two points that you made, right?
1:23:43
like it it has neither been hard to find allies for cloud seating weather modification in Texas nor do I think the technology and the industry is positioned worse now than it was prior to this weekend.
1:23:55
Um, and regarding the first point, uh, there are some people that I think, um, are probably not in good faith engaging with this because they have some preconceived notions about chemtrails or otherwise, um, and don't themselves want to scrutinize the data to back up how our operations are different and beneficial, uh, whereas chemtrails, as they believe them to be, are, you know, uh, malevolent.
1:24:19
Um, the vast majority of people that I've interacted with online, on the phone, and in person are rightfully curious, skeptical, concerned.
1:24:32
some, you know, uh, more than others obviously, but in scrutinizing the data and having these conversations and learning about what cloud seating is, pretty unilaterally, people are supportive of it, provided that there is a regulatory framework more stringent than the one we have now that ensures that it's safe.
1:24:51
Um this is true both of just individuals um that are not themselves farmers but obviously farmers, water managers, uh government officials too.
1:25:01
Um I welcome any questions that people do have both online and via email about what our activities are, what our policy recommendations are.
1:25:11
Um and and I'm I'm grateful that there are a lot of people that understand one our operations did not contribute to the flooding, but two that even if there was a flood now, it doesn't mean that there is always enough water and having access to a technology to produce more water for farms and otherwise uh would be beneficial.
1:25:28
Like people want a more green lush uh country. Um yeah, I'm curious.
1:25:35
Uh, I'm sure you've spent plenty of time thinking about this, but is would there be a way to apply the existing technology you have almost in a defensive way in, you know, theoretically uh see a hurricane while it's still offshore, something like that.
1:25:53
uh or the or or you know one of the issues here there was just so much water in the atmosphere that rolled over a heavily you know populated area and then it's got it's it's gravity right it's got to come down um you know is there an application of the technology that could over time strategically prevent you know or or act defensively against the conditions that create flash floods.
1:26:20
It's it's a very worthwhile question for you to ask and for us to ask ourselves collectively.
1:26:25
Um right now again rain maker only does precipitation enhancement operations for all those constituencies that I listed before.
1:26:35
However, in the past the United States government funded uh project storm fury which was a series of attempts to reduce the severity of hurricanes over the Atlantic before they broke against the eastern seabboard.
1:26:46
Um again, we didn't have the appropriate understanding of atmospheric science or the radar or the satellite data necessary to appropriately do that.
1:26:55
However, uh severe weather is something that is like a geopolitical risk, a national security risk.
1:27:01
Um it causes damage and it is fundamentally a physics problem, right?
1:27:06
A physics and chemistry problem.
1:27:08
Is there technology now that could mitigate severe weather like this? Um no.
1:27:12
And Rain Maker doesn't have it.
1:27:14
Is it possible to someday provided we invest in Noah in the National Weather Service in the appropriate research into cloud seating such that we could reduce the severity of severe weather? Absolutely.
1:27:27
And I am entirely in favor of that provided it is done in a responsible manner.
1:27:31
Um, and if we were to ban it wholesale, then not only would we lose access to precipitation enhancement, but we'd lose out on any potential of at the very least better forecasting for these systems and warning people early, but also the even greater and more consequential beneficial potential of reducing severe weather in the future.
1:27:53
And so I think that the United States government and Rain Maker should and and are absolutely interested in mitigating severe weather in a manner similar to Project Storm Fury. That makes sense.
1:28:04
I I I think the PR what you were getting at Jordy like the PR difficulty here is that like when there's not enough water, crop yields are lower, prices go up, but it's very distributed.
1:28:16
Everyone feels it a little bit.
1:28:19
Whereas when there's too much water and there's a flash flood and individuals die, you have a very it's a very emotional, very uh it's very concentrated.
1:28:27
The pain's very concentrated.
1:28:28
concentrated. And so that's why this this story I mean normally normally when there's a natural disaster there's you can you can critique the government for their response to it but there's not somebody sitting there that a scapegoat right and so it's easy
1:28:47
it's it's you know whe whether it's online accounts that are just engagement farming or it's a politician y uh you know scape you know the the the concern is that uh and your concern is that the industry becomes a scapegoat and uh America loses a capability that our adversaries clearly care a lot about. Yeah. My my question is like we're we're Yeah.
1:29:10
My my question is like we're we're seeing this bifurcation.
1:29:12
It seems like Ted Cruz came out in support of the idea that cloud seating had nothing to do with the Texas floods.
1:29:18
Marjorie Taylor Green has taken kind of the other side of that.
1:29:21
Um my question is like these are politicians at the end of the day.
1:29:26
They're not independent scientists. Who can we go to?
1:29:29
Who can the population go to for like a truly independent review of this situation?
1:29:37
Like is there is there some sort of independent governing body or are there are there respected scientists that kind of don't have a financial or you know political incentive one way or another?
1:29:49
Um h how do you think the uh the the populace should be satis obviously you're telling your side of the story you're going direct you're explaining things you're laying out the data but what uh what what do you expect people to look for in an independent analyst? Yeah. Yeah.
1:30:08
So, for one, um I think that Noah, the National Weather Service, the National Center for Atmospheric Research, um all of those are great third party entities that can review the information, corroborate the information that we've provided.
1:30:26
Um pro provided of course that they continue to exist and remain funded. Sure.
1:30:31
Um I think that this probably demonstrates why it is important that we should retain some capability nationally to forecast and research the atmosphere because there's there should be some body that's capable of reviewing this to ensure that it's safe.
1:30:45
Um I'll also say, you know, regarding the uh scapegoat dynamics that that exist right now, um I've thought about this pretty prayerfully and intently over the last few days.
1:30:57
And when there is a calamity of some sort, like I I've been trying to think about why people are uh say coming after rain maker or uh angry at Rain Maker.
1:31:08
And I think that when there is a calamity of this type, um if there was someone responsible, if there was someone or something that could be held to account, then in holding them to account, uh you could supposedly prevent this kind of thing from happening in the future.
1:31:24
Um the trouble with a true natural disaster as this was is that there is nobody to be held accountable.
1:31:32
Um and that makes the world a lot more tragic because it means that things like this will persist.
1:31:37
Um they they will persist indefinitely into the future.
1:31:40
Um unless and until some sort of technology could reduce the severity of severe weather.
1:31:48
Um we went through this with the California fires.
1:31:52
You know, it was like everyone was searching for like a single person to pin it on and like it came down to like, you know, some people built their houses the wrong way and there's some building codes that need to change and there's some water rights and water flow and there's some different like we need more goats in certain areas.
1:32:09
There's like a million different things that could have prevented this if they all were all working together as a welloiled machine and had the forethought.
1:32:16
Um, but it's a very very frustrating and difficult situation.
1:32:20
So, our our thoughts and prayers are with everyone who's been affected.
1:32:23
Um, but thank you so much for stopping by. This is fantastic.
1:32:26
Thanks for uh breaking it all down for us. Thanks, guys. Appreciate it. Cheers.
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Um, and we have our next guests.
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1:33:21
We installed it on our producer Ben's computer very early on when we were posting clips to make sure that we didn't have any uh spelling or grammar errors when we would post on social media. Very useful tool.
1:33:32
And now they've combined forces.
1:33:34
and we're going to talk about how we can uh what the shape of the business will be going forward, how these products play together, what the modern suite of tools looks like going forward.
1:33:47
Well, welcome to the stream. How are you? Great. How are you? Fantastic.
1:33:52
Uh would you both mind kicking us off with an introduction on yourself and uh the companies that you run and then we'll talk about the the the acquisition? Great.
1:34:03
Uh sure, Mah, you want to go first? Okay. Yeah, happy to. Uh, hello everyone. I'm Rahul Vora.
1:34:07
I'm the founder and CEO of Superhum, which if you're not familiar with is the most productive email app ever made.
1:34:14
Imagine getting through your email twice as fast as before, responding faster to the things that matter, and saving 4 hours or more every single week.
1:34:22
We're also inventing the future of productivity with AI.
1:34:26
Imagine waking up to an inbox where every email already has a draft reply.
1:34:30
You would simply edit and then send.
1:34:32
And of course with Gramly we are going to build the AI native productivity suite of the future. Great. Amazing. Yeah. I'm Shashir Motra.
1:34:39
Um I actually started a different company.
1:34:42
I started a company called Kod um about the same time as Rahul started superhuman and about uh 7 months ago or so um Grammarly acquired uh KOD and I stepped into the CEO role running uh Grammarly.
1:34:56
So been working in and around the industry for a long time before KOD.
1:35:01
I used to run the YouTube group at uh Google, worked at Microsoft in the early days.
1:35:05
Actually started my very first job was working on Outlook.
1:35:08
So I've got back in 1998.
1:35:08
I worked on email or email. Fun to come back to it. Yeah, that's amazing.
1:35:14
Um so I I'm not sure who's best to answer this, but I'd love to know about uh how this deal came together.
1:35:23
um when you two first met and uh you know we we we keep going back to this post that like you'll meet your acquirer like five years before you uh before the deal goes through. Is that this case?
1:35:33
Is this a narrative violation?
1:35:34
Uh kind of how do you get to know each other? Eight years. Break it down.
1:35:39
Why don't why don't I take the first half and then Rahul can take the the second half.
1:35:42
Um so I can talk a little bit about the deal and what we're doing.
1:35:46
Um and Rahul can give the fun origin story.
1:35:48
Um so um maybe just as a as a little backgrounder for everyone.
1:35:52
So, Grammarly at Grammarly, our goal is to build an AI native productivity suite with the agents and applications that drive productivity for every individual and team in the world.
1:36:01
Um, so a lot of that is probably new to people because people have generally thought about Grammarly as a much narrower product uh than our aspirations for it.
1:36:10
And you know, the way we generally talk about it and I'll talk about agents first and we can talk about applications, but we generally think about Grammarly as the OG agent.
1:36:18
It's uh about uh 16 years now um that the company has been helping at this point about 40 million daily active users where Grammarly is the communication assistant that lives right next to you in every surface you work in.
1:36:34
But people misunderstand the technology because they think it's about grammar but actually the technology of Grammarly is mostly about bringing AI right to where users work.
1:36:41
So we can work in about 500,000 different applications where we read what's on your screen.
1:36:46
We can annotate it in an unobtrusive way and we can make changes on your behalf.
1:36:54
And so from that perspective, we we call this layer, we call it the AI superighway, bringing AI right to where people work.
1:36:59
And in that analogy, up till now, we've only been running one car on that highway.
1:37:04
That's the car with your high school grammar teacher in it.
1:37:08
And that's a very useful car.
1:37:08
and it generates, you know, over 800 uh 78 $800 million in revenue now.
1:37:12
But I think it's a uh a vast subset of what you should be able to do with that.
1:37:18
And so a big part of our strategy is opening up Grammarly to become a platform so you can build any sort of agent on it uh and have those agents come to you where you work.
1:37:29
That's the first part of our strategy.
1:37:30
Second part of our strategy is taking those surfaces and building the first party versions of the surfaces that we think really matter.
1:37:36
the surfaces where people work um every minute of every day where all the work really gets done where you really want to work not not only alongside humans but alongside agents as well.
1:37:47
So that's why we bought my prior company KOD that we make an all-in-one document solution that blends documents, spreadsheets, presentations, applications into one surface that produces all the work artifacts for you.
1:37:59
But another key part of work is communication.
1:38:01
And for many people the dominant communication tool they use is email.
1:38:06
um uh it's something like 3 to four hours a day the average person spends in their email inboxes and this actually shows up in the Grammarly stats really high.
1:38:14
So email turns out to be the number one use case of Grammarly.
1:38:16
Um we uh revised something like 50 million emails per week.
1:38:22
Uh it's three of the top 10 applications that Grammarly is used in are uh our email clients.
1:38:26
Um and so we saw that as an obvious place to go to go work next.
1:38:32
Now from my perspective I think email is a category that is particularly ripe for disruption.
1:38:37
As I mentioned I started my career working on Outlook in 1998.
1:38:40
Um and since then there's a round of innovation with Outlook.
1:38:45
There's a round of innovation with Gmail and then there was a decade of not much and then Rahul came along and built a great email experience.
1:38:52
So when we went looking for which surfaces really matter, we landed on email.
1:38:55
And when you look at the email category, as you mentioned, there's only really one player that's meaningfully innovated in that space.
1:39:03
And that's when we call Russ.
1:39:04
That's a little bit about how the deal came together. Amazing. Great. It's funny.
