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Tech Edge: A Living Playbook for America's Technology Long Game

https://csis-website-prod.s3.amazonaws.com/s3fs-public/2026-01/260120_EST_Tech_Edge_0.pdf?Version...
1•hunglee2•1m ago•0 comments

Golden Cross vs. Death Cross: Crypto Trading Guide

https://chartscout.io/golden-cross-vs-death-cross-crypto-trading-guide
1•chartscout•4m ago•0 comments

Hoot: Scheme on WebAssembly

https://www.spritely.institute/hoot/
2•AlexeyBrin•7m ago•0 comments

What the longevity experts don't tell you

https://machielreyneke.com/blog/longevity-lessons/
1•machielrey•8m ago•1 comments

Monzo wrongly denied refunds to fraud and scam victims

https://www.theguardian.com/money/2026/feb/07/monzo-natwest-hsbc-refunds-fraud-scam-fos-ombudsman
2•tablets•13m ago•0 comments

They were drawn to Korea with dreams of K-pop stardom – but then let down

https://www.bbc.com/news/articles/cvgnq9rwyqno
2•breve•15m ago•0 comments

Show HN: AI-Powered Merchant Intelligence

https://nodee.co
1•jjkirsch•17m ago•0 comments

Bash parallel tasks and error handling

https://github.com/themattrix/bash-concurrent
2•pastage•17m ago•0 comments

Let's compile Quake like it's 1997

https://fabiensanglard.net/compile_like_1997/index.html
2•billiob•18m ago•0 comments

Reverse Engineering Medium.com's Editor: How Copy, Paste, and Images Work

https://app.writtte.com/read/gP0H6W5
2•birdculture•24m ago•0 comments

Go 1.22, SQLite, and Next.js: The "Boring" Back End

https://mohammedeabdelaziz.github.io/articles/go-next-pt-2
1•mohammede•29m ago•0 comments

Laibach the Whistleblowers [video]

https://www.youtube.com/watch?v=c6Mx2mxpaCY
1•KnuthIsGod•31m ago•1 comments

Slop News - HN front page right now as AI slop

https://slop-news.pages.dev/slop-news
1•keepamovin•35m ago•1 comments

Economists vs. Technologists on AI

https://ideasindevelopment.substack.com/p/economists-vs-technologists-on-ai
1•econlmics•37m ago•0 comments

Life at the Edge

https://asadk.com/p/edge
3•tosh•43m ago•0 comments

RISC-V Vector Primer

https://github.com/simplex-micro/riscv-vector-primer/blob/main/index.md
4•oxxoxoxooo•47m ago•1 comments

Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

2•InvoxoEU•47m ago•0 comments

A Tale of Two Standards, POSIX and Win32 (2005)

https://www.samba.org/samba/news/articles/low_point/tale_two_stds_os2.html
3•goranmoomin•51m ago•0 comments

Ask HN: Is the Downfall of SaaS Started?

3•throwaw12•52m ago•0 comments

Flirt: The Native Backend

https://blog.buenzli.dev/flirt-native-backend/
2•senekor•54m ago•0 comments

OpenAI's Latest Platform Targets Enterprise Customers

https://aibusiness.com/agentic-ai/openai-s-latest-platform-targets-enterprise-customers
1•myk-e•56m ago•0 comments

Goldman Sachs taps Anthropic's Claude to automate accounting, compliance roles

https://www.cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
3•myk-e•59m ago•5 comments

Ai.com bought by Crypto.com founder for $70M in biggest-ever website name deal

https://www.ft.com/content/83488628-8dfd-4060-a7b0-71b1bb012785
1•1vuio0pswjnm7•1h ago•1 comments

Big Tech's AI Push Is Costing More Than the Moon Landing

https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046
5•1vuio0pswjnm7•1h ago•0 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
3•1vuio0pswjnm7•1h ago•0 comments

Suno, AI Music, and the Bad Future [video]

https://www.youtube.com/watch?v=U8dcFhF0Dlk
1•askl•1h ago•2 comments

Ask HN: How are researchers using AlphaFold in 2026?

1•jocho12•1h ago•0 comments

Running the "Reflections on Trusting Trust" Compiler

https://spawn-queue.acm.org/doi/10.1145/3786614
1•devooops•1h ago•0 comments

Watermark API – $0.01/image, 10x cheaper than Cloudinary

https://api-production-caa8.up.railway.app/docs
2•lembergs•1h ago•1 comments

Now send your marketing campaigns directly from ChatGPT

https://www.mail-o-mail.com/
1•avallark•1h ago•1 comments
Open in hackernews

Units of Economics of LLMs. Reply to Ed Zitron's "AI Is a Money Trap"

6•tudorizer•5mo ago
A lot of attention is aimed at the huge investment rounds, cash burned into training foundation models (the trillions mentioned by Sam A.) and not enough analysts explain the units of economics to understand a business.

