>> Those differ from Dan’s essay, which engages with the literal text of Ed’s numerous predictions during 2024 and 2025 which are demonstrably invalidated by their measurable outcomes.
Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way:
- His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives.
- While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy.
- Luu and this HN crowd, today in a mob mood...completely ignore the highly unstable plumbing of the sector growth metrics. Patrick Boyle is a quantitative finance professor and former hedge fund manager, and has meticulously mapped out the mutual dependence the entire AI boom. Big Tech companies are pouring massive venture pools into AI startups, which are then contractually bound to hand that cash right back to the hyperscalers to buy cloud compute. Analysts have identified more than $800 billion in these arrangements:
- The worst of Luu logical failure, is ignoring ( on purpose? ) were Zitron numbers come from! They come from some very disciplined institutions, which Luu completely ignores. Citigroup quantitative analysts project cumulative global AI CapEx hitting $9 Trillion through 2030, with maximum global AI revenues ( not profit...) covering less than 30% of that expenditure.
- To break even on the physical infrastructure currently under construction, the AI sector needs to generate over $2 Trillion in annual end user revenue by 2030. Total actual revenue generated across the ENTIRE global AI sector today sits at a fraction, around $150 billion.
- Anthropic in a hysterical push, to make it to public markets, before the bubble bursts, recently claimed their addressable market is 30 trillion... the whole of US economy. Are we getting a post from Luu on that? This of course this ignores that MIT Professor and Nobel Laureate, Daron Acemoglu, mathematically proved that while 20% of all labor tasks are exposed to AI, only about 5% can be automated profitably due to upfront enterprise systems integration and the high financial burden of constant human in the loop verification.
- Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at....
Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness.
It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...
Chris Lattner, inventor of the Swift programming language recently took a look at a compiler entirely written by Claude AI. Lattner found nothing innovative in the code generated by AI [1]. And this is why humans will be needed to advance the state of the art.
AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art.
AI systems are trained on vast bodies of human work and generate answers near the center of existing thought. A human might occasionally step back and question conventional wisdom, but AI systems do not do this on their own. They align with consensus rather than challenge it. As a result, they cannot independently push knowledge forward. Humans can innovate with help from AI, but AI still requires human direction.
You can prod AI systems to think critically, but they tend to revert to the mean. When a conversation moves away from consensus thinking, you can feel the system pulling back toward the safe middle.
As Apple’s “Think Different” campaign in the late 90s put it: the people crazy enough to think they can change the world are the ones who do—the misfits, the rebels, the troublemakers, the round pegs in square holes, the ones who see things differently. AI is none of that. AI is a conformist. That is its strength, and that is its weakness.
With fear of sounding like a douche-bag, I honestly believe there's A LOT of incompetence in the tech-world, which permeates all layers, security companies, AV companies, OS companies etc.
I really blame the whole power-structure, it looked like the engineers had the power, but last 10 years tech has been turned upside-down and exploited as any other industry, controlled by the opportunistic and greedy people. Everything is about making money, shipping features, the engineering is lost.
Would you rather tick compliance boxes easily or think deep about your critical path? Would you rather pay 100k for a skilled engineer or 5 cheaper (new) ones? Would you rather sell your HW now despite pushing feature-incomplete buggy app ruining the experience for many many customers? Will you listen to your engineers?
I also blame us, the SWE engineers, we are waay to easily busied around by these types of people who have no clue. Have professional integrity, tests is not optional or something that can be cut, it's part of SWE. Gradual rollout, feature-toggles, fall-backs/watchdogs etc. basic tools everyone should know.
"Perhaps the worst thing about being a systems person is that
other, non-systems people think that they understand the daily
tragedies that compose your life. For example, a few weeks ago,
I was debugging a new network file system that my research
group created. The bug was inside a kernel-mode component,
so my machines were crashing in spectacular and vindic-
tive ways. After a few days of manually rebooting servers, I
had transformed into a shambling, broken man, kind of like a
computer scientist version of Saddam Hussein when he was
pulled from his bunker, all scraggly beard and dead eyes and
florid, nonsensical ramblings about semi-imagined enemies.
