with the total employee compensation of $12.96 trillion in the United States, you can argue that only about half of that market (at the most) is open to disruption from AI replacement products,
Another way to think of this is that AI might be able to earn as much as half of the current U.S. labor force. To me, that sounds optimistic.
Damodaran tries his best to arrive at a quantitative valuation.
With Anthropic, for instance, where the rumored pricing for the IPO is $2 trillion, allowing the company premium pricing margins (after-tax operating margin of 30%) and above-average risk (cost of capital of 10%), the company will have to generate close to $1.2 trillion in revenues, if the AI market matures in ten years, and close to $2 trillion, if the wait is 15 years.
…Using an aggregated market pricing of $5 trillion (probably a conservative judgment, given the VC pricing of hundreds of companies in the space) attached to all AI product and service companies, and assigning a blended operating margin of 20% for the industry, the revenues that you would need for the entire business to breakeven would be $5 trillion, with a 10-year wait, and more than $8 trillion, if the wait is 15 years. Looking back at the discussion of the total addressable market in the earlier section, you can see that this would represent quite a reach, a manifestation of the big market delusion.
From his conclusion (TAM = total addressable market):
a $22 trillion TAM for AI is fiction and recognize that having your ARR grow 80% a year last year is not even close to being a rationale for why you should buy Anthropic at a $2 trillion pricing.
I’m not going to short Anthropic or other AI companies, because prices in the short run can get more irrational. But I’m not going to be a buyer, and I even may try to find an index fund that doesn’t buy AI stocks.
The bet is that a fundamentally new architecture has the potential to take AI to places that today’s LLM-based AIs can’t venture. And one day, pioneers hope to merge these two schools of artificial thought.
…The control systems of today’s more sophisticated robots rely on physics-based simulations of the physical realm. These, too, are world models, though they are painstakingly coded and highly specialized. What works for one kind of robot doesn’t work on another.
Today’s world-model startups want to create a control system that’s as versatile when piloting robots as today’s LLMs are when crafting text
As I wrote before, the physical world is harder to master than the digital world.
“The real world is very complex, and has a lot of special cases and hard edges, and you can’t approximately miss something,” says Konidaris. “If you hit something while moving your robot, everything changes.”
Interpretation is the service with LLMs, not delivery. The model cannot not read and digest everything you send. That is the whole point.
Her excellent essay makes the point that the sort of privacy that was built into snail mail is not as easy to protect with email and probably impossible to protect with Chatbot conversations.
She relates this to the problem of accountability for the actions of AI agents. By now, I assume you’ve read or at least heard about the paper by Tyler Cowen and Sonia Pearson.
Dario Amodei, CEO of Anthropic, said this week that the public’s negative view of AI stems from a deeper crisis of trust. Not from his risk warnings. Not from dystopian science fiction. From a fundamental breakdown between what the tech industry promises and what the public experiences. He said the most accurate criticism of AI companies is that they have not delivered on their big promises to benefit the world.
…Farzad’s response on X was the best I received. Prove out the abundance thesis in the most visible areas. Health. Housing. Education. Cost of living.
But if I am right, the models cannot deliver on those things. Health is a physical problem. Housing, education, and the cost of living are social problems. The models are masters of the digital world. But the physical world and the social world are outside their current circle of competence.
substacks referenced above: @
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