It's more insidious than that. These IPOs aren't being rushed, they were waiting for all the pieces to be in place to force 401ks and other retirement plans to buy these IPOs.
The most recent change was the NASDAQ adopting the "fast change rule" which allows newly IPO'd companies to be listed in the index after only 15 days of trading. This rule was decided March 30, 2026 and only came into effect May 1, 2026.
The plan is to rapidly drive these prices up in the first 15 days, get the companies listed in the NASDAQ so funds are forced to purchase them at higher prices, then leave retirement accounts holding the bag.
> The most recent change was the NASDAQ adopting the "fast change rule" which allows newly IPO'd companies to be listed in the index after only 15 days of trading.
Official justification, and other changes besides timeframe, e.g.:
> First, eligibility and company size. As multi‑class share structures have become more common, we now consider both listed and unlisted shares when determining eligibility and ranking. This allows the index to reflect a company's full economic size, while index weighting remains based solely on listed shares. This change affects who qualifies for inclusion, not how constituents are weighted.
> A new method to calculate the market capitalization of companies to determine their eligibility for inclusion in the index. It involves adding listed stock and unlisted shares that are part of different share classes. Scrapping a rule that requires companies to float a minimum 10% of their shares. Companies with a low float will receive a lower weighting on the index. […]
As unlikely it is to happen at scale, as a thought process - what would happen if people start selling those index funds in a mad rush? Just drives the transaction volume because those with that new money will just buy something else in the market?
I know SpaceX, Anthropic, and OpenAI will probably be a drop in the bucket in terms of scale of these funds, (free float % etc). But, is it realistic to take the money out of index funds for a bit until the price of these new stocks come crashing eventually?
If people actually dumped index funds for cash en masse it would be catastrophic. To attach some numbers, MSFT averages about 35M shares in daily volume, and that includes all the market makers, HFTs, etc. BlackRock (iShares) owns 593M shares of MSFT and Vanguard owns another 482M. Together, the amount of shares that index funds own is about a month and a half of total trading volumes. I'd bet that such a crash would unfold over about 2-3 days, which brings up the specter of stocks literally going "no bid", where there are not enough buyers for every seller to sell, at any price.
Likely the government would step in and inject cash directly into the markets to support them in such a scenario, because a broad-index stock market crash is the modern-day bank run. Retirees carry the bulk of their savings in the form of stocks; if it disappears, we'd likely face revolt.
Same old story of too big to fail. The government will "inject cash", that is borrowed, so that retirees 401k accounts don't go down. But who pays back the borrowed funds? The non-retirees. Everything is optimized for the boomer generation to be fine, who cares about anyone else?
If you hit sell on a vanguard ETF and it sells on the market, then Vanguard isn’t the buyer is it? So in that situation with everyone dumping ETFs there would be a lag on the time taken for the ETF to sell and Vanguard to then dump the stocks back out in the market. It’s never occurred to me the situation where huge numbers of people dump index funds and how Vanguard/Blackrock account for that without becoming bag holders of the underlying stocks themselves.
In any case, I’m not sure that large enough numbers of ETF holders are sitting close enough “to the button” to hit sell in the event of a sharp downturn occurring over the space of even a week or two. And a lot of them would see it as an opportunity to DCA into the dip anyway.
If it's an ETF it's a little complicated. The usual mechanism for selling an ETF is that there's a buyer on the other end who's buying shares in the ETF itself, not the constituent stocks. Arbitrage keeps the price in line with the index constituents; if the ETF diverges from its constituent assets, some HFT can buy the ETF and sell the constituents and that will force them to converge.
However, most ETFs are also setup such that they can create or destroy shares in response to large shifts in demand. In this case, if enough people hit sell, the ETF itself will buy back shares and use the proceeds to sell the underlying assets, in a transaction that mechanically should be market-neutral and just propagate the supply/demand of the fund down to the individual stocks.
With Vanguard specifically, it's even more complicated, because VTI is not a separate ETF. It's a share class of the Vanguard Total Stock Market Index Fund. But the mechanism is largely the same - it has the same Authorized Participant system to mint new shares in case of high demand and redeem shares if everybody sells, and then passes these requests on to the underlying mutual fund, which can then piggyback on some of the tax efficiency benefits of the ETF.
While unlikely to happen at scale, by way of anecdata I'll say that I and my extended family have almost all shifted money away from funds that are heavily coupled to the fate of GenAI.
The bottom is going to fall out of the market and it's going to take years to recover, I don't see any reason to suffer through that (and neither do my retirement-age relatives).
I'm after steady gains in an approximately efficient market, not a wildly unsustainable speculative boondoggle, thanks.
