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Economists have been studying GPTs for decades. They just meant a different kind. In economics, a General Purpose Technology is one that restructures entire economies rather than single industries – steam, electricity, computing. Carlota Perez mapped their lifecycle into a pattern: a turbulent installation phase full of speculation and uneven adoption, followed by a deployment phase where the gains actually compound. The interesting part is the gap between the two. That's where we are right now – except this time the General Purpose Technology shares its acronym with the thing that's causing all the trouble (GPT in AI – generative pre-trained transformer).

This week offered three dispatches from that gap, and together they paint a picture that is worth our attention.

On April 6, OpenAI published a 13-page policy blueprint called Industrial Policy for the Intelligence Age: Ideas to Keep People First. It proposes public wealth funds, portable benefits, worker participation in AI deployment, and a rethinking of payroll-based tax systems for an economy where wages may no longer be the primary source of national income. OpenAI frames these as "ambitious, but intentionally early and exploratory." Whatever you think of the messenger, the underlying logic tracks directly to a real problem in economics known as Baumol's cost disease: sectors where productivity is hard to improve – healthcare, education, government – see costs rise relentlessly relative to everything else. AI potentially cures Baumol's disease by making intelligence-intensive services scalable for the first time. And that changes the fiscal math of entire nations.

They are not alone in trying to grasp our future economics in a more tangible way. Today, Workshop Labs announced it's joining Thinking Machines, Mira Murati's lab. Workshop Labs grew out of The Intelligence Curse, an essay series arguing that when AI replaces labor as the dominant factor of production, powerful actors – states, corporations – lose their structural incentive to invest in people. Their founders write that they "started by asking what happens if AI takes everyone's job" and "didn't like that answer." In Solow growth model terms, they're arguing the elasticity of substitution between capital and labor is approaching infinity – and that this breaks the social contract, not just the job market. They suggest the following solution: AI systems aligned to individual users, decentralizing ownership rather than concentrating it. That Murati's lab absorbed this team tells you where at least one frontier lab thinks the real problem lives.

Meanwhile, the Stanford HAI 2026 AI Index reported that generative AI reached 53% population adoption within three years – faster than the PC or the internet. The estimated value of these tools to US consumers reached $172 billion annually by early 2026, with the median value per user tripling in a single year. Paul Romer won the Nobel Prize for showing that ideas are non-rival goods – one person using an idea doesn't stop another from using it. That was elegant theory in 2018. In 2026, it's an observable market condition with a $172 billion price tag.

But Perez's framework predicts something else about the installation phase: adoption is wildly uneven. And it is. Sometimes in very unexpected places. Steve Yegge shared a conversation with a long-time Google tech director revealing that Google's internal AI adoption looks roughly like any average enterprise –20% power users, 60% on chat tools, 20% refusers. The Googler explains to him that it happened because of an 18-month hiring freeze: no one moves between companies, so no one arrives from the outside to calibrate how far behind Google are. Yegge calls it “the Great Siloing.”

So here's the picture. The original GPT economists warned us: General Purpose Technologies don't distribute their gains evenly or on schedule. The new GPTs are confirming the theory in real time. We are nowhere close to understanding the economic reality that awaits around the corner. But at least the abundance frameworks are now being drafted in real time.

This is both exciting and deeply concerning, because we're entering genuinely uncharted territory. Throughout history, only a thin stratum of the population ever had to figure out how to live in abundance. The aristocracy was, in its own imperfect way, an experiment in what humans do when freed from the necessity of labor – and the results were a mixed bag of patronage, philosophy, and spectacular dysfunction. But we have never been in a position where abundance could plausibly be spread across an entire population. There is no playbook for this. We don't even have the right mindset for it yet – centuries of equating human value with economic output don't dissolve overnight. (I wrote about this before – the concept of an "aristocracy of all" that AI could make possible, if we choose to build for it.)

The frameworks are being written. I’m not sure any of us are ready (yet) for what they describe. We are in the messy middle of installation.

→ If any of those thoughts resonate with you – share them across your social networks. Let’s keep the conversation going.

Topic 2: Meta AI and KAUST just proposed a new kind of machine: a Neural Computer  –where the neural network doesn't use a computer, it IS the computer. Computation, memory, and I/O all collapse into one learned runtime. Sounds revolutionary. But is it? And will it even work? Let’s discuss → 

Trends we see:

Foundations, Interpretability, and Limits

World Models, Simulation, and Embodied Representations

Reasoning, Reinforcement Learning, and Failure Modes

Compute, Memory, and System Efficiency

Learning Dynamics, Adaptation, and Test-Time Learning-

Agents, Environments, and Real-World Interaction

Multi-Agent Systems and AI Workflows

That’s all for today. Thank you for reading! Please send this newsletter to colleagues if it can help them enhance their understanding of AI and stay ahead of the curve.

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