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I want to propose alternative reality where 1.5-2.5T in value doesn't go to a handful of companies. Instead it turns out to be like restaurants where this gets distributed to lots and lots of small, local, mostly interchangeable teams. There will of course be some super star "chefs" leading the industry and setting trends and some "restaurant chain" like big businesses and supply chain for all of this.

FWIW I do think that availability of competitive open weight and other non-frontier models, along with improvements in harnesses that can get good results out of these models, will result in less concentration and a healthier marketplace.

However, these frontier labs are also making moves that could let them capture a disproportionate share of the upside. One possibility is a situation analogous to the smartphone manufacturing space, where there are dozens of players but just a handful (e.g. Apple, Samsung in smartphones) capture the lion's share of the revenue.

Apple you can’t exit the ecosystem.

Samsung the same. And is the best android device.

If tomorrow comes a Nokia os will be dead in the water: it has no apps.

But with a new llm that doesn’t matter. There is nothing sticky about typing Gemini, Claude or codex in a cli.

There's nothing sticky today but you can bet they're working maniacally to fix that. These companies will make most of their money in the enterprise space and there are probably unlimited ways to engineer stickiness in an enterprise setting. Like, MSFT still rakes in those billions despite pretty much every one of their products having commodity competitors.

The AI labs are also making moves to secure long-term enterprise presence, such as their Forward Deployed Engineer strategy. I think that is a trojan horse play that could make enterprises dependent on them forever, much like so many companies are still dependent on IBM's mainframes. As an extreme example, you could imagine a company's core business logic encoded in the weights of a proprietary model custom-trained and hosted by one of these model providers, something even more inscrutable and sticky than ancient COBOL codebases.

The world is not zero sum. Value is created, not just preserved. Anthropic and OpenAI creating value does not imply that smaller guys can not also create value.

But marketplaces also exist and big players in a marketplace are often able to manipulate the market such that they are advantaged and small players are not able to break in.

This is true of every market that has ever existed, and that's not stopped small players from finding niches.

How? Training and operating models seems to naturally focus on those willing to invest quite significantly in these operations.

If RAM prices come down, running your own models will be relatively affordable.

Sysco is pretty big.

I am deeply surprised by the silence of philosophers, sociologists, liberal arts majors, economists. Where are the think tanks who contemplate and debate the societal aspects? The tech is advancing full steam but the "other side" doesn't feel anywhere nearly ready.

Idk why you're perceiving silence. Feels to me like this is the main thing people talk about nowadays.

It has to do with the scope of what they're discussing. It seems extraordinarily small: e.g. what if AI increases productivity growth by 0.4%? Do data centers use too much water? Are AIs racist when reviewing resumes?

The frontier labs, on the other hand, are thinking about replacing all human labor, ending death, and the risk of it causing human extinction. Most of the apparatus we're talking about approach it very parochially; it's almost like they're embarrassed to take the grander ideas even a little seriously, for being too nerdy/sci-fi.

Death is a quirk of the universe underwritten by the current understanding of the second thermodynamic law. I don’t think any AI companies are making progress on that any time soon.

The public would happily string up any of these CEOs if given the chance

Silence? Even the pope has come out against AI? Who hasn't? Diplo??

Because the "other side" is busy trying to anthropomorphise AI into solving the trolly problem, while being mostly clueless about the actual problems.

They'll show up after the fact and whinge endlessly about how they should have been involved.

I guess the real problems are things like people not being allowed to post AI-generated images in digital drawing, painting, and photography communities, because I see a lot of boosters ceaselessly whining about that abject “discrimination”, despite having plenty of places where people post all kinds of that garbage all the time.

Or maybe every cultural group has its own set of whiners and we always think the ones we disagree with are the loudest.

Reid Blackmun has written several books and has a consultanting agency to guide ethical implementation of AI

Sometimes the great algorithmic gods give us a glimpse of our own bubble.

> If LLMs can boost their productivity even by an average of 5% (studies from ~2024 put it in the ~30% range depending on task) that is ~1.5 - 2.5T in value

Minus the cost of inference, that might not be the boon you're making it out to be. I hear what people around here are spending on their api and I'm skeptical that these tools are making me that much more productive.

Personally, for assisted development, I haven't seen much progress in a while.

