For all the lies, bad behavior, and broken promises, SpaceX's achievements and reliability record is still incredible, X/Twitter hasn't crashed and burned after all the layoffs and drama, and Tesla (until recently due to his meddling) had a lock on the leading the car industry's direction & doing a lot to drive practical electrification globally.

I think this is definitionally wrong, but okay. We're talking about the price of supply chain components, not assets or speculative commodities. Tulip valuations were entirely driven by speculation. Housing bubbles are largely driven by speculation with cheap credit. Railway mania was about a stock market bubble, not the price of rail itself.

> Forecasts do not make it so.

In publicly traded companies, they tend to be accurate. Are you suggesting that NVIDIA has given improper forward guidance and is about to be subject to major shareholder lawsuits? That's a major short sell opportunity, and I suggest you get on that!

> Inference is massively subsidized. The demand is fictitious just because of that. Once prices go up, especially once free or cheap inference dries up, demand will collapse.

This is, kindly, incredibly wishful thinking. It may be true for individual companies that are stretching themselves too thin. On the other hand, Anthropic claims they are not subsidizing inference (they have raised prices - it hasn't slowed demand) and will become profitable around 2028 as training costs for LLMs have likely peaked. We'll see.

> It hasn't. For all the claims that AI has made any given job so much easier, developers who claim "It'd have cost me a billion years to do so" (next time bring a counterfactual), the actual economic benefit appears to be a big fat zero. We're right back to the Solow paradox.

... I think you may misunderstand the Solow paradox. Paul Strassmann's work on information productivity and "The Squandered Computer" covers a lot of this: there is no correlation between IT investment and productivity or profitability on the firm level, and a serious lag in overall productivity statistics until the 90s, because such gains are only achievable through alignment to meaningful business goals and structural change on processes. It's only when impactful practices become ubiquitous that they reflect in the statistics. The same will be for AI. Which is why I don't think that AI will be as fast an economic transformation as its proponents believe. But it will take place, unevenly to start.

> So much of the demand for inference is driven by hype.

We agree there.

> Companies using AI in the expectation of an ROI that has not materialized, and in many places, is very unlikely to.

We agree there too.

> Where will these tens of trillions come from if the aggregate economic benefit doesn't exist? Joe Slopman making a dozen CRUD apps a week for half a million in revenue, but there ain't a million of him.

Compressed project capital requirements and schedules, and compressed operational expenditure & time for many business processes. I've seen this myself before AI, 25+ years ago, with business process management and enterprise resource planning - think Peoplesoft, SAP, etc., replacing armies of paperwork processors. Same for accounting systems in the 60s and 70s.

Another area I have deep familiarity with - "precision railroading" pioneered by Hunter Harrison (a mix of locomotive distributed power technology, classification yard technology, IT systems, lean thinking, and management insight) has led to the most effective freight delivery times we've seen and productivity gains that many thought would be impossible (operating ratios in the sub-60s at times). This took decades to tweak and "get right" and is still subject to to many problems (safety issues, crew stress, and reliability delays to smaller customers due to a lack of slack in the system).

Of course, it takes insight, discipline, and design to extract technological gains on an individual firm or agency basis, which is why this stuff doesn't show up in the aggregate statistics until many years afterwards. Business intelligence systems also improved production and market decision making in the 90s and 00's, but that's hard to trace back to IT improvements.

I think with AI it is another round of major IT improvements in industries that were previously less susceptible to such change, and as such it will definitely be a multi-trillion dollar impact. But... It will be a mixed bag for a while - much longer than the AI CEOs would have you believe. I don't think this will lead to a crash, but I do think component shortages will eventually end (towards 2028-2029). Some will find significant benefits with it through expressive prompting, appropriate agent structure & guardrails, and deep contexts... and it will take a while for that know-how to spread.

> In no small part because any "efficiency" or "productivity" gains realized by AI immediately drives down the cost of the good or service produced.

I think there's significant lag on that point.

I had a belly laugh. They're trying so hard to be like Apple with these, but without the clear explanation of user benefit.

If you want to use Bambu's software against their TOS, OK you wouldn't be alone in that, but there's no moral high ground in it.

Of course, many prefer to break their license agreement because They Really Want It, in effect daring Bambu to get aggressive with license enforcement. They probably won't...

In AI, buyers are getting what they want. The demand is real. YOU might not value what they're getting, but that doesn't make it fraudulent.

This is why people misunderstand why AI isn't a bubble. A bubble is asset prices rising due to speculative demand far beyond what the actual demand is.