If you wonder why investors still think it’s a good idea to part with their money, I tried to break down the economic units and long term potential how all this could make sense.

Partial TL;DR

- Cash burn is not a fair approximation for COGS. OpenAI spends mostly on R&D like a pharmaceutical company does. - ChatGPT 4o could be making more than 12.8% in gross margin. - ChatGPT OSS 120B could be making 89% gross margin. It is 90% cheaper than 4o-mini with equivalent reasoning and 3x faster inference. - ChatGPT 5's gross margin is most likely to fall between 12.8% and 89%.

Full breakdown: https://medium.com/@brenoca/openais-road-to-profitability-8c7231f8494b

Comments

kingkongjaffa•5mo ago
Interesting read!

This stood out to me:

> ChatGPT 5 and ChatGPT OSS are here with the purpose of profitability

This is economically good, but it's also a signal that their capacity to moonshot is stalling either through lack of funding or lack of innovation. They're now pivoting to a more sustainable model.

Models have seen diminishing returns over the last 2 generations of model: GPT3.5 to 4o to 5.

Doubling parameter size does not double model ability/quality.

In the long term models will become commodities that can be interchanged with competitors and open source models, there's no moat, it's not likely anyone is going to sustainably have a hugely better model than the next company.

Claude Code is already showing that you can win in a niche with specialization.

I expect 3 things:

1. We won't see massive jumps on model performance again for a while without new techniques. 2. Model makers will specialize in specific use cases like claude code 3. Moonshot projects like stargate will not have outsized returns, the step change from o3/o4 models to whatever comes next will not be groundbreaking. Partly because of diminishing returns and partly because the average person is bad at explaining what they want an LLM to do.

brenoca•5mo ago
I agree 100%, very valid and fair points.

Regarding moonshot projects, I expect those to exist in light of a potential breakthrough in technique. Kind of connecting point 3 and 1 that you made.

I predict that we will see a breakthrough like we saw on transformers in 5 years from now, due to the new interest, capital (financial and human) being dedicated to this cause.

I think the best OpenAI can do is to make their product a cash cow, by reducing cost and focusing on moonshot breakthroughs to stay ahead of the curve.

Keeping in mind that it was almost 3 decades between CNNs and transformer techniques. CNNs came out in 1989 while transformers in 2017. I expect this kind of window to dramatically shorten with the renewed interest in the field.

tudorizer•5mo ago
> because the average person is bad at explaining what they want an LLM to do

Agreed. It's the saving grace for most platform which integrate LLMs even right now. Eg. v0 narrows the scope of general purpose LLMs and offers educated guides.

credit_guy•5mo ago
It's a good analysis, but I am not sure why you are spending time doing this. People who care about your company (investors, users, partners etc) are probably sufficiently familiar with AI to disregard shallow analyses like Ed Zitron's one. You know the saying: a fool can throw a stone in a pond and 100 wise men can't take it out. It's not worth spending time debunking these pieces.
tudorizer•5mo ago
Most likely tickling an own itch. Also to validate/invalidate if sanity wasn't lost.

Plus, Ed's articles have been circulated in some investment groups and nobody expressed a clear counter-point.

PS. I wasn't familiar with that saying.

dazamarquez•5mo ago
I would say this article is very shallow. Zitron criticizes what he calls the AI bubble from multiple angles, it's not just "they will never be profitable" — and I agree this would be a wild claim. Even in the worst-case scenario where AI is a giant con, as Zitron paints it, they might just become profitable if they can con enough people. I also don't expect people with a stake in any of this to read Zitron's posts and immediately stop doing what they're doing. That would be silly. I don't think Zitron writes for them, and that what he writes needs "debunking". For how I see it, Zitron mainly advocates for a more critic journalism. Regardless of whether he's right or wrong, he does attempt to critically report on AI.
tudorizer•5mo ago
Right, but critiquing with the right perspective is important. Statements about making a loss must contain the entire economic picture, otherwise they simply aren't true at some point.

A business can't be scrutinized unless the units of economics are understood.

sebastianberns•5mo ago
Thoughts on the business model of language models by Anthropic CEO Dario Amodei https://youtu.be/GcqQ1ebBqkc?si=kPRTNash6OZ6L3DX&t=992
dazamarquez•5mo ago
The piece isn't an independent analysis as the author has an obvious interest in Zitron being wrong. In fact, the piece closes off with a nice marketing self-plug. But that aside, the author doesn't actually refute Zitron's points. One of the main argument is "the comparison with Netflix is wrong", which doesn't prove anything in itself; and then tries to show that inference is profitable. Though just as in their baker analogy, you must factor in all other costs, including training new models. Worthless marketing plug.
tudorizer•5mo ago
Well ... what is independent analysis? The author is not a reporter, but someone who understands business principles.

I think you got the causality the other way around here.