This assumes the hand-wavvy "production" of knowledge workers translates directly and equally into money. Making a CEO 5% more productive might mean a billion dollars or it might mean nothing. Making a developer 5% more productive might mean the app gets finished faster or some more tickets are closed, but certainly direct monetary relationship between "work done" and "money made", especially if the knowledge work being optimized is, across the board, more of the busy work ("known-knowns") and not the "what should our new product line be" that tends to move the needle in spurts.

If you are a software contractor, yes more productivity turns directly into money. But it is the same for all other contractors, so there is no gain there either. This is really a structural transformation that crosses industry boundaries, not unlike when we all got on the internet. All I know from living through that is certain changes are obvious even if they take a decade (there will be more AI and more deeply integrated in our lives) but most of the secondary effects are unknowable.

If living through the dot com era taught me anything is that transformative tech routes around walled gardens (i.e. AOL/Prodigy) which is bad news for Anthropic/OpenAI. The wild west that comes after is a window of opportunity for founders but investors will overcommit early (like now) and undercommit when the dust is settled (post bubble).

yeah, my personal work experience has taught me that extra productivity is actual really just a problem for many companies. You do more work now I have to too. We don't have to work because we have special relationships with or as gatekeepers ensuring we receive regular income from government, other corps, etc.

Look at what happened to twitter. So many companies aren't 'productive', they are simply parked somewhere where they can charge a toll, and then they pretend legitimacy.

> Note, at 5% productivity boost, humans are not just in the loop, they are the loop. AGI or large-scale replacement of humans is not even needed, but the financial opportunity is already immense, and it scales with how much human productivity can be improved (i.e. how much work can be offloaded to LLMs.)

The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed, which for me would probably translate to something like a 3% increase in productivity. I spend a lot more time on things like getting agreement between teams, documenting approaches to things that don't exist on the wiki, etc, that LLMs are significantly less effective at. Or just can't do; no one will be happy if I send an LLM instead of me to meetings.

I suspect a lot of roles are like that. They give a 10-30% boost to the core role function, but that core role is still only 30-50% of what you do.

> that is ~1.5 - 2.5T in value annually

That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap. And this is before we consider that they need to do it for cheaper or why would anyone bother. Realistically, potential revenue is probably half that at best.

It's also before cutthroat pricing really kicks in. People are willing to pay for Claude right now; I still suspect that as time goes on people will start looking towards Deepseek/GLM/etc models that provide 95% of the performance at 10% of the price. That'll cut the market even further.

The question is how much demand for knowledge work swells as prices fall, and whether that's a soft landing or a crash.

> That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap. ...

It's also before cutthroat pricing really kicks in.

Right, that's more of an estimate on the value proposition of the overall AI industry, rather than valuations of the industry or specific players. While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition (https://news.ycombinator.com/item?id=48740472)

> And this is before we consider that they need to do it for cheaper or why would anyone bother.

Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."

> The studies I've seen recently (at least in the software space) put it at something like a 10% increase in coding speed...

Curious to see which studies you're looking at, the studies I'm thinking of (some here: https://news.ycombinator.com/item?id=45379452) are from 2024 - 2025, so already old and before agents really took off.

However, your point about meetings and agreements and documenting is much more germane. My theory is that the largest productivity gains -- and subsequent labor displacement -- will come from reducing coordination overhead: https://news.ycombinator.com/item?id=48040999

I was thinking of studies like https://www.science.org/doi/10.1126/science.adh2586 I may have misremembered the domain, or I may have neglected to remember the publishers. This time around I explicitly excluded any AI-tied sources (I don’t trust Anthropic to have an accurate study of productivity boosts).

> While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition

We may have a fundamental disagreement here because I think you see reasons for businesses to prefer LLMs beyond price, but my point was that I think a substantial portion of that top line number will be eaten up by needing to be cheaper than humans.

Like 30% cheaper than humans for the same task isn’t an unreasonable demand for onboarding, and that would lower the addressable market to .75-1.25 trillion. That’s also revenue, not profit. Their best case is probably 30-50% margins (even that might be high, I suspect it will become low margin as models are commoditized), so $600 billion-ish in profit spread across all the players and that feels pretty generous to me.