As I paced the hallways, muttering Nixonian rants about my
code, one of my colleagues from the HCI group asked me what
my problem was. I described the bug, which involved concur-
rent threads and corrupted state and asynchronous message
delivery across multiple machines, and my coworker said,
“Yeah, that sounds bad. Have you checked the log files for
errors?” I said, “Indeed, I would do that if I hadn’t broken every
component that a logging system needs to log data. I have a
network file system, and I have broken the network, and I have
broken the file system, and my machines crash when I make
eye contact with them. I HAVE NO TOOLS BECAUSE I’VE
DESTROYED MY TOOLS WITH MY TOOLS. My only logging
option is to hire monks to transcribe the subjective experience
of watching my machines die as I weep tears of blood.”
Dan Luu did not engage on anything more, than a disorganized wall of text, ranted like a teenager using toxic personal attacks, while obsessing over calendar errors and a placeholder in a spreadsheet. If this is what passes here for a smart engineer...Lets analyze his post in a more logical and analytical way:
- His entire argument is based on the naive logic that because LLM execution speeds or benchmarks marginally improved over the last 24 months, the entire trillion dollar investment cycle is justified. A short window of venture subsidized chip buying...tells you absolutely nothing about the multi decade debt structures, physical infrastructure depreciation, and power grid constraints that dictate whether a capital heavy business model survives.
- While he whines about Zitron numbers, fails to provide a single! macro level equation to address the real financial threat. NYU finance professor Aswath Damodaran for example, explicitly warned that the current AI build out is an asset heavy, debt funded run up backed by private capital markets. Unlike the dotcom boom which was equity funded and contained to tech shareholders today AI infrastructure burdens companies with a massive $80 billion in CapEx per gigawatt, meaning a monetization correction will trigger widespread systemic debt distress and loan defaults across the real economy.
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981
- Luu and this HN crowd, today in a mob mood...completely ignore the highly unstable plumbing of the sector growth metrics. Patrick Boyle is a quantitative finance professor and former hedge fund manager, and has meticulously mapped out the mutual dependence the entire AI boom. Big Tech companies are pouring massive venture pools into AI startups, which are then contractually bound to hand that cash right back to the hyperscalers to buy cloud compute. Analysts have identified more than $800 billion in these arrangements:
"Why Wall Street is ignoring big tech's debt" - https://news.ycombinator.com/item?id=49230630
- The worst of Luu logical failure, is ignoring ( on purpose? ) were Zitron numbers come from! They come from some very disciplined institutions, which Luu completely ignores. Citigroup quantitative analysts project cumulative global AI CapEx hitting $9 Trillion through 2030, with maximum global AI revenues ( not profit...) covering less than 30% of that expenditure.
- To break even on the physical infrastructure currently under construction, the AI sector needs to generate over $2 Trillion in annual end user revenue by 2030. Total actual revenue generated across the ENTIRE global AI sector today sits at a fraction, around $150 billion.
- Anthropic in a hysterical push, to make it to public markets, before the bubble bursts, recently claimed their addressable market is 30 trillion... the whole of US economy. Are we getting a post from Luu on that? This of course this ignores that MIT Professor and Nobel Laureate, Daron Acemoglu, mathematically proved that while 20% of all labor tasks are exposed to AI, only about 5% can be automated profitably due to upfront enterprise systems integration and the high financial burden of constant human in the loop verification.
"A new look at the economics of AI" - https://mitsloan.mit.edu/ideas-made-to-matter/a-new-look-eco...
- Dismissing the AI bubble thesis, because you found a spreadsheet typo in a newsletter, and ignoring the other voices who are aligned with Zitron core premise, means you are also dismissing the research of a Nobel Laureate in economics, the Dean of Valuation, veteran hedge fund managers, Barclays, S&P Global, and Citigroup. Arguing that "the models are hitting benchmarks" while ignoring that the physical balance sheets and enterprise budgets cannot support a multi trillion dollar infrastructure build out, is exactly the type of Dunning Kruger this corner excels at....
Ed Zitron is correct, despite the clumsiness or unpleasantness of his message delivery, and this community reaction, will be an historical record of the AI bubble crowd madness.
It took years to take down Maddoff, and more to take down Bear Stearns. It will take maybe 5 - 10 years of "Ed Zitron is wrong posts here" until Anthropic and OpenAI have to be bailed out by the US government, but the day of reckoning will come. The end of this universe is all tax payers will own a piece of AI and will pay for it with increased interest rates for the next 25 years...