Deedy Das 提出 Neolab 的多头逻辑：算力是 Helmer 式"垄断资源"，当前存在获取算力与资本的窗口期，未来资金或算力价格可能恶化，竞争将更难。大实验室受创新者困境制约，难偏离编程现金牛、难自我蚕食收入、难追小于 $1B 的机会。许多 Neolab 已在产生可观收入，且人才认为其财务上行空间更大，至少 5-6 家大型潜在收购方。
If you thought this was a bearish view on neolabs, let me write the bull case.
1. Compute is a great case of Helmer's cornered resource. There is a window of time where you can acquire a lot of compute and capital. In the future, both funding might dry out or compute prices might continue to rise. It could get very difficult to compete.
2. Big labs will suffer from innovator's dilemma. They're unlikely to:
a) deviate too much from their cash cow, coding
b) ship anything that cannibalizes revenue
c) go after opportunities that incrementally look too small (<$1B)
3. Empirically, many neolabs are working and generating significant revenue. They're just not being loud about it. Admittedly, it's still very much early days.
4. A lot of talent sees more financial upside in neolabs than big labs. Whether the companies show signs of life or fail entirely, there are at least 5-6 large promising acquirers.
All startups are difficult and the point of the original post was to illustrate specifically the difficulties of running businesses with massive upfront compute purchases, not say it's impossible.
The economics of a Neolab. A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem. To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW. Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining. If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model. For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference. Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money. Add to that insane cost of talent. So what can you do with the compute? - Not play the model game at all. - Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing - Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry. If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend. It is a difficult game.在 X 查看被引用的帖子
来源：Deedy · x.com