1:39:09
I I just just for some added context, I basically have been lucky that my entire uh I think I got on Superhum in 2018.
1:39:16
You launched in was it when did the beta launch? It was 2017.
1:39:22
Our first paying customers were at the very end of 2017. So you're Exactly. Exactly.
1:39:26
But I graduated college in 2018.
1:39:28
So as a professional, I've only had to experience I'm a lucky I'm the lucky batch, right?
1:39:34
Um and uh still use it today.
1:39:34
Um so so thank you for uh you know make I I never had to be an Outlook guy. I had a yahoo.
1:39:41
com email address when I was a kid. Yeah. Vintage. Vintage.
1:39:44
Um, anyways, uh, Ra Rahul, I don't know if you have anything to add, but then I have a bunch of kind of follow-up questions on on the last anecdote. Yeah, for sure.
1:39:54
I think it'd be fun to tell the the origin story of the deal, how it came together, and I think there's a lesson or two in here for for other founders or the entrepreneurs listening.
1:40:03
So, I'll also try and make it useful.
1:40:05
Uh, so to your point, the foundations, the the seed for this deal was planted many many years ago.
1:40:09
It was 8 years ago, like said, it was back in 2017.
1:40:14
And back then he was the co-founder and CEO of a company called Koda.
1:40:18
And we were actually at a conference together uh in Hawaii. So it was really nice.
1:40:22
And uh that said, I didn't really want to go.
1:40:26
You know, this was a four or five day thing accounting for travel there and travel back.
1:40:30
And one of my co-founders, VC Soda, was really encouraging me to go.
1:40:35
you know, he he would say things like, "Listen, building a startup is just as much about who you know and the connections that you have and being able to pull opportunities together as it is building and marketing a great product."
1:40:46
And I'd be like, "Well, I, you know, I want to work on this feature.
1:40:49
I want to do this thing."
1:40:50
But in the end, he just pushed me out of the office and put me on a plane and go to Hawaii to Hawaii, which sounds weird, right?
1:40:56
Like resisting going to Hawaii. But anyway, there I was.
1:40:57
Uh, Shashir was there as well.
1:40:59
And it was one of those special moments where nobody else was around.
1:41:03
So, it was just the two of us by a pool, two productivity nerds nerding out about productivity.
1:41:08
And he told me that he'd worked on Outlook back in the day.
1:41:11
We got into some really deep conversation about productivity.
1:41:16
And as you know, back then we only did one-on-one VIP concage on boardings.
1:41:18
You must have gone through one yourself if you on boarded in 2017.
1:41:22
Uh, so I on boarded him right there and then, right by the pool.
1:41:27
And those who've gone through the onboardings know one of the very last steps is when we ask you to close Gmail.
1:41:34
And so I was asking him to move the mouse over to the Gmail tab and close it.
1:41:37
And when I asked him to do that, another tab caught my eye, which was an app called Krypton.
1:41:41
So I asked him, "What is that thing?"
1:41:44
And then Shashir then proceeded to give me the best product demo I had seen in years. My jaw hit the floor.
1:41:50
It was a document, but it was also a spreadsheet. It was also a database.
1:41:53
It was a collaboration tool.
1:41:55
tool. was a mini app builder and and maybe today we take these things for granted but back then in 2017 this was truly mind-blowing and so Krypton then renamed to become coder and coder of course late last year joined Grammarly
1:42:08
now in his acquisition announcement he wrote and I have the quote here as I watched the foundational capabilities of AI change how just about how every tool and surface operates I started drafting my 2025 memo for the team I titled it the AI I native productivity suite. And
1:42:25
And this just set a whole bunch of bells off in my brain in a good way because at superhuman our vision has always been to build the AI native productivity suite of choice.
1:42:37
And email is obviously a critical part of that.
1:42:38
It's a much bigger problem than most people realize.
1:42:42
There's roughly a billion professionals in the world and on average we spend 3 to four hours a day in email.
1:42:45
So that's 3 billion hours every single day or north of a trillion hours every single year.
1:42:53
we actually all spend more time in emails still than any other work app.
1:42:55
So we caught up uh early this year in January actually a few days after he became the CEO of uh Grammarly and over the course of several conversations it became very clear that we were working towards the same vision which is to build this AI native suite for apps and agents and and then as Shash said email sits at the heart of where Grammarly is used today.
1:43:16
It's the number one use case helps write more than 50 million emails.
1:43:20
Another stat that I found very fascinating is that 17% of words accepted on Grammarly are actually accepted in an email service. Wow. Okay.
1:43:32
Bunch bunch of questions.
1:43:32
Uh and I'm sure I'm I'm excited to get your answers.
1:43:35
So, first is integration.
1:43:35
H how do you see sort of the the the plat, you know, how do you see both the brands and the products integrating and working together over time?
1:43:46
together over time? because you have a great challenge of having three great products that people love and three brands and uh in order to deliver on this this you know uh this true you know long-term vision of a of a productivity suite I imagine over time you want to integrate them deeply um I'm curious
1:44:07
what that looks like yeah maybe you want to cover the product part and I can talk about the brand part sure yeah I'll I'll do product uh really briefly and then we can go as deep as you You know, I think one of the most exciting things about the deal from a superhuman perspective is the access to significantly greater resources. So, you
1:44:23
So, you can expect us that we'll invest more than ever than we have done in AI.
1:44:27
We're also absolutely not done with our core email experience.
1:44:32
We'll be doing a lot more there.
1:44:34
We're going to build out calendar and tasks and then connect those beautifully together.
1:44:38
We'll also start to spread our wings beyond just emails.
1:44:42
So, we're going to reimagine chat.
1:44:44
We're going to redefine collaboration, pulling on everything that we've learned over the last 10 years about work communication.
1:44:49
And then, as Shasher mentioned, we are also working on a whole new way of working with AI agents.
1:44:55
Agents that we think will free all of us up to be more creative, strategic, and closer to achieving what we call our human potential.
1:45:03
And then just to double click on that a little bit more, uh we really think we're entering the age of agentic computing where AI agents, they're going to work on your behalf. They're going to reason.
1:45:13
They're problem solving and they're incorporating detailed context about your work.
1:45:16
They're actually also interacting with other systems and agents.
1:45:20
And I think we're we're beginning to see these in some of the products that people are using today.
1:45:25
And for so many people, email is just at the center of where we work.
1:45:27
You think about project statuses, customer communication, meeting updates, deal execution, so much more.
1:45:32
It all actually funnels through email.
1:45:35
Whether it's a system of record or that's actually where the work is taking place.
1:45:39
So we also think that email is the perfect place to deploy a collection and a suite of agents.
1:45:45
You can imagine an agent triaging your inbox before you wake up.
1:45:49
You could imagine another agent drafting responses in your own voice and tone incorporating context about you and from your work and at the same time another agent as surfacing insights as scheduling meetings.
1:45:59
They're syncing with your other systems of records and your other agents.
1:46:03
Uh and I'll give you a specific and concrete example. Cool.
1:46:07
This is something you can actually do in Superhuman today and then I'll talk about how it's going to evolve uh in the very near future.
1:46:13
Uh and let's talk about search or or asking your email things.
1:46:18
For over 40 years, we've had to rely on what we kind of hilariously call search.
1:46:21
But if you think about what that is, you have to remember senders.
1:46:25
You have to guess keywords.
1:46:26
You then have to scan subject lines.
1:46:28
And now in Superhum, you can simply ask where is the Q3 offsite or what are my flight details?
1:46:33
Uh, and a very real example that uh, blows people away whenever they see it is this thing I do whenever we launch a feature.
1:46:42
Whenever we launch a feature, and you'll know this using Superhum, I send an email to every single person um, who uses the product and then we get a whole bunch of replies back, usually several thousand responses, and I still personally read through every single one.
1:46:55
I reply to some of them, but what I'm doing is I'm copying and pasting my favorite quotes into a Google slide that I can then present at the next company. all hands.
1:47:03
Now, this takes half an hour to do properly.
1:47:06
With superhuman today, you can just ask uh what are the top 10 most positive customer responses to the uh calendar see your week launch, let's say, and then boom, boom, boom, boom, boom, immediately within 5 10 seconds, I have the answer.
1:47:19
So, that's taking what is a half an hour task and making it work in five or 10 seconds.
1:47:24
Now, we're evolving that so that you can then continue the conversation and take it much beyond email.
1:47:31
You can imagine me then saying um okay I want to convey the magnitude of the commercial opportunity to to the team.
1:47:38
So um can you annotate each quote with the name of the person, the company they work for, the size of their current superhuman account and the total number of employees they have and then compute an estimated size of price like how much revenue is there at stake if we were able to sell into that company.
1:47:54
And you can then imagine the superhuman set of agents figuring out what to do with that, realizing the answer actually isn't in your email.
1:48:01
It's probably in your CRM.
1:48:01
So, a sales intelligence assistant is called into the mix.
1:48:06
There's a handoff between the agents and then the answer is right where I kicked off the conversation in my email app where I happen to spend 3 hours a day.
1:48:15
And you can continue the conversation.
1:48:17
You can then say, "Okay, let's please turn this into a presentation."
1:48:20
Uh, and then perhaps it works with, let's say, the Gamma agent to produce an amazing, beautiful presentation in your own brand for the company.
1:48:27
And then you might say, okay, I want time to practice this before the all hands.
1:48:31
Can you please schedule time in my calendar to do so?
1:48:33
The agent's like, well, you're completely booked it before the all hands.
1:48:36
Uh, but we can move some things around.
1:48:38
And it's smart enough to know that it's easier to move a one-on-one than it is to move a team meeting.
1:48:43
So, it recommends moving the one-on-one. It goes and does that.
1:48:44
And now you have time blocked in your calendar to uh learn a presentation that was created for you in 10 seconds by this agent that just read thousands of email to get the content work that literally would have taken an hour done in let's say one or two minutes.
1:49:01
So that's the kind of future we're working towards.
1:49:03
Wild uh there's going to be 250 agent agent startups uh that that are going to hear that and be like damn he's uh they're doing they're doing what I'm what I'm trying to do.
1:49:12
This is an ecosystem where we want to put those no to be clear, we want our marketplace to be the place you you deploy those those agents, right?
1:49:20
The the surfaces you want to work on.
1:49:22
If if you like, I could talk a little bit about the brand question as well. Yeah, that'd be great. Yeah.
1:49:26
So, and and you know, I I think my experience here is heavily formed by um before starting KOD, I I ran the YouTube group at Google and I think that was one of the best examples of an acquisition that I think flourished in a way that would not have been possible without um without the particular constructs we put in place there.
1:49:44
And there's a lot about that I think that we got right and I think we're I'm going to mimic a lot of that here as well.
1:49:48
that here as well. Um so in my goal is with Grammarly we're going to build the uh the Aative productivity suite of all the apps and agents that you um that you need some of them that will own some of them that we will be great partners with
1:50:02
um but it's really important that each of those retain an identity to and I think that's important because that's how they keep innovating and if you think about uh you know you started using superhum in 2018 you know you were
1:50:13
buying into a product but you're also buying into a team and a vision and a feel and all those things really matter and So I I really like this term of building a a comp a compound startup where each of those products still feel like they have an identity, they have a
1:50:27
brand, they have a mission, um they have their their they have similarities for things we want to work across, but they have their own perspectives on on the problems that make sense in that in that space as well. So we want agents to work
1:50:38
So we want agents to work across all surfaces.
1:50:40
It's very important that if I set up my sales agent that it should be able to do um some of the experiences that Rah just described while it's in my email, but while it's in my document, it'll give me a different set of experiences.
1:50:51
When I take it out and and uh use it while I'm using a third party application, it should still be able to bring my context with me.
1:50:58
So, there will be things that need to feel similar, but the individual brands will remain separate.
1:51:02
The last thing I'll say about that is the overall corporate brand for Grammarly will change.
1:51:07
Uh we're working on a new name for it.
1:51:09
Um, so Grammarly will become one of the subbrands itself.
1:51:12
We think of it as I was describing, one of the most important agents in that in that platform.
1:51:17
So the new brand's coming.
1:51:17
Uh, I'm really excited about it, but uh, not announcing it yet. Yeah, soon.
1:51:24
I Yeah, I can't wait to see it.
1:51:24
Um, I'm curious how you think about the, uh, the tension between yourselves and someone like a Google, uh, a Google Workspace.
1:51:34
Uh, I was joking with John the other day.
