AI: Missing the Point

A little over a year ago I was attending a board of directors meeting for a small startup. This was still new to me — I had been taking directives from the board and my CEO for years, and had only just started attending the meetings myself. I am always eager to learn from new people. I picked a seat at the back corner of the table with some of my fellow VPs. A long day of presentations was behind us and I was looking forward to a light affair.

Wine selection made, one of the most technically literate people on the board turned to my CEO and asked one of the worst-phrased questions I have ever heard. I kept to my small talk with the head of CS. After he got his answer from the CEO, he scanned the table, found me, and said:

“Chris! How long until we’re at 11% unemployment?”

My mind tends to run ahead of the room. As soon as the challenge landed I started inventorying everything that would have to be true — everything required of AI, and of us, to make that number real. And worse. It missed the point.

So I didn’t leave him hanging. “It’s going to be a while,” I said. “Somewhere between five and fifteen years.” He was surprised by the range, so I elaborated.

“Look, Chuck” — we’ll call him Chuck — “start with the basics. The power grid cannot carry the compute those estimates require. Then you have the institutions: lawyers, doctors, and experts are not going to welcome AI as a replacement for specialization and expertise built up daily through training and interaction. Move up the chain to the models themselves and you have two more problems. Training data, and the cost to train — not just the cost to serve.”

“My own use of these tools, and their integration into my engineering team, points somewhere else. With supervision, we get real acceleration and better code quality. They all hallucinate, even on code. What I think we actually get is the ability to do more with fewer people. Writing copy becomes easy. Prototyping becomes easy. Visualizing data becomes easy. In experimentation, we have had good results running a local LLM for research and procedure matching against a large EHR — local, so nothing left the environment. Experimental, and all of it supervised.”

“Well, which jobs do you think will be most impacted?”

“Any job where language is the primary currency, or where the work has a logical structure and a fixed lexicon. Legal. Ads. Promotion. Software development. Honestly, close to everything.”

You’re missing the point.

“You know where it would be genuinely interesting to replace a person with an AI?”

“Where?”

“How about a board seat? Or better — the CEO.” (I like my CEO. He is good at the job and good to work for.)

“What? Well, I don’t think —”

“Why not? An AI can run a spreadsheet. It can read the reports and summarize most of what is in them. The other half of any CEO’s job is to bridge the distance between what is and what could be. AI does that for free. We call them hallucinations.”

“Interesting. That’s a very interesting answer.”

You’re missing the point.

“So the real question is this. With all of that savings and all of that capability, what are we going to do with it? What is the best possible world we could build?”

“I hadn’t thought of it that way.”

“Maybe we let AI propose how to allocate and align a country. Taxes and services, tuned to maximize quality of life and hold costs down. What do you think?”

“Uh. That’s a stretch.” He glanced back at the wine list. “I had been looking at the $400 bottle. I chose the $89. It seemed more appropriate.”

You’re missing the point.

Fast forward to today. A year of buzz and innovation, one year into a five-to-fifteen year estimate. The estimate is getting shorter — and not because of anything the technology did.

Not about Chuck. Chuck was ebullient, and ebullience is survivable. He wanted an interesting conversation about technology and business over a glass of wine, and the worst you can say about the question is that it treated 11% unemployment as a milestone to forecast rather than something that happens to people. Everyone at that table was curious. Curious is fine. Curious can be argued with, which is what the rest of the evening was.

I’m missing the point.

I was missing the point because I answered the question I was asked. I inventoried constraints — grid, institutions, training data, cost — as though the timeline would be set by what AI can do. It isn’t. It is being set by what capital decides to do, and capital has stopped waiting for the technology to earn it.

The point isn’t how fast or perfect the technology arrives. It’s who decides how much we burn down to deliver on imperfection.

Here is who is deciding. The group of people who are supposed to have been rewarded with capital because they make good decisions have shortened my estimate. Not because the use of AI is taking jobs, but because the capital necessary to make AI is taking capital from all other areas. The same financial imagination that once fielded wind turbines and solar arrays for the tax credits is now excitedly fielding data centers powered by turbines. Looking at how the markets and capital are reacting to what they appear not to fully understand, it appears as though we have all adopted burn it down and own what is left, wiping out society in the process — when this could be the optimistic march to a great future with some consideration and care.

And here is the imperfection they are burning it down to deliver. OpenAI released Astra last week, and while the normal hype machine rolls on about how much “better” it is than the last model, the details read out into four lines. It is more efficient — it does more with less. It is lumpy, not marginal: 8.6× better on one axis and worse on another, eight weeks after Sol. Its alignment improved, which is interesting and deserves more disclosure than it got. And the largest gain came from the harness, not the model. Harnessing has been the thing for me for over a year — it is the difference between a toy and industrial equipment — but when the harness takes a D student to an A, that is not intelligence. That is tooling with purpose. Astra is evidence of how lumpy advancement is in this space, while capital is being deployed as if it is solved and delivers. Adding an eleven to the volume knob does not change the result.

So what would it look like to not burn it down? The same capital, pointed at the things that would let this technology actually pay off:

• Clean energy — solar, wind, nuclear, and fusion — so the grid can carry what we are building without a gas turbine bolted to every data center.
• An efficient national grid. You will all take the tax breaks anyway; engineer the next 70 years the way our great-grandfathers engineered the last.
• Investment in people, not attrition. The productivity gains land hardest for the least experienced — that is where the return is. A society should be judged on how the least is treated, and we are ignoring this imperative.