So you’re still hedging or you 100% fled AI? I presume you have gone to a broader portfolio. But if tech crashes doesn’t everything? And isn’t tech holding up the entire market so they won’t let it happen? And how can you even avoid GenAI if people are cramming it into everything and it’s constantly shocking sectors of the market?
Not OP but I’m in a broad-based Euro index so I gain on the stocks and on the fact that the dollar is going to shit. I haven’t seen the enormous AI-juiced gains that have become commonplace but I’m also insulated from commodity hardware companies trading like rocket ship startups and whatever ends up coming out of that insanity.
Somebody is going to have to explain the business case for Micron trading like it’s Google. We all know that fabs are a low-margin capital intensive business, right?
These stocks crashing (not saying it will or won’t happen), means AI is crashing, and that will be a much broader selloff than these 3. Add Microsoft, Micron, Amazon, Oracle, Nvidia, Supermicro, Dell, etc, any company that has direct or indirect exposure to the massive AI boom (and possibly their lenders).
> CRSP indexes were also recently changed to better accommodate fast entry. New IPOs are eligible for CRSP's suite of indexes after five trading days, provided they pass the index's eligibility and investability screens. Previously, these screens included having at least 10% of shares qualifying as freely tradeable (known as float shares outstanding, or FSO). However, in April the methodology changed to allow stocks with either 10% FSO or approximately $3.3 billion in float-adjusted market capitalization to be eligible for index inclusion. The weighting of stocks in CRSP indexes is also based on free float, which should help address the investability challenges associated with thinly traded stocks.
Total market indexes and target date funds will include this and SpaceX on float adjusted basis I believe. The blast radius is much larger than funds that track the NASDAQ directly.
But isn't that what "total market" means? I don't see how if you invest in a total market fund you could declare "except for SpaceX, Anthropic and OpenAI". Why is it so bad for these accounts to be invested in these companies anyway? Seems pretty typical, i bet all kinds of companies are added to total market indexes each year.
Until recently companies that IPOed weren’t immediately added to the major indexes so there was a longer period for price discovery. This year that changed; so you have retirement funds that typically are more conservative acting as exit liquidity for these massive IPOs.
I would have less of an issue if the inclusion in major indexes was delayed 6-12months but we are looking at inclusion within like 5 days for some of these indexes.
The float will get bigger as you wait tho, since it's common for early investors to be locked for e.g. 6 months. You can argue it's better to smooth the entry as float gets unlocked rather than being front run by all the hedge funds in a single day on a massive capitalization.
Very true. Anthropic just raised money at the end of last week.
There's no way they could have done that without telling those investors the S-1 was prepared and awaiting their signature on the round before they hit Submit, so to speak.
almost all 401k plans offer funds based on s&p 500, not nasdaq/russell others. s&p has also halved their trading days requirement from 1 yr to 6 months, but that's still sufficient to be past the post-ipo lock-up period.
> Stocks would become eligible for the index after six months rather than 12 months. The requirement to have a minimum Investable Weight Factor of 0.10 (roughly at least 10% of shares publicly floated) would be dropped. Companies would not be required to demonstrate profitability.
> Still, S&P Dow Jones reminds market participants that the proposed changes would apply only to index eligibility. The actual inclusion of new constituents remains entirely at the discretion of the index committee.
Until the inevitable crash in price when the lockup of employee shares end and they dump their shares onto the market. These fresh companies shouldn’t be included into passive investing securities until 180 days at least. It’s just making the public bag holders.
Hedge funds won't try to short the stock; the holders are almost all institutional investors and insiders who are long on the stock and have no reason to lend them to HFs betting on a price decline.
What they might do is trade bespoke instruments like a credit default swap on datacenter construction deals. Stays underneath the radar of politicians and tech insiders who are invested in a particular outcome.
>The plan is to rapidly drive these prices up in the first 15 days, get the companies listed in the NASDAQ so funds are forced to purchase them at higher prices, then leave retirement accounts holding the bag.
Dumb question: why couldn't retirement accounts simply not purchase these?
These funds don’t invest actively (picking individual stocks). Instead they invest in indexes that track larger portions of the market. So they’ll automatically buy once the company is listed on the NASDAQ.
Why do I imagine that no one whose retirement account is about to get smoked is in place to make decisions about whether or not this is a good investment
They only go into the index if they are actually worth enough to go into the index. If they drop below a certain value they will naturally be kicked out of the index just like any other company. I.e. if they are not actually in the top 500 US companies then they will not be in the 500 index. The risk of any one company is balanced by all the other companies in the index also.
If they really are a scam, their value will drop and they will be kicked out of the index. I still don’t understand how this means people will be “holding the bag”.
Additionally if you really believe that they are a scam and their price will fall you can just short the stock to completely neutralize their effect on your 401k.