AI - specifically chip and memory markets feeding AI - is a demand shock on par with World War 2 in its impact. NVIDIA is legitimately forecasting demand of $1 trillion in their chips+memory by the end of 2027.

This is actual, real, shipping physical product: not vapor, not something that will disappear, not something that will "crash" suddenly.

Yes, there is some speculation among AI providers training new models in the race to AGI, but that is not the majority of demand, inference is 65-80% of demand. If the current pace of training slows, that excess capacity for training will get easily sorted out through resale markets.

The world has changed.

> A capitalist society is one where people assemble different forms of capital to produce capital returns that are larger than the sum of the capital inputs, where the possibilities available to you depend on the amount and quality of capital that you have access to.

The reason we live in post-capitalism is that capital is largely abundant these days, although many regional and cultural barriers remain due to bias, prejudice and risk aversion. But is no longer the determining factor of economic growth - necessary but not sufficient.

> This is all still very relevant when discussing human capital - access to human capital is determined by the quality of your professional networks, whether you decide to be present in geographic talent clusters (i.e. cities as centers of industry), and whether you have sufficient financial capital available in trade.

I feel this is a stretch. In a non-capitalist economic system, for example, the wealth of the collective is arguably also bounded by the scarcity of knowledge in that collective. Knowledge does not have the same properties of capital accumulation that Marx described.

> AI will not transition us to a post-capitalist society. Its promise is solely the ability to replace human capital with other forms: chips and electricity. It does not spell the death of human labor any more than computers and spreadsheets did for accountants.

Spoken like someone who didn't even read my parent comment. We are already living in a post-capitalist society, and have been for several decades.

"Chips and electricity" is reductio ad absurdum, and ignores a vast number of other input factors. AI will not eliminate labor or human capital, that's just marketing. It certainly will transform it as other tools have.

Humans with many agents will be more productive, but the tendency has been for these models is to regress to the mean when it comes to strategic insights.

A mix of political scientists, politicians, investors, entrepreneurs, lawyers, judges, scientists, technologists, and economists have tried to mold society to their own theoretical vision for at least 150+ years. Society then reacts to that in both good and bad ways. This distorts the vision, as society changes it to its concerns. And the cycle repeats.

I think of Karl Polyani's The Great Transformation has a great way of looking at the attempts to force "market society" on England in the 1700 and 1800s, and the reaction that all societies exhibit in the face of unconstrained technological or economic change. Both the imposition of change and reaction to it can be violent, it's hard to predict. We've had such a relatively steady state since WW2 in the developed nations that we're not used to this cycle.

Knowledge is a unique resource compared to the other traditional factors of economic production (land, labor, and capital). It is often invested in with capital (education and tools), but it is carried with the human, and leaves with them. It is always decaying - knowledge workers should be in constant learning mode, and stale knowledge eventually becomes a drag on performance.

I'd argue the future is about knowledge workers all becoming managers. When you use agentic AI, it has the flavor of the skills of management. Management is "a practice and a liberal art", according to Drucker, one that has been in poor supply for some time. LLMs are have somewhat stale knowledge and require the human, tools, and RAG to freshen it. And LLMs will always regress to the mean. It is pretty good at pattern analysis and starts to get shaky and mediocre with synthesis. It requires very nuanced, and elaborate prompting to shape its token output towards insightful results that aren't a standard answer. For coding exercises, that can be fine, but at high complexity levels, or when dealing with issues of strategy or evaluation, it is a platitude generator and has no unique competitive advantage.

In other words, competent, talented management mixed with knowledge work is the scarcity we are heading towards. This is arguably why you're seeing the rise of "markdown frameworks" that people swear improve performance, it's the beginnings of management scaffolding for AI.

Technical folks struggle with valuing management skills, and I expect this will increase its value and scarcity.

As for "Physical robustness. Strength, perhaps brutality. Competence in physical tasks." I think the robots will be replacing that pretty shortly.

"Honesty. Parentage. Birth order (see primogeniture.) Those matter in per-technological societies, and they matter in failed societies now. Those are perhaps humanity's core values."

Ehhhhh not really? What about Christianity, where the meek shall inherit the Earth, and love is the core value (putting aside modern day Pharisees and Charlatans that twist the underlying value system)? Or Islam, whose core value is submission to God? While there have been Societies that valued parentage and birth order, that's far from universal.

I had thought that months aren't quite a human construct, they correspond roughly to lunar cycles. Weeks were a way to carve the month up into the four lunar phases per cycle.

Seconds, minutes, hours, etc. are, as you say, all sexagesimal math bias.