In the end we continue to miss the point. A foundational new technology with benefits is literally being deployed with more disregard than any era before it.

Ironic, that an AI trained against millions of published works is more benevolent and forward-looking than the people funding it. We built a machine that read nearly everything we ever wrote and came back more optimistic about us than we are about ourselves. The least we can do is take the note.

Because the optimistic version is not far away, and it is not abstract. It is a clinic running its own model on its own hardware, so nothing leaves the building. It is the junior engineer who gets a third better in a year instead of getting cut. It is a grid our great-grandchildren inherit the way we inherited the last one. It is the same trillion dollars, pointed at the ground instead of set on fire.

A year ago at that dinner I asked the only question worth the $400 bottle: with all of this capability, what is the best possible world we could build? Nobody at the table had an answer, including me. I still don’t. But I know it is the question, and I know who gets to answer it if the rest of us don’t.

We need to stop missing the point.

Postscript. The story above is loosely based on an actual moment I am recalling from memory. As a human, my recall is exactly as perfect as today’s frontier models. The names have been changed; the point has not.

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Sources

• Grid — Belfer Center (Harvard Kennedy School), AI, Data Centers, and the U.S. Electric Grid: A Watershed Moment
• Compute demand — Nature, Data centres will use twice as much energy by 2030 — driven by AI (IEA Energy and AI)
• Institutional resistance (law) — Norton Rose Fulbright, AI in litigation: Update on Gen AI sanctions in 2026
• Institutional resistance (medicine) — npj Digital Medicine, Who bears liability when AI gives bad prescribing advice
• Training data — Epoch AI, Will we run out of data to train large language models?
• Cost to train — Cottier et al., The rising costs of training frontier AI models (arXiv:2405.21015)
• Hallucination in code — Spracklen et al., We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs (arXiv:2406.10279)
• Local models on PHI — JMIR/PMC, Local Deployment of Open-Weight Language Models in Dermatology: Viewpoint on Privacy, Equity, and Practical Implementation
• Which jobs — Brynjolfsson, Chandar & Chen, Canaries in the Coal Mine? (Stanford Digital Economy Lab)
• The 11% question — Axios, Behind the Curtain: A white-collar bloodbath (Amodei: half of entry-level white-collar roles, 10–20% unemployment)
• AI in the corner office — NetDragon Websoft, NetDragon Appoints its First Virtual CEO (Tang Yu, Aug 2022)
• AI allocating a country — Koster et al., Human-centred mechanism design with Democratic AI, Nature Human Behaviour (DeepMind)
• Scale of AI capital — Goldman Sachs, Global AI Investment Is Forecast to Exceed $1 Trillion in 2026
• Crowding out — Bridgewater, The Macro Implications of the AI Capex Boom
• Capital ahead of returns — Axios, Nvidia reignites “circular” financing concerns as it weighs OpenAI deal
• Tax-credit-driven investment — Knowledge at Wharton, How Tax Credits in Renewable Energy Finance Distort Outcomes
• Astra — OpenAI, GPT-6 Astra System Card. GPT-5.6 Sol shipped 9 Jul 2026, Astra 3 Sep 2026 — eight weeks. Over that gap, third-party evaluators measured: no-CoT math time horizon 3.6 → 30.9 min (UK AISI); CoT controllability 48% → 93%; FrontierCyber 34/226 → 86/226 and cost per successful solution ~⅓ of Sol’s (Irregular); bio HPCT 66.5 → 65.9 and WCB 67.6 → 61.6. Also: fewer factual errors, fewer deception/reward-hacking flags, first model at the Critical cyber threshold.
• Astra coverage — TechCrunch, OpenAI launches Astra, its powerful (and controversial) new model
• The harness, not the model — Wijk et al., Forecasting Frontier Language Model Agent Capabilities (arXiv:2502.15850) — same model, 33% vs 62.2% on SWE-Bench Verified depending on scaffold
• Clean energy — Data Center Dynamics, Three Mile Island to return as Microsoft signs 20-year, 835MW AI data center PPA
• National grid — U.S. DOE Office of Electricity, 2026 Draft National Transmission Needs Study
• Enable the pack — Brynjolfsson, Li & Raymond, Generative AI at Work (NBER w31161) — 14% average productivity gain, 34% for novice and low-skilled workers, minimal effect on the already-experienced
• Attrition backfires — Entrepreneur, Klarna CEO Reverses Course By Hiring More Humans, Not AI — ~700 support roles cut, then rehired after quality fell
• Turbines — Data Center Dynamics, Elon Musk’s xAI granted permits for 15 gas turbines at Memphis data center
• Trained on published works — NPR, Anthropic to pay authors $1.5 billion in settlement over chatbot training material (Sept 2025): court found more than 7 million digitized books in the training corpus, beginning with ~200,000 from Books3; settlement priced at ~$3,000 per work across ~465,000 registered titles. Also Gao et al., The Pile: An 800GB Dataset of Diverse Text for Language Modeling (arXiv:2101.00027), the public corpus that included Books3.