This case study reveals the future of AI-assisted[1] work, far beyond mathematics.
It relies on a combination of Humans, LLMs ('General Tools'), Domain-Specific Tools, and Deep Research.
It is apparent that the static data encoded within an LLM is not enough; one must re-fetch sources and digest them fresh for the context of the conversation.
In this workflow, AlphaEvolve, Aristotle, and LEAN are the 'PhDs' on the team, while the LLM is the Full Stack Developer that glues them all together.
[1] If one likes pompous terms, this is what 'AGI' will actually look like.
> Aristotle integrates three main components: a Lean proof search system, an informal reasoning system that generates and formalizes lemmas, and a dedicated geometry solver.
[from elsewhere on how part 2 works]
> To address IMO-level complexity, Aristotle employs a natural language module that decomposes hard problems into lists of informally reasoned lemmas. This module elicits high-level proof sketches and supporting claims, then autoformalizes them into Lean for formal proving. The pipeline features iterative error feedback: Lean verification errors are parsed and fed back to revise both informal and formal statements, iteratively improving the formalization and capturing creative auxiliary definitions often characteristic of IMO solutions.
> same Mike in an org wide email: thank you all for your support. Starting next week I will no longer be a developer here. I thank my manager blah blah... I will be starting my dream role as Architect and I hope to achieve success.
> Mike's colleagues: Aww.. We'll miss you.
> Mike's manager: Is this your one week's notice? Did you take up an architect job elsewhere immediately after I promoted you to architect ?!
Kind of tells you something about how timidly and/or begrudgingly it’s been accepted.
IMO the attitude is warranted. There is no good that comes from having higher-level code than necessary at the kernel level. The dispute is whether the kernel needs to be more modern, but it should be about what is the best tool for the job. Forget the bells-and-whistles and answer this: does the use of Rust generate a result that is more performant and more efficient than the best result using C?
This isn’t about what people want to use because it’s a nice language for writing applications. The kernel is about making things work with minimum overhead.
By analogy, the Linux kernel historically has been a small shop mentored by a fine woodworker. Microsoft historically has been a corporation with a get-it-done attitude. Now we’re saying Linux should be a “let’s see what the group thinks- no they don’t like your old ways, and you don’t have the energy anymore to manage this” shop, which is sad, but that is the story everywhere now. This isn’t some 20th century revolution where hippies eating apples and doing drugs are creating video games and graphical operating systems, it’s just abandoning old ways because they don’t like them and think the new ways are good enough and are easier to manage and invite more people in than the old ways. That’s Microsoft creep.
> Forget the bells-and-whistles and answer this: does the use of Rust generate a result that is more performant and more efficient than the best result using C?
The Burry short is just one data point, but the "facts we know" are piling up fast.
Here is a possible roadmap for the coming correction:
1. The Timeline:
We are looking at a winter. A very dark and cold winter. Whether it hits before Christmas or mid-Q1 is a rounding error; the gap between valuations and fundamentals has widened enough to be physically uncomfortable.
The Burry thesis—focused on depreciation schedules and circular revenue—is likely just the mechanical trigger for a sentiment cascade.
2. The Big Players:
Google: Likely takes the smallest hit. A merger between DeepMind and Anthropic is not far-fetched (unless Satya goes all the way).
By consolidating the most capable models under one roof, Google insulates itself from the hardware crash better than anyone else.
OpenAI: They look "half naked." It is becoming impossible to ignore the leadership vacuum. It’s hard to find people who’ve worked closely with Altman who speak well of his integrity, and the exits of Sutskever, Schulman, and others tell the real story.
For a company at that valuation, leadership credibility isn’t a soft factor—it’s a structural risk.
3. The "Pre-Product" Unicorns:
We are going to see a reality check for the ex-OpenAI, pre-product, multi-billion valuation labs like SSI and Thinking Machines.
These are prime candidates for "acquihres" once capital tightens. They are built on assumptions of infinite capital availability that are about to evaporate.
4. The Downstream Impact:
The second and third tier—specifically recent YC batches built on API wrappers and hype—will suffer the most from this catastrophic twister.
