Marc Andreessen 🇺🇸

I’ve been saying this for months, and the early data is beginning to support it. Five years from now, I believe AI will create more jobs than it eliminates. That may sound counterintuitive. But the data is proving this theory. Here are some factors worth considering. AI is exposing new human bottlenecks When one part of a workflow becomes dramatically faster, the constraint simply moves elsewhere. Organizations then need more people to remove the next bottleneck and capture the value AI has unlocked. Automation does not eliminate the need for human contribution. It often reveals how much more could be accomplished with it. AI fluency will become one of the world’s most valuable skills The emerging divide will not be between humans and agents. It will be between people who are highly fluent with AI and those who are not. AI-fluent people will not be 10% more productive. In some forms of work, they could be 50x or even 100x more effective. They will imagine better uses for AI, orchestrate agents and apply judgment where machines still fall short. These people will be scarce and enormously valuable. Productivity creates demand The assumption behind mass unemployment is that the amount of work the world needs is fixed. It isn’t. When technology becomes dramatically cheaper and easier to create, we do not simply produce the same amount with fewer people. We build more products, start more companies, solve previously uneconomic problems and serve markets that could never be served before. AI will lower the cost of ambition. The real risk, therefore, is not that humanity runs out of work. It is that millions of people are not prepared for how quickly the nature of work changes. The future will likely have more jobs. But they will not be the same jobs, performed in the same way, by people with the same skills. The imperative is not to protect people from AI. It is to help every person become fluent in it.

This is happening in plain sight. The leading AI companies themselves are embroiled in the fiercest battle to hire the most highly paid software programmers in the history of the world. And so it goes.

In late November 2025, AI agentic harnesses became highly useful, and enterprise adoption exploded. For example, based on BTOS data, the percentage of large (250+ employees) U.S. enterprises using AI grew from ~25% to 37% between November 2025 and May 2026. And the unemployment rate... hasn't moved. For workers aged 20+ it was: September 2024 (first reasoning model): 3.6% November 2025: 4.1% July 2026: 3.8% We see the same thing for workers aged 20-24: September 2024: 7.0% November 2025: 8.3% July 2026: 7.1% This is still very early data, but perhaps some aggressive assumptions regarding AI's impact on the labor market should be tempered a bit?

The age 20-24 unemployment rate is now ~unchanged since the AI boom began:

The fact this one dude - by himself - can walk into any state and find blatant fraud everywhere he goes proves two things: 1) the fraud is rampant at an unprecedented scale and 2) the government agencies who are supposed to protect our tax dollars are either completely incompetent or complicit in the fraud.

Nick Shirley uncovers an adult day care in Flushing, Queens with 7,000 phantom members. Nick: “This public document says you have 7,899 members.” Employee: “No, we don’t have 7,000 members.” Nick: “So you’re overbilling then? You’re getting paid $1,600 per patient — that’s how you got $12.9 million in 2024.” Employee: “Please leave.” American taxpayer dollars at work.

Yuji Tachikawa, one of the most respected mathematical physicists in the world, reports that Fable solved a problem that he and his collaborators were stuck on.

The skill that Theo is implicitly describing in this essay is quite hard to do well: management. In business schools we have a whole department devoted to it (I work and teach in one). A large fraction of people will soon find themselves going from solo work to managing whole teams. Knowing when to hand off to whom, how to communicate instructions, delegating leads and followers, establishing flexible hierarchies—it’s a completely different skill set than what most are used to, but it’s worth putting in time to figure it out.

This is the best essay of the year: - The dispersion of knowledge is a collective strength; it’s the source of variety, adaptability, and resilience of the overall system. It’s the reason that free markets outperform planned economies. - For artificial intelligence to benefit from distributed knowledge, it must itself be distributed. - [Models] that handle live, multimodal interaction natively, in the model itself rather than in scaffolding bolted around it [allows] interactivity [to scale] with intelligence. - Even with the best intentions, a model shaped in one place inevitably encodes the values of its owner, not the individual users it serves.

Steve Mandel of Lone Pine was making a similar point, that there were fewer players doing true price discovery on assets 3-5 years out, which advantages those who still play that game. Well, turns out, the water's warm, and Ken Griffin is wading in.

Ken Griffin says short-term alpha from calling the quarter with alt data is dying, and the next era of stock-picking belongs to those with a long-term horizon. "I think there'll be more focus and emphasis on those who have really good vision about what companies are actually creating transformative products that will change society. The market will reward that far more intensely in the future than will a company beat this quarter's earnings or not. I think the question of will a company beat this quarter's earnings has gotten far more difficult over the last 10 years because, for example, the rise of alternative data. I have access to the credit cards of millions of Americans. What are they spending money on? What's that mean for Starbucks revenues this quarter? What's it mean for McDonald's this quarter? This is a decade-old transformation, but the here and now is just becoming far more transparent, far more readily understood and triaged by the combination of really bright people and really good AI technology. Where this will leave us is those who are able to see what is unfolding over years to come will be in a very valued position on a relative basis."

I'm perplexed at why people who I know are smarter than me, including the authors, buy the epistemic strategy used in the AI 2040 scenario, in which, tl;dr, the famous METR time horizon plot predicts eschatological doom. I don't believe the authors have sufficiently grappled with why the radiologists and programmers still have their jobs, and why baristas haven't been replaced by automatic espresso machines Or even why we haven't seen more small-scale scheming / reward hacking / misalignment damages due to the use of coding agents across Big Tech, in which we've already granted such agents significant autonomy. I think if these gaps were more deeply considered they'd be making a more nuanced and less extreme argument. In the meantime I'm concerned that resources, advocacy and governance are being influenced by their conceptual model.