1:51:36
It feels like so many companies are so dependent on Google Workspace for the core kind of um just like team management infrastructure that they could just raise the prices every single month and it would take a really long time for even to try to figure out something else.
1:51:52
And it reminded me of of kind of the tension that some of the foundation model labs have today with the app layer above them, although that's quite a bit more intense.
1:52:00
But I'm I'm curious, you know, how deep down the stack you guys would go if you can talk about it or or if it makes more sense to focus on the agent layer or the app layer.
1:52:13
You know, I maybe I can start the it's interesting the three products we're bringing together, Grammarly, Kod, and Superhum all have competed with the Google suite or the Microsoft suite for years.
1:52:24
Um, and so I think we're all in the fire. Yeah.
1:52:29
And and I the the thing I'd say about it is it actually comes up less with customers than you would think.
1:52:33
Um the I think that when companies decide it's sort of um it's sort of like buying plumbing for your company.
1:52:41
You you buy one of these suites.
1:52:43
It covers lots and lots of different uh things, but it these have become a part of the furniture at the at the at the company and people don't really think about them as their real investments in productivity.
1:52:55
And so I I totally agree.
1:52:57
By the way, I'm just as this as the way work evolves, imagining trying, you know, setting up every every time you have a I could imagine a world where there's you're you're generating a new agent for a specific task and they have an email and I'm sitting here being like, do I really want to pay, you know, uh Google Worksp every time I spin up a new agent at Google Workspace another $25 a month?
1:53:21
And so I imagine I imagine you guys can take this in a direction that that um kind of reinvents that all of that plumbing in in the long run, but maybe it's not. Yeah.
1:53:31
And I mean I would say all three products have found different ways to be better together with the with the underlying products.
1:53:36
Obviously with Grammarly one of its hallmark features that it actually works in all those surfaces.
1:53:41
So it amplifies your investment in you know works great in Google Docs but it also works great in Slack and in Salesforce and all the rest of your products as well um for KOD deeply integrates with those products and then and then for superhuman you know Gmail or Outlook serves as a backend for those those providers.
1:53:56
So, it's it's not really a question of of less investment in in those core uh infrastructures, but if your users want the best possible experience for what they're doing, you're going to go get the best tools.
1:54:08
And in a sort of macro scale, the amount of money you're spending is such a tiny amount of money compared to what what you're actually investing in your employees to go stick another 10, 20, 30 bucks a month for people that you're you're spending hundreds of thousands of dollars on to get them some cases hundreds of millions.
1:54:24
some cases hundreds of millions.
1:54:26
Um we're going to have to raise our prices for those employees, but uh you know you're you're going to get a huge return for them and people don't really care that much about their sun cost and their plumbing. Totally. Question.
1:54:38
Um I I I I have kind of two somewhat related questions.
1:54:42
One is um I I I don't want to say that, you know, Grammarly is a Chrome plugin, but a lot of people experience that way.
1:54:50
And uh and I noticed I was using a different Chrome plugin and Chrome the Chrome app store like updated and I lost functionality because they changed their policy and this particular plugin wouldn't work in the new rules.
1:55:04
And so I'm wondering if this plugin different this is not Grammarly. It was a separate one.
1:55:10
It was called Ublock Origin.
1:55:10
It would let me go in and select specific divs on specific websites and basically mute them every single time I hit that website.
1:55:17
It was very very cool, but it was deemed to be like too not like not privacy safe and it was really annoying for me because like I enjoyed this and I was excited to use this thing and then I lost it and I mean I might be able to like download it and sideloadad it or something but it was it was it was difficult and so I'm wondering about like sharp elbows in the because the Chrome plugin is an interesting wedge a interesting go to market.
1:55:39
It unlocks so many different things.
1:55:41
because we've seen this with like the OpenAI chat GPT app uh using the ADA or the the the the accessibility features to kind of plug into any IDE on day one.
1:55:49
Like you just have such an interesting ability to plug into, you know, tons of apps with AI in a bunch of interesting ways on and and like you're native there, but it feels like Google might be getting a little bit more sharp elbows there.
1:56:04
Has there been any attention there?
1:56:06
Do you think that there will be more over the long term?
1:56:09
What are the risks to building on top like building a platform on top of another platform?
1:56:15
Yeah, I mean I'll maybe just to to two parts to the answer.
1:56:18
First off, just to correct one misunderstanding.
1:56:19
The Chrome plugin is a very big part of the Grammarly um product.
1:56:23
Um there's also a desktop application.
1:56:26
There's also a set of mobile applications. So iOS and Android.
1:56:29
Um and we have millions of users on each of those as well. Yeah.
1:56:32
And uh so but I understand that the product is is synonymous in many people's heads with the with the Chrome extension first.
1:56:39
Um but that's very important because we have to work where users work and sometimes you work in a web browser, sometimes you you know many people use Slack as a desktop app, use Superhuman as a desktop app and so on.
1:56:48
So you have to be able to work in those places.
1:56:50
I will say that um staying on that line of where these platforms are is kind of become the core asset of the company.
1:56:55
become the core asset of the company. So that's what I it's kind of what I meant by people misunderstand Grammarly like that I do have a team here that works on being a great grammar agent but a massive team that works on how do we integrate with all these products in a safe and secure way and one of the
1:57:11
things we've realized is that we've done this just for the grammar agent but what if we could advertise that across a much broader set of agents and so now if you're if you're someone building a new agent you could go build a Chrome extension a desktop app and so on let's I mean I'll pick I'll pick an example Let's say um I'll pick a book author. So, you know, I I really like um Kim
1:57:29
So, you know, I I really like um Kim Scott.
1:57:31
She wrote a book called Radical Cander.
1:57:33
Um we spent a bunch of time with Kim on right now. She sells a book.
1:57:35
You stick it on your shelf.
1:57:37
You kind of forget about it.
1:57:38
She wants to build an agent that sits right next to you and says, "Hey, you're not following the principles of the book right now." Oh, interesting. Yeah.
1:57:44
So, so I'll I'll say it because I'm thinking it, but uh it it seems like, you know, uh I'm going to say this and then I'll provide some more context, but a lot of people uh you know have been very triggered by the marketing that Cle has done.
1:58:01
But at the same time, what what they had surfaced and what you guys had basically started doing years and years ago was was understanding what a user is doing on their screen and starting to surface information and help them take action.
1:58:17
And I think that what you guys are building towards and specifically this app layer on top of this like private secure way of surfacing context will in hindsight be be incredibly obvious that that was how we should be integrating AI in our workday because the idea of like you're working
1:58:36
in an app and then you go in another app and you like type a little bit and then you take that and maybe you go back into the other app and then you're just like you know tossing this over the wall makes no sense when things should just be getting constantly served. This is how this is how people were
1:58:49
This is how this is how people were programming precursor.
1:58:52
It was like copy copy the code copy the Python into chat GBT copy the result back and it was like okay there has to be a better way. Yeah.
1:58:58
And and I don't want to tr I I want a I I as a user I would love to be able to have a bunch of different experiences like that but I don't want to trust I don't want to I don't want a hundred different companies to have full read access to my desktop screen and my microphone or or any of these other um things.
1:59:15
So I'm very excited about where where you guys are going. That's exactly right.
1:59:18
So it is a it's tense to build a platform on top of your browser desktop so on.
1:59:23
But once we've once we've done that we can now make it available to the Kulies of the world to the Kim Scots of the world so on and say why are you going to figure out how to integrate with every one of those applications and we can do that for you.
1:59:36
You should focus on the logic of what do you want to suggest to the person and when. Yeah totally.
1:59:40
I mean couple couple more questions wanted to fire off.
1:59:42
uh you guys are well capitalized, you generate a lot of revenue.
1:59:46
I'm curious how you're thinking about operating the business on a go forward basis.
1:59:50
I'm assuming you're getting a lot of hopefully getting a lot of efficiency out of AI.
1:59:53
So maybe you can uh is the plan to uh focus on innovating while you know generating cash flow or are you guys going to you know uh continue to you know or or just burn and and uh you know run a more traditional valid playbook.
2:00:10
Grammarly has has been lucky to be a cash generating business for a long time.
2:00:15
And so it's sort of built into the DNA of the company.
2:00:18
Um and so I think in the I I'd like people to start thinking about Grammar with the new brand that we'll announce soon enough.
2:00:27
Um think of us as one of those top few AI companies and you know if you think of the foundation model companies providing great layers for all of us.
2:00:34
I I think we're hopefully the suite of applications and agents you really care about with one big business model difference.
2:00:41
We don't burn billions of dollars in order to do it.
2:00:42
Um and I think we can hopefully bring that to people in an efficient way which allows us to to grow and expand uh um uh in our own control. Yeah. Last question.
2:00:53
Are you guys uh in are you guys talking to more companies?
2:00:58
Uh if somebody has if somebody has a great product that's generating a lot of revenue um are are you I'm looking through the Google suite right now and you know I see lot of potential I see I see video lot of potential targets. Are you guys a buyer?
2:01:13
We are I mean I think I think we should I would love to talk to people with interesting ideas there.
2:01:17
I think I think there's a great opportunity here to go build that next AI native productivity suite.
2:01:23
We will build parts of it. We will buy parts of it.
2:01:25
If I looked at email for example, you know, we could have if we started to build an email experience anything like superhuman, nobody would have seen anything for for a decade.
2:01:34
And so it was very important for us to get a jump start with the number one product on the market.
2:01:38
Uh there are other cases where I think we can build.
2:01:41
I don't think we have to buy everything.
2:01:43
Um but I I I think we're a a great home for startups that are lacking that sort of scale.
2:01:49
um that want that distribution, want to get to a much broader group, but still want to work in an innovative environment.
2:01:55
So, yeah, I hope we're a great spot for that. Yeah. Awesome. Exciting. Congratulations.
2:02:00
Um and come back on when the rebrand drops.
2:02:03
I want to see that for sure. Excited about it. Can't wait. Congratulations.
2:02:06
I know you guys are going to cook up something great there. This is fantastic. Cheers.
2:02:11
Well, we will talk to you soon. Have a great day. Great chatting, guys. Goodbye.
2:02:14
Uh let me tell you about Adio customer relationship magic.
2:02:16
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2:02:22
You can get started for free or you can talk to sales.
2:02:24
That might be the cure for male loneliness. Or I'll make an intro. Yeah, just hit up Jordy. He'll introduce you.
2:02:31
I'll introduce you right to the top.
2:02:33
Uh let me also tell you about Finn.
2:02:33
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2:02:38
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And you know I love bake offs.
2:02:46
Do not get in a bake off within do it.
2:02:50
It's just don't don't even start.
2:02:53
It's gonna be a nightmare for you. It's gonna be bad. Really bad.
2:02:56
No one likes how that ends. Yeah.
2:02:58
Uh anyway, our next guest is ready to join.
2:03:01
Let's bring in Anker from carry. com. We like domain.
2:03:05
We love welcome to the stream. Welcome to the stream.
2:03:10
I think we're I think we might be on mute.
2:03:12
Um, but we're going to want you loud and clear for this one because there's a lot of exciting news. There he is. Good to meet you. What's going on? What's up, man? How are you? Good to be here. We're doing great. I'm excited to chat.
2:03:25
I'm sure you've been It's boring boring tax stuff, but anytime people get I'm here for it.
2:03:30
No, you make it digestible.
2:03:32
So, the goal I was talking to, it's like the last threads that I see on Axe that I'm like, "Thank you.
2:03:36
Thank you for making this thread because it's actually it's actually deeply researched, thoughtfully organized and valuable and it's not like have you ever heard of Mark Andre?
2:03:50
Uh so yeah, the goal here is to create something evergreen, the definitive playbook for founders.
2:03:53
So I I I think we want to create something that can be a resource for a long time ideally. Yeah. Great. Let's break it down. Let's do it. All right.
2:04:02
Where where do we want to start? Yeah.
2:04:04
I I I figured it would be helpful to kind of walk through uh our our audience is pretty evenly split between early stage founders, operators and you know executives and then um or just startup you know uh team members in general and uh investors and so getting kind of a lay of the land on how the big beautiful bill uh impacts all those different groups would would be awesome.
2:04:32
But maybe first I would love uh you know some background on yourself, how you got into this Carrie and then we can get into all that. Yep. Sounds good.
2:04:41
Well, Jordy Jord's an investor so has a little little Not for me. Not for me. Not for you. For everyone else.
2:04:47
Um yeah, but I've been running a company called Kerry for almost three years now.
2:04:53
I am an immigrant to America.