When the tide goes out, the "Yes" men who got carried away by the wave will be shouting the loudest, pretending they saw it coming all along
I don't believe your comment is just a direct dump out of an LLM's output, mainly because of the minor typo of "acquihires", but as much as I'd love to ignore superficial things and focus on the substance of a post, the LLM smells in this comment are genuinely too hard to ignore. And I don't just mean because there's em-dashes, I do that too. Specifically these patterns stink very strong of LLM fluff:
> leadership credibility isn’t a soft factor—it’s a structural risk.
> The Timeline/The Big Players/The "Pre-Product" Unicorns/The Downstream Impact
If you really just write like this entirely naturally then I feel bad, but unfortunately I think this writing style is just tainted.
I don't think this entire piece taught me anything new other than a speculation that "speculation is mounting that Tim Cook may be preparing to step aside as CEO" --
"speculation". "may be". "preparing".
The lagging behind the AI wave is known, and matter of fact, the "Liquid Glass" saga is not even mentioned while they focus on the Apple Vision glasses.
This is a great model for the poor low quality of journalism that became industry standard nowadays.
Yes, apple direction is questionable, and while it is mainly questionable because the of AI wave, well, the entire AI wave is questionable nowadays.
one more thing, the URL path has "/apple-tim-cook-leadership-changes" in it, suggests the title "what the heck" is most likely a newer version than the original one which they decided not to publish as is since it is not based enough.
I gave Replicate a shot but needed to run on my own GPUs, so I initially used Cog to port the workload.
I quickly realized Cog was an obstacle rather than an accelerator. I replaced it with a lightweight FastAPI layer, which immediately unblocked me:
1. Native I/O with Google Cloud Storage.
2. Freedom to use the latest Torch and Nvidia Docker images without abstraction overhead.
3. Running Torch and TensorFlow in parallel (legacy model constraints that Cog struggled with).
It forces the question: What is Replicate's value proposition for a startup where the founders are competent engineers? If you aren't afraid of a Dockerfile, the "ease of use" premium evaporates.
The answer to that question is likely this acquisition.
The standalone AI middleware market is precarious; the landscape shifts too fast and technical founders will eventually outgrow the training wheels.
Folding into Cloudflare gives the team a sustainable home to leverage the platform's scale, rather than competing solely on a container abstraction layer.
Wish them the best. Cloudflare’s infrastructure is likely the right environment to turn this into a high-leverage product
Me too. After trying it out I found Cog to be super frustrating to use and the only use case for me ended-up being trying out a new model through the web UI occasionally.
The Burry short is just one data point, but the "facts we know" are piling up fast. Here is a possible roadmap for the coming correction:
1. The Timeline: We are looking at a winter. A very dark and cold winter. Whether it hits before Christmas or mid-Q1 is a rounding error; the gap between valuations and fundamentals has widened enough to be physically uncomfortable. The Burry thesis—focused on depreciation schedules and circular revenue—is likely just the mechanical trigger for a sentiment cascade.
2. The Big Players:
Google: Likely takes the smallest hit. A merger between DeepMind and Anthropic is not far-fetched (unless Satya goes all the way). By consolidating the most capable models under one roof, Google insulates itself from the hardware crash better than anyone else (especially in light of TPU obvious advantages).
OpenAI: They look "half naked." It is becoming impossible to ignore the leadership vacuum. It’s hard to find people who’ve worked closely with Sama who speak well of his integrity, and the exits of Sutskever, Schulman, and others tell the real story. For a company at that valuation, leadership credibility isn’t a soft factor—it’s a structural risk.
3. The "Pre-Product" Unicorns: We are going to see a reality check for the ex-OpenAI, pre-product, multi-billion valuation labs like SSI and Thinking Machines. These are prime candidates for "acquihres" once capital tightens. They are built on assumptions of infinite capital availability that are about to evaporate.
4. The Downstream Impact: The second and third tier—specifically recent YC batches built on API wrappers and hype—will suffer the most from this catastrophic twister. When the tide goes out, the "Yes" men who got carried away by the wave will be shouting the loudest, pretending they saw it coming all along.
There was a time when posts at HN where at the quality of a printed magazine. And so was a guy, named Lim Cheng Sun who was designing and printing (!) and shipping the best reading material, best stories from the startup ecosystem.
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