Ed Zitron loses his mind annotating an AI doomer macro memo

> Commented [1]: what if pee pee was poo poo

Rarely do I read something that starts off with such promise!

The financial market things are over my head and I don't have a dog in the game, but I think "Nobody is replacing salesforce with their internally vibe coded software" is just false? Both taken literally [0] [1] and as denying the general trend. Just in my company we already replaced WMS software subscription with own solution, and I wouldn't be able to write it fast enough and maintain it by myself without the use of Claude Code. I'd say "Not perfectly or with every edge case handled, but well enough that the CIO reviewing a $500k annual renewal started asking the question “what if we just built this ourselves”" is an accurate description.

[0] https://lovable.dev/blog/how-a-startup-replaced-a-salesforce...

[1] https://seekingalpha.com/news/4144652-klarna-shuts-down-sale...

I do enjoy a good Ed Zitron sneer. The fact that the original article moved markets says a lot about the critical thinking skills of stock market traders.

The original memo: https://www.citriniresearch.com/p/2028gic

HN discussion: https://news.ycombinator.com/item?id=47114579

Ed's main thesis is that cost is unsustainable for AI companies but this is clearly wrong.

The unit cost is going down and has gone down by more than 20-30x over the years. Sure, the fixed cost of training is going up but that's because of the implied returns. Once the returns to training don't happen, it would simply reduce modulo cutoff date updates. The companies have a choice to just stop training and focus on inference cost reduction.

What am I missing here? Unless the consumers decide that they are no longer willing to pay the same amount as before and their expectations are rising with prices falling, what else?

Offtopic - The success of coding agents must be Ed Zitron's nightmare.

He has been a perpetual bear

I've started to feel like Ed Zitron is actively hurting people I care about.

I'm lucky to have worked in the field for a long time, and be able to spend a lot of tokens. In the last month it's become clear to me that the tech works. The science is done, and what's left is engineering.

There are a lot of risks and mitigations and theory to build, but it's all solvable. The tech isn't mature, but neither was the Internet 30 years ago. And we built transatlantic cables and ran new wires to everyone's house.

People I care about, engineers with 20 years of experience, are having mental health breakdowns, caused by Zitron's work. They insist the tech will never work, and avoid learning about it, becoming progressively more paranoid and isolated. I'm trying to be supportive and help them start to recover, but it's slow going.

If someone is having a crisis about this, I hope they start talking to a therapist. I don't need them to agree with me, but I do need them to not harm themselves.

> "What if our AI bullishness continues to be right...and what if that’s actually bearish" - what if pee pee was poo poo

Despite the vulgarity, it is exceptionally illuminating to how much some of these slop pieces are just a mere pretension of rhetoric. I see this pretty consistently with a lot of the material I come across on the job that's gone through the LLM meat-grinder.

Also, the comment made me giggle like a little kid.

I've also heard Cory Doctorow recently offer a similarly dismissive view, describing AI as "just statistics".

Only those with no understanding of how multi-nationals compliance work think that replacing Salesforce or Monday with internal development systems, even with AI assistance tooling, is a reasonable use of their engineering's time.

Salesforce, SAP, etc exist for a reason.

Related:

An AI doomsday report shook US markets

https://news.ycombinator.com/item?id=47138860

He is funny and entertaining but does he provide constructive investment advice? I am not so sure.

Okay, I'm tired of reading the debate about costs going down and therefore Ed is wrong. The cost of running the inference is not the problem. The cost of the input CHIPS is the problem. Let's return to Dario Amodei's interview [0] with Dwarkesh, shall we, for AI Economics 101?

Here goes:

The Epoch data everyone keeps citing measures the price per token charged to API customers. That's the sticker price. It tells you nothing about whether the business is viable, because the existential risk for AI companies isn't the marginal cost of running a query. It's the upfront capital expenditure on chips and datacenters, committed years before you know what demand looks like.

Anthropic CEO Dario Amodei spelled this out in his Dwarkesh interview. Here's the short version: 1. Data centers take 1-2 years to build out. 2. Each gigawatt costs roughly $10-15B per year. 3. The industry is currently at ~10-15 GW, scaling roughly 3x annually. 4. By 2028, ~100 GW. By 2029, ~300 GW. 5. We're talking multiple trillions per year in committed infrastructure spend across the industry.

Now NVIDIA's Q4 earnings [1], which printed today: 1. $68.1B in quarterly revenue, $62.3B from data center alone. 2. Full-year: $215.9B, up 65% YoY. Guiding $78B next quarter. 3. Someone is writing those checks. Those checks are not refundable.

Dario, who believes we're 1-3 years from a "country of geniuses in a data center," described his own demand prediction as a "hellish" problem.

His exact framing: If this revenue comes in at $800B instead of $1T, "there's no force on earth, there's no hedge on earth" that could stop him from going bankrupt if he'd bought compute at the higher projection.

He's at ~$10B annualized revenue today, and he won't commit to buying at the scale his own thesis demands, because being off by a single year is fatal.

This is the actual argument (I'm not saying this is Ed's argument, but this is the argument against these companies). Not "inference tokens are expensive."

The argument is structural: these companies must pre-commit billions in non-recoverable CAPEX based on demand projections that are, by the CEO's own admission, a coin flip.

The gross margins on serving tokens might be great. But the training spend for next-gen models grows exponentially, and it has to be funded before that model earns a dollar.

The Epoch chart measures what customers pay per token. It doesn't measure the $215.9B NVIDIA invoice those customers collectively funded this year, or that these chip purchases are one-way bets against future demand that may or may not materialize.

Inference costs going down 20x is wonderful for consumers. It tells you almost nothing about whether the companies making those chips, or the companies buying them, will survive the demand prediction gauntlet.

And if we're being honest, the Epoch data showing 9x to 900x price drops per year should make you more nervous, not less, because it means the asset you bought last year is depreciating at a rate that makes used cars look like gold bars.

[0] https://www.youtube.com/watch?v=n1E9IZfvGMA&t=2298s [1] https://nvidianews.nvidia.com/news/nvidia-announces-financia...

Where does the title come from?

What is this document?

What is the context?

It’s sort of disappointing to me how on both sides it seems hard to have any sort of rational perspective. I find both the Citrini memo (and the subsequent market reaction) and Ed Zitron’s critique of it to be wildly off-base.

I wish everyone would just calm down a bit.

Good to see people are finally turning against this grifter.

"AI fake, AI poo poo, AI going away!" is the only argument he ever had. Nothing more.

Ed Zitron, from what little I have heard of him, seems incredibly irrational. I don't think I've ever seen anybody stick their head deeper in the sand more than I've seen him do.

It's one thing to dislike or even detest something, but to constantly claim it is worthless and without use when people are already benefitting from it everyday is nothing short of delusion.