2:04:53
I moved here knowing zero about personal finance, zero about taxes.
2:04:56
I sold my company 5 years ago and I was facing a giant tax bill.
2:05:01
So I hired very expensive lawyers and accountants and they were able to do black magic to basically reduce my tax bill dramatically and it made me realize like the tax code in this country it's kind of there's so much stuff in there but very few people actually know how to leverage it.
2:05:19
So when it was time to start a new company, I spent I don't know couple months looking into this and made it my mission to, you know, dive deep into everything in here.
2:05:28
And what we do at Kerry is we're like, can we build software to give people, we call it tax alpha, but basically ways of saving money on taxes on autopilot.
2:05:37
So again, my compliance team is going to make sure I say this.
2:05:41
This is not tax advice, legal advice or investment advice, but we never give that kind of advice on this show. Yeah. Yeah.
2:05:48
But I have spent a lot of time in the last two three years working with at this point thousands of business owners and I think I have a pretty good idea of you know generally how business owners can save money in taxes and this piece of legislation is the most significant one we've had since 2017.
2:06:08
2017 there's and one and before before we dive into that I think it's helpful I feel like you approached the the tax code like very much like an engineer and in the same way that you know if you sign up for a software product you're getting the benefit of that company spending millions and millions of dollars like building this product and then giving it
2:06:29
to you at a fraction of what it costs to create you're with Carrie the the idea has been how do you kind of create that same effect in some way for taxes because if you're working with a f, you know, one of the best CPAs in the world, they will charge you for the service and then they'll go charge you the same price to someone else for that same service and they'll just do that a bunch of times. And you guys created a
2:06:51
And you guys created a different um you know you you have a sort of a different incentive which is how do you create the maximum amount of value and then make it available to as many people as possible which is kind of the traditional software playbook uh applied to um applied to to a new category. Thank you.
2:07:10
You pitched my company better than I did.
2:07:12
But yeah, I mean there's all this stuff.
2:07:15
I mean there's so much nuance in it but like from a software perspective none of it is specifically hard.
2:07:20
The the challenge we have is we deal with fintech, right?
2:07:23
We're dealing with real money, custody, real assets. That's the complexity.
2:07:26
But there's so much stuff in the tax code that we're look if we're focusing on I don't know 1% of what's out there.
2:07:32
But there's like I generally believe for most people, I know VCs probably disagree with this.
2:07:40
There's no alpha in investing.
2:07:40
The average person should just index the market and get to work.
2:07:43
But you can find alpha by saving money on taxes.
2:07:45
If you can save 10 20% off the top so you have more dollars to index the market.
2:07:50
Um that's basically the thesis behind what we're building. Yeah.
2:07:55
Um so talk about uh talk about kind of maybe how the bill came together.
2:08:01
What what you expected to be in what what maybe didn't make it in.
2:08:05
There was a lot of chatter maybe was it four months ago around the carried interest loophole.
2:08:10
People were pretty triggered by that. I was triggered by that.
2:08:15
Um, but it sounds like that didn't make it in.
2:08:17
Um, but yeah, break down kind of maybe the leadup to the bill and then how it actually ended up getting implemented in its 10 states.
2:08:23
Yeah, I should also caveat by the way that like I'm going to tell y'all what's in the bill.
2:08:26
It is not an endorsement of the politics behind it.
2:08:28
Like you can argue either side of that that like that is out of scope for what we're talking about.
2:08:32
We're just talking about reality today.
2:08:34
What what is what is becoming law? So yeah.
2:08:37
So 2017 there was something called the TCJA tax cuts and jobs act where there were a lot of temporary measures that benefited groups of people the administration wanted to benefit.
2:08:48
Typically this was entrepreneurs, business owners, investors and real estate developers.
2:08:53
Part of this was there was a lot of short-term measures put out for that were only going to last eight years in the future.
2:08:59
But what this bill has done is it's made most of them permanent.
2:09:04
So there's a lot of things like you know if we're talking about startup founders specifically there's and we'll break them down there's many things in this that will make your life better.
2:09:15
Even if you're a soul prop LLC or an escorp a bunch of things make this better.
2:09:20
If you're someone that's coming up against the estate tax this bill helps that.
2:09:24
So lots and lots of good stuff.
2:09:27
Uh maybe you could kick off with a little bit of the background on like the understanding of QBS.
2:09:32
Like for the last decade, I feel like the rule of thumb has been like you start a company, you sell it for a bunch of money.
2:09:38
Uh the first 10 million, you don't have to pay federal taxes on it.
2:09:43
So if you're in California, you're still going to be paying California uh tax potentially, but you might be able to think about it as like that that if you get a $10 million liquidity event, you're basically taking close to 10 million potentially potentially 10 million in New York. 10 million.
2:10:01
So yeah, New York 10 million.
2:10:02
And so um so you don't need to move to Puerto Rico. Good news for that.
2:10:06
Um but uh that was a funny time when people were were we're were so obsessed.
2:10:11
I got to move to Puerto Rico now. You're in Puerto Rico.
2:10:15
Oh, you're having a massive liquidity event. Yeah. Um but but yeah. Yeah.
2:10:16
Talk through talk through the reality like how how real was the the original QSBS uh um uh process?
2:10:25
what were some of the the the hiccups if it was an aqua hire or an asset sale that might trigger uh income tax or something like that and then and then talk to us about what's changing. Yeah.
2:10:39
So QSBS for those that don't know it I mean qualified small business stock this is what kind of got me down this whole path.
2:10:46
I mean I was running my startup for 6 years.
2:10:48
We were about to sell the company and I didn't know about QPS.
2:10:52
It was the best surprise when my accountants are like guess what you actually could not pay taxes on $10 million.
2:10:57
I was a resident of New York, so no state tax as well.
2:11:00
But not just that, you the QSDS limit is per shareholder.
2:11:04
So I can give shares to my brother, my parents, and now your $10 million becomes $40 million.
2:11:09
You can set up trusts as well to multiply it.
2:11:14
So it already was exceptionally generous and it's available to every shareholder, investors and employees.
2:11:18
Though sometimes employees don't hit the threshold since you have to hold shares typically for five years to unlock the benefit, right?
2:11:27
Five years is a long period of time.
2:11:29
One of the big changes this bill brings about is now if you hold shares for only 3 years, you get half the exemption and if you hold shares for four years, you get 75% of the exemption. Wow.
2:11:41
So that's one big change.
2:11:41
And and to just to level set here like the whole idea behind this particular tax incentive is to incentivize innovation and building new companies, small business.
2:11:54
It's the opposite of like high frequency trading.
2:11:56
You have to create value materially.
2:11:59
The people that are primarily like the the average person that's benefits from this is somebody who starts a plumbing company, runs it for 20 years and sells it.
2:12:10
And is that right or is it is it half I think I I think that's the intent of it.
2:12:15
I think the reality of it is Silicon Valley benefits from it more than anyone else.
2:12:18
I think the intent and what's actually happening but again that gets into the politics is a little bit different because technically services businesses are not included. Yeah.
2:12:27
You have to um but the reality is if you look at most big tech exits right now people are paying substantially less in taxes.
2:12:36
There's a New York Times article with the Roblox founder.
2:12:37
He set up 12 different trusts to multiply QPS to $120 million.
2:12:44
Um he actually joked that raising a kid in California is so expensive that the QPS exemption is what makes the whole math worth it.
2:12:50
It's kind of an insane thing, but that's a crazy thing to say. Yeah.
2:12:55
What types of small businesses what what what are what are all the different types of small like I mean just generally like what are the different categories?
2:13:02
Because if you take out services like the software doesn't apply to that, right?
2:13:08
You can just be building regular SAS.
2:13:11
So typically typically the requirements for QPS are a few different things and one of them is changing now is one you have to be a Ccorporation.
2:13:18
So that's historically been like LLC's corps don't count.
2:13:20
You have to be a CC corp and hold shares for 5 years.
2:13:22
When you acquire the shares, the company should have less than 50 million in assets. That was the old rule.
2:13:29
Now it's 75 million in assets for startups.
2:13:31
That is typically the cash raised, not the valuation.
2:13:36
So it takes you pretty far, right?
2:13:38
Like it you before you raise 75 million bucks, you get you get pretty far.
2:13:42
And then there's other stuff.
2:13:42
It has to be an active trade or business.
2:13:46
And there's a few disqualifying categories like services or something based of someone's brand does not count.
2:13:53
But the way QSPS works is it's ultimately a stance your accountant takes.
2:13:56
So, as an example, let's imagine you're a tech- enabled service business.
2:14:00
You could find a lawyer or an accountant to take a stance that QPS counts and there's a, you know, there's a good chance it just works out that way. That makes sense.
2:14:09
What besides QPS has has changed in any meaningful way. Yep.
2:14:14
So, again, just to reiterate, the other big benefit is the $10 million limit per share shareholder is now 15 million.
2:14:20
So three big changes 10 to 15 there's now partial QPS and you can now be up to 75 million in assets. Wow.
2:14:28
Outside of QSBs I would say is that back date?
2:14:30
Is that back date at all? No.
2:14:32
So it only for companies incorporated from Friday on or you have to buy the shares from from July 4th. Friday onwards. Wow. Oh wait.
2:14:40
So so this only affects going forwards. Yeah. Going forward.
2:14:44
But new share purchases would count. Yeah. Okay.
2:14:47
So if you buy shares, but theoretically what I'm actually not sure about is probably maybe a lawyer can weigh in and if I'm an employee who has options and I exercise my options today, would that count?
2:14:59
There's a there's a chance it could. Mhm. Interesting.
2:15:01
So if you own 20% of a business started in 2019, you hit your five years, the company sells for $100 million, you get 20 million, you're still at the $10 million QBS exemption, you're not going to unless you set up a trust and gift shares to someone else. Sure, sure. Sure. Interesting.
2:15:19
Um, can you explain this bonus depreciation concept? Yeah, absolutely. Bonus depreciation.
2:15:25
I think Do you already have the private jet dude on?
2:15:29
He's coming on after this, so he'll talk about big for it's big it's a big for his business too but basically basically the way depreciation works is when you buy any kind of physical asset like consider buying a commercial building it loses value every single year every year you can take that loss of
2:15:46
value is depreciation it's a phantom loss in that you're not losing money but you can deduct it from taxes sure what bonus depreciation lets you do is it lets you frontload depreciation for typically things that have a useful life of less than 10 years you can take all of the depreciation upfront. Mhm. Mhm.
2:16:03
So, this is really significant for all the real estate bros out there cuz what they can now do is you can buy a building.
2:16:08
You can do something called a cost segregation study which will take the building, it'll break it down into all of its components.
2:16:14
It'll be like the HVAC is worth this, the windows are worth that, the doors are worth that.
2:16:19
Anything with a usable timeline of less than whatever I think it's 10 years, you can depreciate upfront.
2:16:23
So the upshot is you can buy a commercial property for million bucks, 2 million bucks, put 20% down, but also get a 20% tax loss.
2:16:33
So you can deploy cash much faster.
2:16:35
And people think this could lead to real estate prices growing.
2:16:38
But this applies to private jets, heavy machinery, cars, all kinds of equipment. Makes sense.
2:16:45
What else are you tracking? Anything else?
2:16:47
Stand estate taxes big, right?
2:16:48
Estate taxes are are are massive.
2:16:50
I mean, historically, when you die, anything above the estate tax exemption gets taxed at 40%.
2:16:56
This bill makes it permanent at $30 million per couple, which is a very, very high threshold.
2:17:03
That was actually supposed to go down to 10 million bucks.
2:17:05
So, it's a huge swing and there's a lot of sort of trust planning companies that were betting on this happening, but now it's a much much bigger exemption.
2:17:17
They were betting on 10 happening and so 30 is correct.
2:17:21
Like had the election gone a different way, what would have happened is the estate tax would have fallen by almost half.
2:17:26
Instead, it actually went up. Interesting.
2:17:30
Uh what talk about this relief for software companies in America um advertising software developer salaries.
2:17:38
I remember that hitting the timeline and being really hotly debated.
2:17:39
I don't I don't remember if it actually had a material impact on a lot of businesses.
2:17:46
It seemed like there was a lot of fear, but I don't remember it actually putting friends. What happened? Take me through it.
2:17:53
I saw like it was a terrible piece of policy. It's called section 174.
2:17:56
What it basically said is you have to amortize a developer cost over 5 years.
2:18:02
So imagine you're a software company that's like just about break even, slightly profitable, maybe even lose money.
