All-In with Chamath, Jason, Sacks & Friedberg
Debt Spiral or NEW Golden Age? Super Bowl Insider Trading, Booming Token Budgets, Ferrari's New EV
13 Feb 2026
Chapter 1: What recent AI updates are impacting token budgets and salaries?
All right, everybody, welcome back to the number one podcast in the world, the all in podcast with me again, the core for the original quartet, David Sachs, David Friedberg, Chamath Palihapitiya, I'm Jason Calacanis, and we have a very full docket today. All right, topic one, gentlemen, AI acceleration, it was a big week for AI.
New study published on Monday, February 9, in the HBR Harvard Business Review, suggesting that AI tools intensify work, but do not reduce it to UC Berkeley researchers spent eight months embedded at a 200 person tech company. So this is one company's experience.
What they found, employees who use AI worked at a faster pace, took a broader scope of tasks, and extended work into more hours of the day. Workers reported feeling more productive, but they also felt a little more stress and burnout. Sax, your hot take here, your quick take on this study. Obviously, it's just one company, but it does track, I think, some of my experiences.
All right, well, a few points here. Number one... As you may recall on the prediction show for this year, my most contrarian belief is that AI would increase demand for knowledge workers, not put them out of business. And I think you see in this UC Berkeley study, the reason why that might be the case is because the employees who use these tools, like you said, they work faster.
They took on a broader scope of tasks. They actually ended up working more hours in the day. So they did more work, not less, and even more effort rather than less, not because they were required to, but just because they were more motivated. And I think they were more motivated because their work was getting up-leveled, right? They're kind of able to offload
more menial tasks to AI and it made their work more purposeful and meaningful. So I think we're kind of moving from what some people, I think maybe Jensen has called task-based jobs to purpose-based jobs.
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Chapter 2: How is insider trading being addressed in Super Bowl prediction markets?
And I think a key skill of employees is going to be the ability to structure work for themselves and their AI agents. And the employees who can do that are going to be far more productive than those who can't.
That kind of brings me to point number two, which is that I think there's a tremendous opportunity this year for employees who are early adopters of these tools or so-called AI natives to demonstrate their value to their employers. They're going to be able to get a lot more done. They're going to appear to have superpowers.
They're going to be the people in meetings who can take an assignment that would have taken days before and get it done in two hours, whether it's a presentation or a spreadsheet. People are going to be shocked at how quickly they can get these things done because they're going to be facile at working with AI. So I think there's a big opportunity there.
And there was an article that went viral this week by Matt Schumer called Something Big is Happening, where he talked about this career opportunity that's going to be available to kind of AI early adopters.
Chapter 3: What insights were shared about the All-In Liquidity investor conference?
And I think that brings me to my third point, which is I think that you're going to see massive enterprise adoption of AI, not just chatbots, but agents this year. But I think it's gonna be driven by the bottom up. It's gonna be driven by these early adopter employees coming in to their workplaces, bringing in these kind of consumerized AI tools, start using them at work.
Chapter 4: What does the CBO report say about the US economy's future?
as opposed to top-down initiatives. I think there's a lot of top-down company transformation initiatives that are happening in large enterprises where the CEO has tasked a team with figuring out how to use AI, how to transform their business with AI. Those initiatives are going to take months. They're going to be studying what tools they should use. They're going to be doing RFPs.
And I think it's ultimately going to be very slow. And while those things are trudging along, I think there's going to be these early adopter employees who just make the transformation a fait accompli by, again, bringing these tools into the workplace from the bottom up.
So I think in the same way that you saw consumerized SaaS tools spread from the bottom up in enterprises, I think you're going to see consumerized AI tools spread from the bottom up in enterprises. And I think it'll ultimately be one of the big themes this year.
couldn't concur or agree more. Nick, throw up that tweet I did. I did a tweet and it got 2 million views. Basically, I said, listen, if you got laid off by Amazon or Microsoft over the last two years, just wear an open claw and automate your previous job. Show you know how to use these tools. Go back to your boss and say, hey, I want to come back and automate everything or go to startups.
Every startup I know is hiring for this position, which is somebody who knows how to build and manage agents.
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Chapter 5: How is the state of the economy affecting US jobs currently?
