That is exactly the point. These circular deals artificially increase the earnings of company and as a result artificially decrease price–earnings ratio.
What kind of sources do you like to read? Do you have a blog or publish anything? I keep on thinking about this comment and want to hear more of your perspective.
The amount of coping, seething etc at the fact that Nvidia is hilariously profitable leads to some of the funniest cognitive dissonance I’ve seen on the internet.
The problem is - what happens when the AI bubble bursts for whatever reason, and suddenly NVIDIA has to deal with a lot of its high-margin business going away? With its outsized weight amongst most indices and consequently ETFs, even a small percentage of value correction will wipe out a lot of wealth.
We've seen that with the dot-com bubble in the past, a lot of the "dark fiber" that we use and rely on today was gotten for incredibly cheap after the collapse of debt-fueled customers and, subsequently, the collapse of the ISPs.
That scenario is what I am afraid of repeating, partially because a lot of the market (especially at the tail end, such as datacenter companies investing into buildouts, and consequently construction companies investing in machinery and staff) is fueled by debt and a domino-level collapse can trigger another cascading debt default crisis that can then send off banks into failure.
Prodia | Fastest Inference API | London / REMOTE (US or Europe) | Software Engineer
Engineers at Prodia build the systems that make image + video generation fast, reliable, and easy for developers to use.
This is a highly practical role: shipping models, features, and improvements that show up directly in latency, quality, and uptime.
Sample projects include…
* Optimising, testing, and integrating frontier image/video models (research -> production)
* Building the best APIs for developers: clean ergonomics, great docs, predictable behaviour
* Designing evals + observability: catching regressions, ranking outputs, and understanding quality changes
* Improving our distributed job scheduling (throughput, latency, fairness, cost)
* Reading the latest papers to gather methods for future improvements
* Using the newest AI/agentic tooling to speed up debugging, experimentation, and iteration
You might be a fit if…
* You’re an excellent programmer and love shipping quickly
* You're a first principles thinker who is unfazed by hard problems
* You're looking to learn more about machine learning at a rapid pace
* No specific degree or background required — show us what you’ve built and how you think
Please email me: monty at prodia dot com. If you're in London, I'd love to buy you coffee and learn more about you.
Zig has been relatively stable for the past few years for the main Zig code.
What has changed the most is the `build.zig` build system (which we aren't using).
We are also looking ahead at Zig roadmap, and trying to anticipate upcoming breaking changes,
and isolate our users from that.