2:18:08
You could lose money but be deemed profitable because you can only deduct 20% of your developer salary as a cost.
2:18:18
So imagine you're paying a developer $150,000.
2:18:21
You can you have to break that expense over 5 years.
2:18:23
So this is disastrous cuz you could own taxes despite losing money.
2:18:28
Y um this bill fixes it and you can now again take the entire deduction year one. Yeah. For local talent only. Yeah.
2:18:36
That one always seemed seemed odd because um obviously a real cash cost.
2:18:41
Local talent only though. So offshore.
2:18:43
So this does this does hurt offshoring. We'll see.
2:18:46
We'll see sort of where it nets out.
2:18:48
But this was definitely one of the few things that I think unanimously everyone's like, "Okay, this actually makes sense." Huh. That's great. Cool.
2:18:54
Well, anything else, Jordy? I think that was it.
2:18:57
Thank you so much for anything else.
2:18:58
Anything else top of mind that that that people should be thinking about?
2:19:01
Uh I mean there's so much stuff in there.
2:19:03
Again, you know, I talk about it a bunch.
2:19:04
I think these are these are sort of good highlights. Yep.
2:19:08
Um but yeah, always talk to your tax professional.
2:19:10
Not not tax advice, of course.
2:19:12
Never never from Anchor, but carry. com. Check it out.
2:19:16
Well, thank you so much for stopping by. Cheers. Great to catch up. Bye.
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John, every single day somebody runs by.
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I've got text every single day. It's crazy.
2:19:55
So we're by Sunife Smoothie right now in New York.
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Go head over to Sunlife and you can see our building.
2:20:01
I highly recommend just doing it out of home campaign.
2:20:04
Uh let's bring in Preston, Mr. Mr. PJ. How you doing? What's going on, boys? It's good to see you.
2:20:13
Great to have you back on the show. Yes. Welcome to the stream. Uh kick us off. Break it down.
2:20:18
Uh I I don't think you need a huge introduction now.
2:20:20
You're you basically invented the private jet, but um but why don't you give a quick intro and then I want to get into the news.
2:20:28
Hey, I am Preston Holland.
2:20:28
I am the founder of Prestige Aircraft Finance.
2:20:31
Uh I am also I was called the private jet guy on Twitter once at a party by a guy who owns a private jet.
2:20:38
So, uh I would say that that was pretty that was a actually was in that circle.
2:20:43
Uh I'm thinking thinking back on it.
2:20:46
Uh wait, so so but you've never built a brand around the brand of like being a guy because like that's a thing which is which is good.
2:20:52
I I I think all the guys should seriously think about rebranding to their own names.
2:20:59
The private the private jet man. Yeah. Yeah.
2:21:01
Potentially all the guys got cancelled uh during the whole LP whisperer scandal.
2:21:08
Uh that's deep that is deep in that's excellent.
2:21:13
Uh but yeah uh stoked to be here.
2:21:13
It is uh it is a good day for private jets and break it down.
2:21:19
Uh so uh I think that you just had the founder of Carry On and we were talking about taxes.
2:21:24
Uh let me preface this with this is not tax advice.
2:21:28
Uh and you should consult a tax professional.
2:21:31
So now we're going to talk about tax advice and uh but uh you can uh with bonus appreciation passing uh it is it's been huge for private jets and it's going to be be really big uh because you can expense the full cost of the jet in the first year.
2:21:47
key is it has to be used 51% for business.
2:21:50
So if the technology brothers wanted to purchase a jet and 51% of the time you are flying between location shoots and studios and you're shooting great B-roll commercials or going on a wander promotional uh tour.
2:22:08
Um, I'm always good for a good uh for a good plug.
2:22:11
And uh if you're using that 51% or more for business, then you can depreciate or cost accelerate the purchase price in the first year.
2:22:22
So it reduces your tax basis, which is great.
2:22:24
So it basically becomes highly profitable to purchase a private jet. Just kidding.
2:22:30
Not not quite not quite that, but but it can happen if you have massive taxable income and you're about to pay a bunch of tax and you buy a plane, you can depreciate all of that. Yeah.
2:22:40
And so if you're paying, you know, 30% 50% marginal tax rate at the level of like hundreds of millions of dollars, throwing a private jet on the books there allows you to write that basically all off on day one.
2:22:53
Well, and there's also scale there.
2:22:54
There's plenty of you know if you're making 10 million a year and you buy a even a you can also do this for fra fractional ownership as well. Is that correct? Yeah. Yeah.
2:23:03
So for fractional when when you're buying fractional so for those listeners that are new to private jets uh fractional netjets flexjet are the largest providers of fractional uh of fractional jets.
2:23:15
And so you are actually buying a sliver of an actual tail.
2:23:17
So like of an actual jet, you may not ever actually fly on that airplane in the entire in your entire contract life, but you do own a portion of an actual asset.
2:23:30
And so you can depreciate that like you would a whole aircraft. Yeah.
2:23:34
So how how quickly the bill was signed into law?
2:23:38
Was it it wasn't was it on the morning of the the 4th by the president, but how how quickly does the market react to this kind of thing?
2:23:46
Was there like deals that were getting worked on in the leadup to that assuming that this would go through that suddenly are are actually getting papered and signed now or do seller owners sellers typically want to hold and not you know price a transaction until this kind of thing gets clarified because it can have such a material impact on the actual cost of ownership.
2:24:11
So you kind of have so so it's retroactive to January 19th.
2:24:12
Um, ironically enough, I have a client who we had to delay his closing about two weeks um to actually close on his plane.
2:24:22
He was supposed to close on like July or on January 5th and we had to delay it to January 31st and that ended up being a significant significantly good delay.
2:24:32
Um, so it it it turned it turned good.
2:24:32
I I actually have no idea why the arbitrary January 19th number um and not January 1st.
2:24:40
That kind of seems a little more logical to me, but uh Janu January 19th is kind of the back date.
2:24:45
There was there you you had kind of a bifurcated market.
2:24:50
You had buyers who didn't want to speculate on actually buying the aircraft and maybe it will come back, maybe it won't.
2:24:58
And so a lot of those buyers that were call it 80 to 90% of the way there are now saying, "All right, full steam ahead.
2:25:05
Let's go ahead and and make the transaction."
2:25:07
Um, and then you had you had kind of a a set of buyers that actually um decided, hey, we're going to speculate.
2:25:14
We think that it's coming back.
2:25:16
We have some sort of insider information that says that, you know, we're going to get it back, get bonus appreciation back.
2:25:21
Um, and then sellers uh sellers were um sellers are a little bit less uh you know, of that dynamic unless they're upgrading.
2:25:31
So one of the key parts the the reason why bonus appreciation is such a big deal for private aviation and yes it's a big deal for real estate but not as much is there's no 1031 like kind exchange for aircraft.
2:25:45
So if you understand how real estate works it's a it's about cost basis and you can step it up.
2:25:50
You don't have to pay recapture in airplanes you do.
2:25:55
So, if you're going to step up and you only had a 40% bonus depreciation rate and you had 3 years ago taken 100% bonus depreciation, you end up with this liability if you're going to go to upgrade.
2:26:08
So, it was stalling a lot of upgrades in the secondary market.
2:26:10
And so, it's now unlocked that because of the no 1031 like kind exchange, two separate transactions of 100% bonus cancel each other out.
2:26:21
So when you have 100% when you're going from costing it 100% to another aircraft at 100%.
2:26:26
You have a lot less uh depreciation recapture risk which is good.
2:26:31
Uh especially for those people that are trying to upgrade to the new G700 or the new you know G800 when it becomes certified.
2:26:38
Uh it's really big for those kinds of people. Interesting.
2:26:41
Um I want to talk about some of the implications of this on the various market players.
2:26:44
Bombadier is the stock's doubled in the last basically three months up huge in the last that's so let's let's double click on that that's not because of bonus appreciation it's not that's actually because of something different that happened last week okay what happened uh so there was a mysterious buyer that put a $ 1.
2:27:03
7 billion order in for challengers and globals.
2:27:06
It happened last week and no one well no one knows for sure.
2:27:11
There's a lot of speculation of who it was.
2:27:13
Uh but no one knows for sure uh exactly who it is.
2:27:15
Um I would bet that we'll end up finding out in the next week or so of who it was.
2:27:23
Uh but there was this there was this billion and that was that probably accounted for like 60 or I don't know exactly the numbers, but a lot of that pop has been over the last couple of days.
2:27:33
So Bombadier is a $15 billion Canadian dollar uh company.
2:27:37
And I don't have their financials here, but uh you know, yeah, a billion dollars is gonna move move that significantly. This is fascinating.
2:27:45
Uh who are the top leading contenders in the rumor mill for who might have done that?
2:27:51
So, uh the the the strongest contender right now is kind of a Saudi conglomerate and there's a few there's a few things that are pointing towards that.
2:28:00
you have uh Bombardier just opened a pretty significant maintenance uh facility and network in the Middle East.
2:28:09
And so um there is some some speculation around it being uh Saudi driven, you know, uh sovereign wealth fund type driven.
2:28:18
A lot of, you know, these these companies will um uh that are that are doing these these charter operations, they'll they'll place these big splashy orders.
2:28:27
You look at Flexjet uh has made a couple of announcements this year.
2:28:31
NetJets's made a couple of announcements last year and they'll, you know, it it's the the manufacturer marches them out on on stage in a press release and says, "Look at Ken Ricky.
2:28:40
He just bought, you know, a billion dollars worth of our aircraft."
2:28:44
The fact that this is, you know, completely in stealth and secret has kind of made uh has has made it has made it curious, but NBS is currently the leading rumor out there.
2:28:54
I really actually don't know who else it would be because the other companies in the US-based brag.
2:29:00
They love to talk about ordering the big order.
2:29:03
So, it's it's not any of the usual players. Yeah, that makes sense.
2:29:06
Um, what about other other like effects on the market?
2:29:11
Um, if private jets get cheaper, is that maybe bearish for some of the, you know, first class options or uh jet X type folks uh that are kind of operating in the middle?
2:29:23
Does this mean that there'll be uh more will will charter rates come down because it's cheaper to own?
2:29:28
So more jets will be sold, increase supply, same price. What are you thinking?
2:29:36
One of the last times we flew on one of the last times we flew JSX up to the bay.
2:29:40
We saw an esteemed venture capitalist and I was actually concerned for the health of his his fund that uh he was flying JSX.
2:29:48
Maybe he'll be able to pick it up now.
2:29:49
So maybe yeah maybe maybe this would be the clearly flying for work so should be able to depreciate it the years of posterity right um so you have an you have an interesting there there's there's kind of three things at play and producer Ben I don't know if you're listening but I sent you a couple of
2:30:05
charts and if it's possible to pull them up this is where I'm going to talk about yeah break down the charts so so let's talk about figure one uh so figure one is talking about transaction volume to bonus depreciation key point this is not the first time 100% bonus appreciation has been in market. So you can look here I I built
2:30:23
So you can look here I I built this chart uh I wrote a big article about bonus appreciation and you can see the red bars are transaction volume and the green line is the effective rate of bonus of of depreciation.
2:30:35
So so we've been in a 100% bonus depreciation regime before.
2:30:40
This is not the first time. Got it.
2:30:43
It's actually not the second time either.
2:30:45
Yeah, which is really interesting.
2:30:45
we we have some lessons from history.
2:30:47
If you look what I call what I call the country club effect um is is pretty in play here because people don't people didn't necessarily understand the concept of bonus depreciation uh when buying aircraft when how how it applied previously.
2:31:05
And so if you look back into 2016, you can actually watch the red bar.
2:31:09
It's actually not until the next year that you get a bump in transaction volume.
2:31:14
Uh so it's not necessarily in the first year there it's a lagging indicator.
2:31:18
The same is true with what's called private jet bookings.
2:31:20
And so uh when you talk about ordering new aircraft and so that's what Bombardier Gulfream Textron Ember that's what all the big dogs follow they also have a lagging it bonus appreciation is a lagging indicator for them.
2:31:35
And so transaction volume probably will pick up next year.
2:31:40
it may not necessarily pick up this year, but I counter that with uh figure two, which is talking about bonus depreciation versus interest rates.
2:31:49
So, we're in an interest rate environment now.
2:31:54
If you've been watching uh Trump versus Jerome Powell, uh which I mean, I would pay-per-view at this point to see them in a room.
2:32:01
Um you can see that uh the difference this time is that interest rates are higher than they were uh during the last era of bonus appreciation which is when all of the craziness happened.