There is no job rec for this yet or a title. We should come up with what this person does. But it used to be called prompt engineer. It's no longer just prompt engineering. It's managing and educating and offloading work to an agent and then making sure they're actually doing it. And right now, it feels like the people in my organization have four of them who are focused on this out of 20.
I would say that their leverage is between 10 and 20x the other 16. So now I'm going down the slope of employees from, you know, most technical to you know, to lease, and trying to get each one of them to adopt and create an agent for them will probably take six months. But when we do, I think our leverage versus a competing firm is going to be 10x.
As an example, in the podcasting space acts, we now have it going through podcasts, looking for the best moments, or you can just give it a moment. And it will clip the clip for you and put it in the Google Drive. So imagine we're all in, I don't know, our little group chat and you said, oh, from the last episode, can you get me a clip of minute three to minute six?
And then it's just on your iPhone. It's just in the group chat.
Chapter 6: What are the details of Ferrari's new fully electric car?
Boom. Nobody has to go find it. It just clips it. That's the kind of work it's doing. And then we have it looking at our YouTube stats. We have it looking at our Instagram or TikTok stats, and then trying to tell us which clips are going the most viral, which ones have the most comments, and then giving us strategies to how to make them go more viral.
It's really weird because it's coming up with really great suggestions and eliminating all the reporting work that knowledge workers do. Shamath, do you have a take on this? I know you've deployed the software factory, which is, I think, aligned with obviously this. revolution happening in real time.
Last couple of weeks have been pretty big with Cloud Opus 4.6 coming out, ChatGPT, Codex coming out. A lot of advances and obviously the open cloud revolution that I've now done seven podcasts on in a row. What are your thoughts, Jamal? I think there are two open questions that I find really interesting right now. The first question is,
I tweeted that this morning, but is on-prem the new cloud, which is weird to think that that could even be possible. But we've spent since 2008 migrating everything to cloud because there were these economies of scale. And it created better margin and lower OpEx and lower CapEx because you could essentially share infrastructure with other companies.
And that's how AWS and GCP have built such gargantuan businesses. The counterpoint to that, though, is that in the AI revolution, companies, I suspect, will be fighting for their lives. And I think it's very much unclear whether it makes sense for a company to allow the natural leakage of their edge and their confidential and proprietary information out into the wild.
versus the control that they would get if they ran on-prem. That's a really important question. What do I mean by all that? Once you use these tools, it is very difficult for a company to be able to control how their data is used subsequently thereafter.
Meaning, if I gave you, Jason, a PDF of some really important strategy document or a PowerPoint deck or a really critical model, and you're interrogating it with one of these models, If you're just using ChatGPT, the mainline instance of it, you're leaking all of that prompt and response metadata back to ChatGPT, back to Gemini, back to Cloud. And there's nothing a company can do about that.
If you're using a set of agents to act on all that information, all those agent traces are going back to these model builders. That may or may not be a problem for some, but I suspect it is a deep problem for others and they just haven't uncovered it yet. When they realize that that is a problem, the enterprise will have to decide, do I just give up?
and keep running all of this stuff in the cloud in a shared experience, or do I bear the incremental cost of running this stuff in a more coordinated manner that I control on-prem? And that would be a crazy shift just to completely go back to where we were 20 and 30 years ago. That's a non-so-obvious thing that may happen. So that's number one.
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Chapter 7: How do token budgets compare to traditional salaries in the workforce?
This moment in time when we have people saying it's happening faster and it's become recursive. Recursive, obviously, fancy word for those in the audience who haven't heard it before. Just these models and these agents can go out and improve their own work. So after they do some work or a job for you, you can have another agent say, hey, here's how to do it better or go learn these new skills.
Go use this skill last 30 days to go find... the last seven days or 30 days of best practices with this tool and make yourself better and do that every night at 1am. What are your thoughts Friedberg on the moment in time we are in right now?
