> Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T param
I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.
Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.
They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.
Given that tokens are supply constrained right now for Anthropic and OpenAI (especially a problem for Anthropic), stepwise efficiency advances for either would give it a leg up on the other. It would also help them better compete on price with Chinese models.
Given that neither company releases parameter counts, that sort of information would be slow coming out anyway. The most important thing is improvements in actual performance/ benchmark numbers, which allow them to preserve their price points as much as possible.
Google, who has invested in their own hardware supply chain and is already solvent in their own right, seems to be best positioned to force the other players to implement SOTA optimizations in their product offerings.
Google can definitely play a spoiler role here not only due to their compute infrastructure and ability to play the long-game financially but they also have more existing ways to monetize with their other businesses.
The ideal pro-consumer scenario is OAI and Anthropic are prevented from extracting monopoly rents between 'close-enough' self/cloud-hosted open source on one side and Google on the other. I'm really hoping that's how it plays out. Of course that will be somewhere between bad and disastrous for all the VCs and hedge-funds who financed the mad AI build-out far in advance of demand, and then kept funding it as prices went vertical.
However, I'm shedding no tears for them as I look forward to the fire sales when all the GPUs and RAM they pre-bought flood back onto the spot market. :-)
Google has also built a Knowledge Graph Ontology project which has stored facts. So LLMs could just incorporate facts requirements from there. All they need is a proper reasoning model which is reason heavy and fact lean.
> While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret.
So you are saying that frontier AI labs are spending billions of dollars on datacenters as a form of marketing. And they are colluding to hide the fact that they don't need to.
Of course they profit more if they are in front, but bleeding money to pretend to be in front is not a winning strategy. They can't fool the market if their models are not actually better, and they know this.
No. Your paraphrase is not at all what I was saying. And I certainly don't think they are "colluding." There's a thing which economists call "conscious parallelism" or, sometimes, tacit collusion.
It occurs when competitors in an oligopoly (a market dominated by a few large firms) independently recognize their shared market interdependence. Without any explicit agreement, meeting, or secret communication, changes in pricing, output, and marketing strategies tend to align simply by observing and reacting to each other's public market behavior. It happens quite often, has been extensively studied and isn't illegal. No nefarious cabal required.
> bleeding money to pretend to be in front
I never said bleeding money was the purpose. It's a side effect of pushing the envelope of performance and capability. They have already spent enormous sums on infrastructure and have committed to spend much more in coming years. This is a risky, but potentially winning, business strategy sometimes referred to as "Drag Racing." It pays off best when the bleeding edge stays uniquely valuable AND there are significant barriers, such as massive capital and infrastructure, limiting the number of competitors at that edge.
Once you're committed to playing that strategy, like committing a trillion dollars to corner scarce resources like GPUs, RAM and gigawatts, it's much less good for you if the bleeding edge gets less unique or the necessary capital/infrastructure becomes less of a barrier. Of course, being technology, your financial models assume the competitive barriers will get lower over time, but you've bet a trillion dollars the rate will be slow enough that you'll be able to extract far more than a trillion dollars from all the infrastructure you pre-bought before it depreciates to zero. If the cost barriers your margin projections rely on suddenly fall off a cliff much faster than your ~5 year depreciation schedule, THAT would be problematic, to say the least.
So, here's the rank order of a frontier lab's preferences, assuming they've already sold their soul to fund pre-buying scarce resources.
1. Your own costs get much cheaper faster than you predicted but no one else's costs change AND your customers keep paying the same high rates.
2. If you can't have #1 as a guaranteed, no-risk outcome, then you'd prefer the status quo you already planned for. Your costs and your 2-3 frontier competitor's costs roughly follow the slope your model predicts AND remain huge barriers keeping the mob of low-cost competitors away from the frontier.
3. The absolute disaster scenario would be if the cost barriers protecting you and your 2-3 frontier competitors falls much faster than you modeled and the barbarian horde is unleashed to feast on your margins before you're paid off your infrastructure. Why? Because the front runners have already sunk their costs. If they can't magically be "The One and Only" player with eternally sustainable high margins and super low costs (which is a fantasy), they're fine with #2: trading high-margin, top-dollar customers with their handful of frontier peers. High-margins going away for everyone is death to all the frontier players who've already bought the scarce resources to win a drag race.
The frontier labs have paid a fortune for the world's best AI researchers. Why didn't those researchers discover DeepSeek's early 2025 "breakthrough" before DeepSeek did? IMHO, it's because they weren't assigned to look for that kind of resource optimizing, cost reduction breakthrough. Because you wouldn't devote scarce research bandwidth looking for the kind of breakthroughs you don't want to find (and have bet a trillion $ don't exist). Especially breakthroughs which unleash egalitarian benefits that help everyone (see disaster scenario #3 above). Frontier lab's huge financial commitments to drag racing have painted them into corner where they benefit much more from research that makes models smarter at the same or higher costs than they do from research that lets models deliver the same smarts with fewer resources and costs (lowering barriers and draining moats you're counting on for ROI).
I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.
Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.
They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.