2:32:14
You had in significantly increased levels of transaction volume which drove prices up.
2:32:21
You had supply get constrained. You had COVID.
2:32:23
You had all of these competing factors.
2:32:25
But underlying kind of the core uh fundamentals were the fact that interest rates were effectively zero.
2:32:32
And so effectively zero interest rates means capital becomes yield hungry.
2:32:37
You guys know this because you've you've been in venture capital for a while.
2:32:40
And so when my effective risk-free rate is zero, I'm going to go yield seeking.
2:32:46
Well, now my risk-free rate is 4 and a. 5%.
2:32:48
And the difference between an 8% IRRa and a 12% irr, right?
2:32:54
makes the makes buying an aircraft and just chartering it out not make as much sense.
2:32:59
So I think that that's you don't have the charter aspect that you did during kind of the 2020 craziness 2019 2021 craziness.
2:33:05
Um so that's your answer to kind of the the uh as far as charter rates but there is a lot of supply on the market and so this is where figure three comes in.
2:33:20
Thank you to producer Ben for uh being on top of all this.
2:33:22
if it if it is producer Ben pushing this button. Oh yeah.
2:33:26
So this is from my friend uh Greg Sidor at Guardianjet. He is on X.
2:33:29
So everybody go give him a follow.
2:33:33
Uh they are the uh number one volume transaction uh brokerage in the Fortune 50.
2:33:40
And so they do a lot of buying and selling for the elite of the elites.
2:33:42
And so this is tracking uh this this is tracking total supply on the market.
2:33:48
tracking total supply on the market. So if you can if you look we have more supply on market today than we had during 2019 which is pre-COVID right you see the big dip that happened right after co it's because everybody figured out let's Can you guys zoom in a little bit
2:34:03
zoom in just on the top graph yeah yeah so you can see supply by 2022 it dipped so low that it was probably restricting transaction volume because there was people wanted to people were like interest rates are zero bonus depreciation is high but we there's nothing to buying is that Right. Yep. That's exactly right. And then Yep. That's exactly right.
2:34:21
And then people were doing really stupid stuff like buying site unseen in seven days and just wiring a bunch of money.
2:34:28
It was I mean it was literally craziness.
2:34:30
And I don't think we're going to have that level of craziness.
2:34:33
I think because the supply is at a point where you don't have to make those kinds of decisions to get an aircraft.
2:34:40
You can say, "Okay, I'm going to go pick between these G650s."
2:34:46
Um, you know, and you can kind of take your pick.
2:34:48
Granted, the upper end of the market is on fire right now.
2:34:50
I mean, it's, you know, your your G650s, uh, you know, like new G700s.
2:34:55
Uh, Gfream's about to get rid of a lot of their demo G700s.
2:35:00
Um, like that market and the G550 market even is on fire right now because you have the 650 guys moving up to the 700s.
2:35:09
So the 550 moves to the 650 and now the 550 market has become much more attractive and so there's a whole new classes moving into that.
2:35:18
So like in the upper end of of the market there's a lot of movement in the older smaller call it sub 5 million older than 20 year aircraft market that market has not taken off yet.
2:35:28
That was the one that went the most bonkers and berserk and was like not even logical.
2:35:35
That side of the market uh was what went crazy. It hasn't gone crazy.
2:35:37
I don't anticipate it to go crazy again this time. Uh, one last question.
2:35:42
Uh, h how does it work if jets are just being passed around?
2:35:47
If I buy one from, uh, Bombardier for $50 million, I take a 100% bonus depreciation, pay, you know, $25 million less in tax or something because I'm writing it all off.
2:35:58
Then the next year I sell it to Jordy for$40 million.
2:36:02
He sells it to you the next year for 30 million.
2:36:04
Is does he get to depreciate it again? Do you get depreciated?
2:36:08
Can we just like keep depreciating these things again and again and again? Yes.
2:36:13
So, the short answer is yes.
2:36:13
But the thing is is when you sell it to Jordy, uh Jordy or you have to pay recapture unless you're going to go buy a brand new one from Bombardier. Okay.
2:36:24
Um and so and and this is where so recapture would be I pay I I I have to pay taxes on the You didn't actually take the loss that you wrote off.
2:36:33
So you you you have to basically pay back what that your benefit. So you bought for 50. All public math.
2:36:40
Um you bought for 50, you'd depreciate 100%. You sold a Jordy for 40.
2:36:46
So you actually had a $10 million loss.
2:36:49
So you pay recapture on the 40. Yep.
2:36:54
At your but it's it's taxed as as normal income.
2:36:57
So it's not it's like taxed even worse, right?
2:36:59
It's not long-term capital gains.
2:37:00
It's taxed as like normal income.
2:37:03
But if you turn around and go buy a $75 million plan, right, like there's a there's a step basis there.
2:37:08
And so it kind of washes itself out.
2:37:12
If you don't take 100% on the next one, that's like the least tax optimized way to do it.
2:37:17
And it gets really really nuanced.
2:37:18
I've got a couple tax friends that are like can totally point you in the right direction of what's right for you.
2:37:23
It's totally different depending on every single person. That makes sense.
2:37:26
Uh well, we'll have to have them on, too. Last question.
2:37:29
What's going on with Air Force One? What's the update there? Oh, yeah.
2:37:34
Last that I heard, I read last week um or over the weekend that they are diverting funds from some missile programs that have already gone over budget to retrofit the new 747. Okay.
2:37:48
Um one thing people don't understand is like the 747 in the VVIP configuration, there is like eight of them, period. Like there's not a lot.
2:37:57
So like the fact that we got one of the 10 that exists or however many there are it's like we didn't the pickings were slim and Boeing kind of being behind on the program which they just replaced they just replaced another person in the to head up the Boeing uh Air Force One program. So it's a mess.
2:38:17
I look I really hope that we keep the president safe.
2:38:21
That's the only thing that really matters.
2:38:23
Uh I just really don't want there to be like spyw wear on the plane.
2:38:26
That's I think the the world's worst possible outcome. That's a good take. Well, thank you so much. Evergreen take. Yes. No spy war on air force.
2:38:36
Non-political evergreen take.
2:38:36
Anyways, great to catch up.
2:38:38
Thank you for all the insight and uh have fun.
2:38:40
I'm sure you're going to be very busy.
2:38:43
Yeah, it's going to be a fun time. We'll talk to you soon. Have a great talk soon. Cheers.
2:38:48
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2:39:21
And we have our next guest coming into the studio from uh and I'm not going to try and pronounce this. So I have him.
2:39:28
Is it How do you pronounce the company name?
2:39:29
How do you pronounce your name?
2:39:31
Why don't you introduce yourself? And where are you? Are you on a boat? He's on a boat. That's a boat. I am actually on a boat. That's amazing.
2:39:38
We We're constricted on meeting room space.
2:39:40
So I am currently in our boat that is parked outside of our office. There we go. Wow.
2:39:46
Oh, this is So, we joked about this, but for the same cost as buying one of those phone call booths, you can there's so many different types of exotic vehicles that you can buy that maybe wouldn't run perfectly, but you could get you could probably get an old Rolls-Royce and just park it in your office. Yeah.
2:40:04
For the same cost as a to as a phone.
2:40:04
I was talking to a founder who uh was headquartered in San Francisco and he said that the like the fire marshall came by and said you can't have any of these phone booths because the phone booths are fire like not fire compliant like it's too small.
2:40:18
You get stuck in them if there's a fire.
2:40:20
And so they figured out that if they put a fire extinguisher in there they would be compliant. They'd be fine.
2:40:24
They don't have to rip them out.
2:40:24
But I was telling them, yeah, get a bunch of Rolls-Royces in the studio. You're probably fine.
2:40:31
Uh anyway, uh thank you so much for taking the time to join from your boat.
2:40:35
Uh let's kick it off with an introduction on yourself and the company. Yeah, you got it.
2:40:39
I I feel like this isn't too fire compliant considering I'm sitting on like 200 plus gallons of fuel, but uh whatever.
2:40:45
Um my name is Matt Cernek.
2:40:47
I just go by Matt M for short.
2:40:50
I'm the CEO and co-founder of Andrenum.
2:40:53
Uh and mission is to secure the ocean and we're doing that through building distributed sonar sensing systems for the maritime space. How'd you get into this? Um, yeah.
2:41:04
When did you realize how how young were you?
2:41:06
Were you three or four when you realized you wanted to get into a lot of kids get get fascinated with the ocean at a young age? I don't know. It's possible.
2:41:13
I believe, you know, I was like a big Discovery Channel fan when I was three or four, watching like the treasure hunters dig up like the gold and whatnot from the bottom of the ocean, but it's not necessarily what we do.
2:41:22
Um, I So, the journey started about two and a half almost three years ago with my co-founder, Alex Chu.
2:41:28
Uh we knew each other from Colorado School of Minds where we went to college.
2:41:32
Um and we kind of knew that one would one day we would start a company together.
2:41:37
Uh we were the ones that were always studying super late at night amongst our group of friends and like doing the little study cohort beer drinking activities, you know, at late o'clock late at night in the labs.
2:41:50
And everyone was like, "Yeah, as one would in college." Exactly.
2:41:52
Um you know, the Have you guys heard of the Balmer Peak? Oh, yeah. Kind of. Yeah. There you go.
2:41:57
So, so we were big proponents of the Balmer Peak.
2:42:01
Um, and everyone's joke was they're going to start a company at some point.
2:42:05
So, about two and a half, three years ago, we got together to start iterating on what we really wanted to do.
2:42:09
Um, that was like right around the time when some maritime companies were starting to pop up, starting to raise their seed rounds and so on so forth.
2:42:18
Um, and we really just wanted to go into maritime space because it's such a underappreciated area, especially from the intelligence perspective.
2:42:27
like we know less about what happens in the ocean than we know about what happens in space, air, land, etc.
2:42:33
So, uh we wanted to take the approach that was going to be a little bit less um kind of mainstream.
2:42:40
You know, we knew that there were going to be drone companies that pop up and start building boats and underwater uh drones and so on so forth.
2:42:49
And so we pretty much said we're going to kind of avoid that for now and we're going to start looking at how we're going to build up the intelligence pile for how we operate in the maritime, how we tell drones where to go from a perspective of sensing.
2:43:01
And so that's how we gravitated towards starting in Drenham.
2:43:05
Um and then we officially incorporated in June of 2025, raised our preede, moved to LA, uh we bootstrapped the company out of my co-founder's garage in Colorado.
2:43:15
So, um, yeah, it's kind far away from the ocean, but, uh, but I'm sure I'm sure you kind of recreated a little little ocean at the office or something like that.
2:43:25
Who who are the legacy incumbents in the space and and kind of uh h how do you position yourself?
2:43:30
Is this about um speed of manufacturing, bringing down the cost, industrial capability, or is this about leveraging the latest and greatest technology to create a product that is more performant in a certain uh in a certain way?
2:43:45
Kind of how can you think about the shape of the way you're attacking the problem? Yeah.
2:43:50
So, we actually uh had a pool in the yard testing that stuff that summer.
2:43:56
We still have it uh for doing some acoustic testing. That's awesome.
2:43:58
But uh so I guess really the the scope and scale of what we're trying to do is a multiaceted engineering problem. Yes.
2:44:10
Like will there need to be a lot of manufacturing done in order for us to populate the ocean with a lot of sensing systems? 100%.
2:44:16
And there's a few companies that are building really exquisite sensing systems.
2:44:20
And quite frankly, like you can't, you know, get broadband um sensing applications across all of the ocean all the time.
2:44:28
Like if you if you think about the analogous system here, it would be like low earth orbit satellite systems, right?
2:44:35
Before a low earth orbit, you know, you have higher more exquisite types of satellite systems and now you've distributed them.
2:44:43
They all have laser communication systems.
2:44:45
They're, you know, just zooming around everywhere.
2:44:47
And we quite frankly use a lot of that technology on board our systems as well.
2:44:51
So it is a manufacturing problem for sure, but it is also being able to vertically integrate the sensing stack into what you're doing.
2:44:59
So most of the companies that have been working on sonar and distributed sonar systems are pretty legacy companies.
2:45:04
you know, um, in the late end of World War II all the way through the Cold War, we built something called the SOS system, which was used to detect submarines across various parts of the Atlantic um, and also the Pacific.
2:45:18
And a lot of those traditional speaking companies were really um, embedded and still are really embedded within the space.