I suspect a lot of general knowledge work tasks could be done with fairly dumb local models. The small Qwens could probably respond to my Slack and emails for me.

> Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."

It’s also an enormous supply-side risk. Your AI provider could charge basically anything they want and you have to pay it, for the same reason people still pay bonkers Oracle licenses: too hard to migrate away.

Unless companies maintain their ability to swap providers, but then I can’t see why prices for inference don’t crash down to the cost of GPU time and electricity. If Claude doesn’t have some secret sauce that keeps me locked in, I don’t see why I wouldn’t use Deepseek or Qwen or one of the other dirt-cheap inference providers.

I just haven’t seen anything that seems to keep people locked in so far. Even the non-tech people I know that like AI are rotating providers to find their ideal price:performance ratio, many are finding success outside of Anthropic/OAI. I do know a couple holdouts that refuse to use anything Chinese. They mostly stick with OAI or Anthropic, though they do moan about paying more.

Agreed, there's nothing sticky about the models right now, but I see the big AI players making moves that hint at long-term success, and even dominance, in the enterprise space, which is where the real money is. As Microsoft has shown you can create stickiness for things that are already heavily commoditized. For instance, the FDE play itself could be a huge business.

Plus the big factor in my mind for why these frontier labs will succeed -- and this is very fuzzy and hand-wavy -- is that they are very business- and government-savvy and execute extremely fast. They have the most powerful AI models at their fingertips with sharp people who know how to use them, along with insane levels of funding, and are showing the world how a truly AI-empowered organization can operate. I suspect they will thrive despite all the forces arrayed against them.

I’d also point out that LLM inference revenue already totals more than 100B annually based on publicly reported numbers. Almost none of that is replacing knowledge workers. Almost all is increasing their productivity. So empirically what you describe is already happening to a nontrivial degree.

Where does this BS come from?

> publicly reported numbers

Anyone can google it /shrug

So, maybe a dumb question, but do we have any real reason to believe that productivity at any level is actually associated with the actual underlying profit and earnings of any of the companies that are heavily 'knowledge work' focused, like big tech? Sure, for a factory, or a farm it makes sense.

But for tech I get the impression that valuation has been fundamentally unmoored from actual production for decades already. Its less about 'productivity' and more about vibes.

You’re trying to apply value based pricing (infinite margin upside) to a commodity.

Pre-bubble pricing: $1400 gets a 128GiB iGPU optimized for inference. Glm and kimi need 800-1000GiB. Call it 1TiB. The $1400 boxes could be ganged into sets of 4-8, with a switch. Call the switch $1000.

Each box has a TDP of 250W. 8 x 250/120V = 16.666A, or one household circuit in the US, so no new power infrastructure is needed.

$1400 x 8+1000=$12,200. Assuming standard five year depreciation, that’s $2440 a year. There are a billion knowledge workers alive today. So that’s $2.4T annual revenue. Average net profit margins on computer hardware are 4.3%. That works out to $105B net income, globally.

So, I guess the question is whether the (currently #2) open weight models provide $1.4-2.4T less value per year than the #1 and #3 models, and, if so, if customers can measure this, or are willing to spend 2x more and deal with censorship, data theft, intentional enshitification, sabotage, ads, product placement, etc, to get the slightly “better” model.

Also, note that my numbers assume moore’s law stopped for all time in 2024, but we’ve seen HW improvements since then.

Right, that number is more of an estimate of the value proposition of the entire AI industry rather than projections of revenue or valuations... it's essentially an estimate on how much the market could theoretically bear. Whether the companies can capture that value is, to your point, rightly a different question.

I do think open weight and other competitor models, especially with better harnesses, will play a significant role in the equation and will result in less concentration in the market. However, I do also think the big AI companies will capture a lot of that value. Partially for the same reasons that the cloud industry has been growing like gangbusters, even pre-AI, despite on-prem being much cheaper: companies will outsource anything that is not deemed a "core competency" for their business.

A lot of the problems you mentioned will be relegated to the consumer market and won't apply to enterprise contracts -- which is where the real money is.

A buzz word here, a buzz word there.

Mate your logic is insanely broken, underpinned by wishy washy assumptions mixed with shaky predictions about the future.

Aka a whole lotta noise

What a load of nonsense lmao.

Pls stop posting you are creating noise.