Well, I think the thinking historically was that it was going to be about recursive model development, where we were going to continuously improve the actual model And we were waiting for a context window where you could feed the model back to itself. So you're effectively retraining the model continuously. And it may be the case that the output is with recursive. And that turns out...
is having the effect that everyone was waiting for. So it's kind of a surprise. I saw a lot of computer scientists that have worked in AI for some time, I think, be a little bit surprised about this moment that we're in, that we're seeing such incredible strides in model performance just by making the output recursive. So let's see how far it goes. Are you still obsessed with OpenClaw, Jacob?
I am. We have now seen that every week, five to 10% of the work we're doing inside of our venture firm is being moved over to open claw. We call them replicants. You can think of them as personas. So we now have three or four of these. We give them a Notion account, a Slack account, and we give them a Google Docs account. They have their own email.
And I think all of this technology was here all along. It was really, or maybe for the last six months, let's say, really good models out there. But no company would give the keys to the kingdom to allow these agents to actually act on your behalf. Why? Because they don't want to be responsible if it ships your Bitcoin keys or your passwords to somebody else.
So in order to use these, you have to trust them. And if you trust them, and then you are monitoring them, the results are unbelievable. We have also, to your point, Shamath, fired up Mac Studios. We have Kimi on them. We are moving all of the work onto these. And then they'll use Kimi for most of their easy jobs, which is free. Then they will use Claude 4.6 Opus to orchestrate things.
We also, now that we have four of them, Friedberg, We've created OpenClaw Ultron, which is one meta replicant that is managing the other four. And it checks their work. It talks to them all day long about what they're doing and then summarizes it. And we're building skills into each one of these. So one of the skills is like doing deep research.
One of the skills is being able to go into our sales database, which is in Pipedrive. The gains we're getting, I was able to go through everything my Athena assistant was doing, and I was able to take about, and I know Chamath, you have an Athena assistant too, I was able to take maybe 30% of the Athena assistant's work and give it to the replicant.
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Chapter 8: What implications does the CBO report have for future economic policies?
B, you have this situation where too much of what you have has to be guaranteed into the future because for them, it makes no sense to price it on spot. And if you buy on spot, you just get these surges. You can't deal with it. So there is no solution today that makes any sense. It's absolutely correct, Shamath. I'll just put some numbers behind it briefly.
We, with our agents, hit $300 a day per agent using the Cloud API, like instantly. And that was like doing maybe 10 or 20%. That's $100,000 a year per agent. We're getting to a place where we have to basically now say, what is the token budget that we're willing to give our best devs?
And then if you aggregate it across all people, you can clearly see a trend where you're like, well, hold on a second. Now they need to be at least two X's productive as another employee. That is actively happening inside my business because otherwise I'll run out of money. Yeah, this is a very interesting trend that you're not going to hear anybody else talk about.
But when do tokens outpace the salary of the employee? Because you're about to hit it. I'm about to hit it. I think superstar developers are already there. Yeah. I think the rank and file is probably 10, 20% max. More than likely, they're spending a few thousand. The average non-technical employee is probably in the hundreds to low thousands. But to your point, the trend is what matters.
So unless we have some gigantic leap forward in generating output tokens at one-tenth the cost of what they are today, which I suspect we will have, so... Bear with everybody for a while because I think NVIDIA and Grok and Google and AMD, they're all incentivized to massively ramp up the energy density and massively push down the token cost.
That's going to happen, but it doesn't change the trend. And it doesn't change the incentives on confidentiality. Let's talk about prediction markets, gentlemen. They hit critical mass this past weekend at the Super Bowl. More than a billion bet on CalSheet, 700 million on PolyMarket, almost $2 billion in wagering.
The media has been obsessing a bit about market manipulation, insider trading, and all these issues that are totally valid to discuss around prediction markets, which are something new in the world, at least at this scale. Two specific examples from the Halftime Show.
A day-old anonymous Polymarket account correctly predicted 17 out of 20 halftime show bets, including the special appearances by Lady Gaga, Ricky Martin, but it only profited $17K, a tiny amount. And then another account created less than 24 hours before the game correctly bet on Bad Bunny's set list.
Wall Street Journal this morning, with an article titled, "'Israeli soldiers accused of using Polymarket to bet on strikes.'" Israel arrested several people, including army reservists, for allegedly using classified information to place bets on Israeli military operations. Quote, the account in question raked in more than 150,000 in winnings before going dormant for six months.
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