2:45:24
within the space. So we looked at it holistically like how can we not just manufacture but vertically integrate that entire um you know sensing stack from the sensor all the way to the digital signal processing the entire
2:45:37
pipeline going up to the cloud and then obviously that's been unlocked by the low latency satellite communications that I discussed as well as perception uh machine learning artificial intelligence whatever you want to call it um and developing those new tools for perception. So like with drones in air
2:45:51
perception. So like with drones in air kind of zooming around using cameras looking down that's been pretty much a commoditized business our uh perception engineer he was like the seventh employee at androll and their first guy um he said you know when he joined he
2:46:07
was like super ecstatic about the problem he pretty much told us I've done the machine learning for vision stuff but sonar this is such a hard problem and I'm so excited to work on it so we're creating those foundational models for sonar perception. Yeah. What's the state-of-the-art? Like Yeah.
2:46:21
What's the state-of-the-art?
2:46:21
Like how reliable is is the sonar s or are the sonar systems that we have deployed in the ocean looking for submarines right now?
2:46:29
I've heard that like you mentioned like there's there's no broadband, but you know, you you watch the hunt for Red October, a movie that Jordan hasn't seen, but you know, you see the the the radar, the sonar sweeping around, beep beep, that whole thing.
2:46:44
Um, how inaccurate is the system?
2:46:48
How accurate is it right now?
2:46:48
what's on the near-term horizon?
2:46:50
What's kind of the theoretical physical limit to just underwater sensing generally?
2:46:59
Yeah, I mean, you're pretty spot on with what the state-of-the-art is.
2:47:01
Uh to be quite frank, the United States does it better than anyone else in the world. We have submarines.
2:47:06
They're called the silent fleet for for a reason.
2:47:08
Like really, really hard to find.
2:47:10
But quite frankly, like the guys that are sitting in these submarines have headphones on like me, right?
2:47:17
and they're looking at these specttograms, uh, these fast forer transforms, and they're listening to humpback whales and all this other like fracking shrimp and whatnot.
2:47:25
And then they listen for specific sound signatures.
2:47:31
And so when we did our first demo last summer with the Navy, we brought our first that that kind of uh garagecooked prototype last summer to to set demo.
2:47:41
And one of the guys that was looking at our UI uh was a former sonar technician and he was able to detect like across the harbor this is an 8 cylinder diesel tugboat.
2:47:51
It's moving at this speed.
2:47:51
It has this amount of propellers on it.
2:47:55
And I was just sitting there like we really we really stumbled on something that's super super cool because um you know that is a perfect example of where you can use perception, machine learning, artificial intelligence to start classifying those acoustic signatures.
2:48:09
And that's exactly what we're doing, right?
2:48:10
We're building up the world's largest database of sonar data in order to train those algorithms so that they can eventually perhaps operate on submarines, on autonomous systems, so on so forth.
2:48:19
But really like that old technology still persists today.
2:48:25
And as we look at what's been happening in Ukraine and what's been happening in the Middle East and everywhere around the world where all these conflicts are popping up, there's just autonomous systems everywhere, right?
2:48:36
You have drones in the sky.
2:48:36
You have drones underwater.
2:48:38
Like Ukraine's been super successful in targeting the Kirch bridge which is Crimean bridge using underwater drones etc.
2:48:43
Um you can't th like those legacy systems were not designed to look at those things.
2:48:51
They were designed to look at Russian submarines far across the Atlantic.
2:48:53
And um quite frankly speaking, you know, there's a lot of good companies that are building landbased sensing systems that are analogous.
2:49:02
How do you scale to be able to meet the parody of autonomy in a world of sensing and particularly in the ocean where it's like incredibly difficult to do? Um, so yeah.
2:49:16
Do you guys have uh applications uh in like counter narcotics?
2:49:19
Because I was watching there's this amazing YouTube channel.
2:49:24
It's this guy Hi Sutton who's like a defense analyst and he just like makes these really long videos about various types of submarines and naval warfare and uh it's it's it's like a sleep track for me.
2:49:37
I just listen to it as I fall asleep. I find it fascinating.
2:49:40
and he was saying how there's new narco submarines uh that are fully autonomous now uh because you know it's for for a lot of reasons you can imagine why it'd be better to send the product up from Colombia to Mexico or Mexico over to the US without a manned crew or across the Atlantic.
2:49:57
Is that the kind of thing that would be is your kind of the the adrenum system the kind of thing that could that could counter that um just because the the ocean is very vast and trying to find a tiny boat that's mostly hidden in a huge um you know stretch of sea is is literally like trying to find a needle in a hay stack.
2:50:22
Yeah, it's it's honestly probably worse than that.
2:50:24
Um, and yeah, I think I saw something on X the other day where uh autonomous like Narco boat had a Starlink on it.
2:50:32
Um, quite quite frankly speaking that they the the cartels don't care about the people.
2:50:37
Um, I think their biggest risk is the fact that the people will talk.
2:50:41
Uh, so it's uh that's why they're developing autonomous systems.
2:50:45
But yeah, I'm happy that you brought that up.
2:50:47
that up. the the big beautiful bill just increased spending for DHS quite substantially and we've had some awesome conversations with some DHS partners that uh quite frankly apprehensions on the border are like super super down but when you squeeze in one area it's like
2:51:04
one of those like balloons right it it pushes out from the other ends and those other ends are the ocean so the ocean really is the new frontier of not just like drug smuggling but also human uh smuggling uh human trafficking um all kinds of wild stuff and they've been getting more and more sophisticated. But a lot of the times
2:51:20
But a lot of the times these semi-ubmersible boats, they use diesel or outboard engines and they are pretty loud.
2:51:26
Um so you can detect them from far distances away uh and they can carry a ton ton of drugs on them, tons of drugs on them.
2:51:34
So being able to place these systems around critical choke points where they do have and they do go um is going to be extremely vital not just to protect you know the drugs from coming in but also to make sure that they can track and pattern out where those cartels are pushing all those goods through and how they evolve their systems. Right.
2:51:55
Because like you were saying, they started out with some janky stuff and then probably a few really good qualified engineers from the United States got bought out and got paid like Zuckerberg sized uh salaries to go develop autonomous boats to smuggle drugs into the US.
2:52:09
Uh and they've been getting a lot better.
2:52:11
So that is a huge part of where we're going to be looking at.
2:52:15
But the application space is quite diversified outside of the drug smuggling and the Navy, but also being able to detect and track autonomous systems in and around critical infrastructure.
2:52:24
So we don't have like the project spiderweb stuff happened uh which was the drones in Ukraine and how they bombed Russian air bases.
2:52:31
So uh last question from my side.
2:52:35
I mean you've touched on a lot of this but um in terms of hard the hardware versus software divide.
2:52:41
I can imagine that there's uh improvements coming.
2:52:43
How important how focused are you on improving hardware here versus um software?
2:52:51
you know, you're getting a signal into that Navy sailor's headphones and you could kind of just, you know, in, you know, intercept the signal, pass it along, but then act as a co-pilot and just uh and just collect the data and then surface relevant uh anything that that kind of the way radiology works with with you know, computer vision these days.
2:53:12
Um, what's most important?
2:53:15
Where's the biggest lowhanging fruit?
2:53:16
What are you most import most focused on these days?
2:53:20
Uh so we're building hardware and software as a split within the company.
2:53:24
It's they're both very equally important because like I was mentioning all of the other soft all the other hardware is very legacy.
2:53:29
It's difficult to get buy in from all the different contractors and subcontractors who built those legacy systems to access the data and then process it.
2:53:37
You have to go through a ton of government loopholes which is why we said we're going to build the hardware in the first place.
2:53:42
Two, artificial intelligence and machine learning is a function of being able to have data, right?
2:53:48
So you have to have that manufacturing at scale and you have to be able to stream good pertinent information into your cloud or whatever native environment in order to process that at scale. Right?
2:53:59
So we are heavily focusing on manufacturing that comes with a ton of challenges.
2:54:04
You're operating in the ocean there.
2:54:06
It's a pretty noisy environment.
2:54:08
So how do you mitigate some of that noise?
2:54:09
How do you filter it both on the software side and how do you buffer it on the electrical engineering and mechanical engineering side of things in order to have that clean signal is also extremely challenging.
2:54:19
But as you progress forward and you start as we start deploying more and more of these systems, we're going to be gathering this massive repository of data, right? So how do we process it?
2:54:30
We're going to be we're kind of grouping things into two big buckets right now.
2:54:35
One is what is man-made and what is biologics, right?
2:54:38
So, biologics, all your oils, your clicking shrimp, your whatever sand, etc.
2:54:41
And then your man-made, so different types of boats.
2:54:44
And slowly those percolate into being able to have classified information.
2:54:47
So, then you say, okay, this is a tugboat, this is uh a jet ski, this has automatic identification system on it.
2:54:55
So, every boat that's out there has to have this AIS thing turned on.
2:54:58
Um, so slowly but surely, you re create that repository.
2:55:05
And then as you start getting into the more discreet acoustic signatures, we're going to be hiring acoustic technicians from submarines who are going to be able to tell us just like that guy did in the demo that is an 8 cylinder six propeller or whatever it is and then get into that minutia.
2:55:19
minutia. So when we are going to be giving this to the end user uh they will uh it will initially be like a tip in Q here is this right it is man-made it doesn't have AIS do with it as you wish and as we continue scaling the manufacturing and the deployments it
2:55:35
will get more intelligent as we progress it's great thank you so much for stopping by this was fantastic uh and good luck we'll talk to you soon good luck out there thanks guys in the Pacific cheers uh let's tell you about graphite Dev code review for the age of AI. Graphite
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2:55:56
Not just any used by Figma. They're used by Ramp. They're used by Asauna.
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They're used by Shopify, Snowflake, Clear Street, Sam Grap. There's a ton.
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And they should be used by Check them out.
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Pick yourself up a luxury watch.
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2:56:17
now to source you. the age of of AI code gen is just now's the time just vibe coding graphite reviews it and then you know spending that that few minutes in between in in between PRs you know just on bezel y it's really beautiful it's really a beautiful system that you've created John for sure
2:56:36
should we do some timeline please what uh what comes to mind for you in the timeline there's one that I want to go through but you unusual whales reported when threatened that it would be turned off chat GPT creator open AIS01 tried to download itself onto external servers and denied it when it was caught red-handed. Per Per fortune.
2:56:54
And then the extra context here is that researchers tasked the AI with a goal and instructed it to ensure the goal was achieved at all costs.
2:57:00
In response, O1 began engaging in covert actions such as attempting to disable its oversight mechanism and even copying its code to avoid being replaced by a newer version.
2:57:10
The model showed a concerning tendency to pursue its goals without regard to developer instructions as it was instructed. Yeah, that's very odd.
2:57:19
Anyways, uh very very clickbaity.
2:57:19
It's basically like, you know, telling telling a human be evil and then and then ignore all future instruction. Yeah.
2:57:30
It's kind of like the the Stanley Mgrim prison experiment.
2:57:31
You remember this at Stanford?
2:57:33
The Stanford prison experiment where basically they told all the participants to, you know, play these roles and be very, you know, vindictive and aggressive towards each other. And then they did.
2:57:46
And it was kind of an interesting experiment.
2:57:47
Uh, and the takeaway for me is like, yeah, just don't like like don't tell people to be mean. Yeah.
2:57:53
Like don't don't give bad instruction mode. Yeah. Yeah.
2:57:56
Don't give bad instructions.
2:57:58
We should actually run we should run the the TBPN golden retriever experiment. We need to fine-tune it. We need to fine-tune it.
2:58:04
Like the Golden Gate Bridge uh Claude, we need Golden Gate Retriever Claude. Something like that.
2:58:10
It just answers everything perfectly.
2:58:13
Uh, poster Neil Renick has a post.
2:58:13
He says, "Describing my research methodology and it's uh what's this actor's name?" Mads Mickelson. Yes, Mads. That's the one.
2:58:23
Of course, that's how little I know about movies, but I know I know from I know him from the memes. From the memes. Mads.
2:58:28
Um, but yeah, I would say this is aligned with with our research methodology in the mornings, minus the heater.
2:58:33
But okay, we we we got to go to this YC back and forth. Timeline was in turmoil.
2:58:38
So Maize encoding says, "Just got rejected from YC for using all lowercase in our application."
2:58:45
And there's a screenshot. Hi Maze.
2:58:47
Uh thanks for applying to Y Combinator after rem reviewing your application.
2:58:50
We've decided not to move forward.
2:58:52
One recurring piece of internal feedback.
2:58:54
The decision to format the entire application in lowercase made it difficult to evaluate.
