alt Hacker NewsI don't see how Anthropic can become profitable enough to justify its investments unless they can either stop training models ("Pace the Frontier!!1"), or magically eliminate open source competition so that their volume x margin on inference is high enough to dwarf the training costs.
For the IPO, I assume they're going to try to get everyone to focus on inference - which is profitable in isolation. But I don't know how they'll distract the market from the other half of the equation.
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Open source is what has given them all their coding ability.
Open source doesn't have the compute to meaningfully hurt them.
If the models get good enough, the demand for them will soar.
These companies believe they're a year from AGI, and that's how they'll be profitable.
Unlike the dot-com era, this is a bubble that everyone saw coming.
Like dot-com, there are many long-term winners, but cash hungry frontier labs are not among them.
Prediction:
OpenAI, Anthropic, Oracle, and SpaceX[1] go down and assets be sold or companies acquired for a fraction of their valuation. The same goes for most neoclouds. Profitable "old" tech companies like MS, Google, Meta, and Amazon are fine and scoop up the valuable parts for pennies.
Nvidia will be in Cisco's position (Cisco was the world's most valuable company in the dot-com era). Both are good companies that are not going anywhere, but stock prices get decimated when markets collapse (Nvidia liabilities are not high enough to take company down, they are mostly tied to there not being a bubble and data centers are getting built)
[1] Everyone already knows that, according to SpaceX, > 80% of SpaceX's valuation is in AI, not in space stuff, right?
It's also weird that most seems to think that if inference is good, it's all good. You still have massive costs in data centers, replacing hardware, upgrading hardware, paying salaries, etc. You can have 99% margins on what you sell and still go bankrupt.
> inference - which is profitable in isolation
They have not actually demonstrated this, really.
Question is really how many companies will stay due to inertia while competition catches up
You're of course speaking of a world that doesn't manipulate the cost of memory.
Hard to run local modals when sam altman continues to drive up the cost of compute.
Their inference margins are already high, the thing bringing their net down isn't training per se, it's the fact that they have to train the next iteration right away due to competition, otherwise an extended inference cycle would've eventually recouped training costs.
The bet is that cost per intelligence will keep going down and demand for intelligence will grow at the same time, if this bet holds, a user could get the same or more done with a cheaper model (running-cost wise) while paying the same subscription and the lab gets improved margins.
Open source is a challenge but their current moat is the fact that they're ahead, and as long as they stay ahead they'll be able to charge a premium.