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by robomartin 19 hours ago
I remember when Amazon was going to go broke every year for over a decade.

Until they didn't.

4 comments

That wasn't what it seemed like at the time. Amazon didn't post profits, sure, but they sure as hell weren't a giant money suck either, they didn't need billions in financing to run their business. There were a lot of Amazon bears, but they were concerned about the high valuation, not about them going broke (since even the most pessimistic bear can read a cashflow statement).
That’s because they were reinvesting the profits. I think they had given a profitable quarter just to show that they could do it.
And I'm sure Anthropic would be immensely profitable if they stopped investing their inference profits into training newer models.
And then everyone would stop using their inference as soon as a better model for a reasonable price came out.

The R&D expenditure is a critical requirement for the inference profits, to the point where we should probably lump their financials together, at which point is definitely not profitable.

What will it look like when R&D plateaus (and yes it definitely will, but it could take a while), investment falls, and a few main competitors remain in the music chairs?

It's very difficult to predict. The inference profits we are seeing the profits of a company that is temporarily ahead, but the revenue will level-out in a more stable market, depending on how many survived. It's also hard to tell where the costs will be at the end of the game, with constant efficiency optimisation mixed with cost increases for higher intelligence.

I think it will be quite similar to the semiconductor industry, where, yes there are some key monopolies, but they are not the initial big players, and none of it is actually very profitable; while the real profits are reaped by those that make popular consumer products based on the foundational tech. I guess the main difference is that OpenAI and specially Anthropic have been quite effective at directly tapping into the consumer market rather than remaining technology providers.

  And then everyone would stop using their inference as soon as a better model for a reasonable price came out.
Exactly. It's competition now that is driving high training costs - not a business model problem. There will be winners and losers. The losers won't be able to keep up with the training costs forever. See my post here: https://news.ycombinator.com/item?id=49119265
It's never that simple, that's not the only possible endgame.

We have seen plenty of examples in other industries where you can never really stop investing a ton on R&D with diminishing returns (like in semiconductors or pharma), because the moment you stop newcomers overtake you.

Or the whole thing becomes a commodity with lots of competitors, where technological advantage is overtaken by marketing as the dominant force.

Or you really are left as the only player alive, but you realise that the market cannot absorb higher prices for your product by then, they prefer just not to buy it. Perhaps you are the only player alive because the business has become so low-margin that everyone else has abandoned it intentionally.

That Silicon Valley pitch you are echoing rarely works out as advertised, even for the winners.

  We have seen plenty of examples in other industries where you can never really stop investing a ton on R&D with diminishing returns (like in semiconductors or pharma), because the moment you stop newcomers overtake you.
In semiconductors, it almost always become a monopoly or dupoly. x86 CPUs - only AMD and Intel left. Discrete gaming GPUs - only Nvidia and AMD left. 5G chips - only Qualcomm left in western market but Apple is about to join the part. In advanced chip node - only TSMC but Samsung and Intel survive due to geopoltics.

I think you're proving my point. Eventually, R&D heavy industries almost always become a monopoly or duopoly. Small/losing players can't keep up and drop out or acquired.

For how long though? If Amazon never built AWS the core business conceivably would still be around today, if Anthropic stopped providing new models two years ago no one would care about them now.
An LLM model depreciates to $0 in a year or two.

An Amazon fulfilment warehouse depreciates to $0 over 30 years.

One type of investment is different to the other.

How much does that warehouse make vs that LLM model? IE. How much has Opus 4.5 made for Anthropic?
Amazon had a close call around the .com crash as capital markets froze, but they were not going broke every year. They were purposely (and rather famously in business circles) investing every dollar made in order to grow the business. It was clear early on the original business worked.

Amazon also added/pivoted to AWS, which is where a huge part of its value comes from today.

I remember when hundreds of dotcom companies were going to go broke, and they all did.

Not sure what your point is.

Simple: None of us have a crystal ball. Trying to predict the future is foolish.

I am not saying that what we are seeing might not be problematic. I am only saying that nobody really knows. We can't know.