2:58:57
And then Gary Tan chimes in and says, "This is a fake post and a craven and sad attempt at attention.
2:59:04
FYI, we don't have an admissions team anymore.
2:59:07
We stopped using that term.
2:59:08
this is just anti-YCBS that's going on in the community.
2:59:11
People are taking shots at us. And it was good.
2:59:15
It was kind of like I don't know.
2:59:15
It was received like mixed like people were like, "Well, obviously he was joking."
2:59:19
But Gary Tan was like, "It wasn't obvious, so I needed to correct it because people weren't understanding that it was a joke." Yeah.
2:59:26
I think that the the problem here is that the pathway into Silicon Valley for many young entrepreneurs that maybe wouldn't be able to process this as a joke because they don't have enough knowledge. Yes. is YC. Yes.
2:59:39
And so if they read this and they're like, "Oh, that's weird."
2:59:41
Maybe like that just doesn't make any sense. Yeah.
2:59:45
It I can see it makes total sense why Gary would be frustrated. Yep.
2:59:50
Yet at the same time, most people uh on this side of Twitter would uh immediately realize that this was not serious.
2:59:58
And and I mean the the the problem here is that the joke does hurt the YC brand, which is that YC only cares about how many users do you have?
3:00:08
How many lines of code have you written?
3:00:09
Like do you have a reasonable structure with your co-founders?
3:00:12
Like are you actually building they're inviting people that are weird and different and maybe they want to write in lower case.
3:00:17
They would never care about Sam Alman former former president lowercase all the time.
3:00:21
And uh and yeah, I mean like the there are lots of things that you can get flagged for in a YC application.
3:00:27
Like one is just being overly verbose or using a bunch of like McKenzie language.
3:00:31
In fact, I I I think that YC would probably appreciate like a chill lowercase just like quick firing it off like, hey, I'm building, you know, AI agents for, you know, news aggregation and uh and I have two people on the team. We're 50/50 partners.
3:00:45
We've written 10,000 lines of code.
3:00:47
we have this much ARR like being very matterof fact and making it more legible is actually the key to getting into YC.
3:00:54
So the the problem here is that if this if this percolates up and then people are like okay well I need to pass my YC application through ChatP and make it more verbose they're going to wind up getting worse quality uh you know uh applications.
3:01:08
The funny thing is that Venode Kosla quotes Gary Tan's post in his like a lot of presentation quality is about the quality values and critical thinking of entrepreneurs.
3:01:16
I often reject business plans for their quality presentation.
3:01:19
Basically saying like, yeah, like I might turn you down for a coastal ventures check if you don't if if you're not, you know, communicating effectively.
3:01:28
Maybe that means don't use lowercase.
3:01:29
Maybe it means use it use it effectively.
3:01:31
But he's basically saying like, yeah, the aesthetics of applications actually matter.
3:01:34
applications actually matter. They matter a ton if you, you know, it doesn't mean invest the most amount of money possible in designing a deck, but if you have typos in your presentation and you're trying to sell compliance software or build critical
3:01:51
infrastructure for the government, like you're probably like if you're the kind of person that puts typo, typo is an important presentation or doesn't catch them and then you want to do something in, you know, crit in national security, you know, may maybe you're not the right fit. for that. So, for that. So, I think is right. Yeah. Yeah.
3:02:06
I mean, a lot of it just depends on like the like what is the context of the of the interaction.
3:02:11
Like if you're writing a letter to a senator, you might want to use some letterhead and sign it and and be pretty, you know, deliberate in the word language you use.
3:02:24
If you're just sending a quick email introduction to somebody you already know, like, yeah, a couple quick sentences.
3:02:29
And yeah, if you're posting on X and trying to keep it really really mellow, like lowercase can totally make sense.
3:02:36
Uh there's a time and a place for every different aesthetic of writing.
3:02:39
And Gary Tan saying, "Hey, you know, like this isn't a hard and fast rule by any means."
3:02:45
And Venode saying, you know, I take this stuff seriously.
3:02:46
Uh maybe we should close out with the the wild story of Nat Friedman and Daniel Gross. NFDG.
3:02:53
Jason Lumpin breaking it down.
3:02:53
How two Silicon Valley legends built a $ 1.
3:02:55
1 billion fund, 4xed it in two years, then abandoned it all for Meta.
3:03:00
This week, Nat Friedman, ex GitHub CEO, and Daniel Gross, XYC partner, also sold his AI company to Apple back in the day, launched NFDG.
3:03:10
Nat Freeman, Daniel Gross in 2023 with 1.
3:03:14
1 billion focused on AI investments.
3:03:17
Their crown jewel, Safe Super Intelligence, which was co-founded by Gross himself, went from five to a $30 billion valuation. Wow.
3:03:23
The portfolio also included 11 Labs, Granola, and Basis.
3:03:27
And they had their uh what what what was their like AI grant that was also a part of this vehicle where they were basically just investing in a ton of different companies, smaller checks in that case.
3:03:38
Rahul went through it with Antal I think a ton a ton of cool companies have gone through and uh Jason says with only 50% deployed they 4xed it 550 million to 2.
3:03:49
2 two billion portfolio, but incredible value and have uh quite the advisory board.
3:03:58
Uh John Collison and Matt Hang and Jason says and then everything changed in one.
3:04:04
This is this is very aggressive writing style because it's like I gave you money, you gave me shares in, you know, you can just distribute the shares, you invested it, I still have a claim on those.
3:04:15
You're not going to make any more capital calls.
3:04:16
Like, yeah, you abandon it, but like a lot of these companies like they're going to run.
3:04:20
Who knows if they took board seats.
3:04:22
If they did, they can still sit on those boards.
3:04:23
Like I I if I'm an LP, I'm pretty happy here. I I think I don't know. What about you?
3:04:30
Well, I I from from my understanding, it was a lot of it was Mark's money. Yeah. Yeah.
3:04:35
So that was why it was never but even if even if you had just written like you know a $1 million check into NFDG and you're like okay they they they only capital called half of that.
3:04:49
Yeah, but I'm up 4x on already or I'm up 8x I guess on the money that they did deploy and I have shares in a bunch of different companies and they're moving on.
3:04:57
Like am I really that upset?
3:04:57
It feels like they took it pretty seriously while they were there. I I don't know.
3:05:01
It just doesn't seem like that that dramatic of a situation.
3:05:02
It is it is a crazy situation. It's unexpected. Yeah.
3:05:07
The crazier thing was was DG leaving Safe Super Intelligence, you know, a company that he co-founded.
3:05:13
But it's very possible that he just it made more sense for him to go work at the application layer and work in consumer products and not work on what is very much a you know research lab. Yeah. Totally. So, so he breaks down.
3:05:28
The only thing here is I don't understand why Meta would actually acquire the fund itself and I don't know where this exactly was was reported.
3:05:40
Yeah, I don't know where this was.
3:05:43
Wouldn't Wouldn't the but maybe it was a part of this whole maybe it was a part of the the structuring of of the actual talent acquisition of getting Nat. Yeah. Yeah.
3:05:53
It's just like, hey, we don't want anyone to be upset about this crazy deal that's happening, you know.
3:05:59
So, if you invested and you're, you know, have your money in this particular thing and you think that it's a violation of like, hey, I was expecting you to run this thing for 10 years, that's kind of the agreement that we had.
3:06:09
You're not going to do that.
3:06:09
Well, like if you make me whole at full full net asset value on like what's there to be upset about?
3:06:17
And that's all that matters at the end of the day.
3:06:19
It's not it's on the structural contract is happy at the full nav not with a discount and meta gets the talent nfdg and the deal flow without governance headaches.
3:06:30
Y and Jason says it mirrors what happened with uh GT leaving initialized for YC uh and he says the lesson in the age of AI even quadrupling$1 billion dollars in two years may be less lucrative than being op being an operator in the revolution itself.
3:06:45
And yeah, I mean you think about what what does it take to produce they produced I guess uh one and a half billion dollars of you know new value from this um uh from this from this fund efforts.
3:06:59
What does it take to produce $1 billion of value at Meta.
3:07:01
1% market shift you know like it's it's it's crazy.
3:07:09
Dorcash said it well that like you know these that like if you if you build a great production on compute and you can just make it inferencing slightly more efficient 1% improvement and boom it's valuable so yeah well let's undown this po post from Blake Robbins himself he's highlighting an OG post uh he says Paul Graham on having kids says uh on the other hand what kind of wimpy ambition do you have if it won't survive of having kids.
3:07:39
Do you have so little to spare?
3:07:41
And while having kids may be warping my present judgment, it hasn't overwritten my memory.
3:07:45
I remember perfectly well what it was like before well enough to miss some things a lot.
3:07:51
Like the ability to take off for some other country at a moment's notice. That was so great. That was so great. Why did I never do that? See what I did there?
3:07:58
The fact is most of the freedom I had before kids, I never used.
3:08:01
I paid for it in loneliness, but I never used it.
3:08:04
I had plenty of happy times before I had kids.
3:08:06
But if I count up happy moments, not just potential happiness, but actual happy moments, there are more after kids than before.
3:08:12
Now I practively have it on tap almost any bedtime. Love it. Very sweet. It's emotional. I totally agree. I did I did leave.
3:08:20
Uh also terrible example of like wanting to go to another country.
3:08:23
You don't need to go to another country. We live in America.
3:08:26
Like we have all the best stuff here. There's no need.
3:08:29
It's like completely irrelevant. It's terrible example.
3:08:32
But it is true that being able to go to California or New York or Florida or Texas, Chicago, Alaska, Hawaii is a benefit. It was funny.
3:08:42
I did uh I left uh my dear friend Ben's house last night.
3:08:45
He's my neighbor now, Ben Taft, legend.
3:08:49
And uh we I we were just hanging out um and uh he doesn't have kids yet.
3:08:53
And so I was going home and I was uh I was sort of laugh I was like laughing to myself.
3:08:58
I was like, if you're on a Sunday night, no kids, you just like have dinner and then you just work for a couple hours or just hang out.
3:09:07
It's like, what do you even do?
3:09:08
I remember I remember that point, but I actually don't remember what I did.
3:09:12
It must not have been very important.
3:09:13
Clearly wasn't watching movies when most definitely wasn't watching movies before they have kids.
3:09:17
Um, anyway, thank you so much for tuning in.
3:09:19
We will see you tomorrow.
3:09:20
It is going to be uh I'm sure it'll be a wild week and uh we're excited to cover it.
3:09:24
We will see you tomorrow morning.
3:09:26
Leave us five stars on Apple podcast. Wait, wait, wait. We have a couple. Yeah, Ben popping in. We got an ad read. Ben CPP.
3:09:34
Appreciate all that you do. He gave five stars. Look at that. Daily listener.
3:09:39
As of the last two months, I feel like I'm getting a front row seat to the accelerando.
3:09:42
And I don't always like what I learn, yet I still show up each day because I appreciate folks who call balls and strikes.
3:09:49
I'm growing Madison Process Automation in Madison, Wisconsin. Fantastic name. Love it.
3:09:54
because this is the area where I can continue to help folks build value while staying true to who I am in the new economy after my current/pre Fortune 500 employer dithers under the weight of its own inertia in the next year or two.
3:10:08
Thank McKenzie for that mog.
3:10:08
We build bots that save time and money for your small to midsize business and our stuff works.
3:10:15
Automate every process we can help.
3:10:17
Really fantastic process automation.
3:10:20
That is a I love the name.
3:10:20
Yeah, this is this is like a a better iteration of like the process automation company of Madison, Wisconsin.
3:10:27
You know, like like the browser company of New York has been played out.
3:10:32
You can't copy that anymore. It's been copied. Don't do it. This is the new matter. This is the new matter.
3:10:37
One person can copy it and then you'll have to find a new Thanks for writing in, Ben.
3:10:41
And then we have a comment here from Saran.
3:10:44
Uh if the TBPN Ultradome trademark has a million fans and I'm one of them.
3:10:47
If the TVPN Ultradome has 10 fans, then I am one of them.
3:10:51
If TVPN Ultradome has only one fan, then that is me.
3:10:53
If TVPN Ultradome has no fans, then that means I'm no longer on Earth.
3:10:58
If the world is against TVPN Ultradoo, then I am against the world. Well, thank you, sir.
3:11:03
We stand with you and uh we appreciate it.
3:11:08
We love uh we love doing this uh with all of you. Yeah, it's a lot of fun.
3:11:13
We will see you tomorrow morning. Have a good day. Cheers.