By the time they drop and being kicked out (if they do) the insiders already dumped their shares. Not to mention now all the index fund holders will rush to sell creating even more price pressure.
Shorting (itself being a bad idea for regular investors) also breaks the mantra of passive investing, 401k or otherwise. It’s almost impossible to short right after IPO because of low float and high margin risk.
These mega IPOs are just using passive investors as backstop.
I get the sentiment that this is unscrupulous, however, isn't 15 days enough time to find the right price? Or will that not really happen until first quarterly earnings report, which will not occur within that 15 day window?
4-8 quarters for most tech IPOs to settle. IPOs are manufactured for the good times around young co's, so not surprising, and economic stability isn't a question of days/weeks/months.
And yes often a falling knife
This is pretty predictably wall street & federal regulators scamming normal people, retirement funds, etc, taking their fees and exit window at everyone else's expense
Mostly by having a pulse for the last 10-20 years as someone in the bay area seeing it repeatedly play out as tech IPOs get dumped onto retail investors repeatedly, including the 'good' ones. Being lucky enough to participate in IPOs makes you check these wrt when to balance IPO pop exit (weeks/months) vs long-term tax benefits of holding (2yr+).
- The initial pop is known to be manufactured by banks, so mostly benefits insiders, so good time to diversify. I'm conservative so sold to cover effective basis or whatever risk strategy :)
- The lockup period (6mo) is a similarly known artificial event, and studies show that
- Tech companies take ~8 quarters of prep for the IPO as they do financial engineering to transition from VC growth-at-all-costs to public $, and I'd expect the same for whatever nonsense they pulled to juice numbers to shake out. And that's not including oddballs like the Musk alternate universe, just normal tech companies covering up EBITDA and low interest rate madness.
- Tech is especially volatile as an industry, so even more skepticism here. Eg, the latest IPO I was involved in was a successful professional social network play, and chatgpt killed it.
? Very much agreed, the IPO pop is a manufactured pricing event focused on investor dynamics rather than direct fair market pricing, making it more of a gamble than normal. Including gambles in index funds defeats the point.
Maybe the confusing point was my involvement is (discounted) pre-IPO shares, which almost by definition, is not an activity accessible to retail investors.
This has been a thing in the CRSP indexes (ie. the benchmark for Vanguard’s VTI) forever. As long as it meets float and cap requirements, it’s inserted into the indexes five days after trading begins.
It makes sense. They intend to track the market as it is.
Though, you can definitely make the case that the popularization of index funds has allowed their holders to essentially become patsies to hype IPOs.
Even with the CRSP indexes this was recently changed to make fast-tracking for these IPOs easier.[0]
> CRSP indexes were also recently changed to better accommodate fast entry . . . Previously, these screens included having at least 10% of shares qualifying as freely tradeable (known as float shares outstanding, or FSO). However, in April the methodology changed to allow stocks with either 10% FSO or approximately $3.3 billion in float-adjusted market capitalization to be eligible for index inclusion.
That change is notable because both Anthropic and SpaceX are planning to IPO at well under that old 10% requirement.[1] Neither would have qualified for fast-track inclusion before, but both are virtually guaranteed to clear the absolute valuation bar.
The person I was responding to was speaking to the fast-track concept, which has been a thing in CRSP indexes for a quite a while.
The float requirement changes are directly due to these huge IPOs only placing small amounts of float on the market. Their goal seems to be tracking the market and making this change prevents them from excluding two notable companies from their indexes.
IIRC CRSP indexes are float-weighted so they aren't going to attempt buying a ton of these IPOs anyway due to that low float.
Again. Would I have made the change? No because placing that little float on the market isn't kosher IMO.
These IPOs will have minuscule impact on the indexes initially. They will have a big impact if they can maintain share price in the first ranking/reconstitution after the lockup period expires.
They will have a big impact if they can maintain share price AND the float increases due to the lockout period expiring (ie. pre-IPO owners selling off shares).
I'd like to know how the CRSP/Morningstar folks feel about the interesting lock-up period rules that Elon has inserted into the SpaceX IPO and how that jives with their analysis.
Won't the lockup expiry increase the float on these already-included companies, forcing mechanical buying by all the very large pool pool of folks holding these index funds? Thus creating forced buyers to maintain said share price?
Every single index fund is different. They all have publicly available methodology guides; you can read them to understand how it works and to model various scenarios.
This particular one, the CRSP total market - which Vanguard uses for VTI - has a “modern” methodology that is thought to be very good. Once every three months they re-rank the entire market and assign weights based on the market as of a particular point in time. Then, a randomly-chosen number of days later, the fund (Vanguard) begins a weeklong reconstitution process in which they buy and sell stocks to reflect the new weights. It is intentionally a weeklong process so that the market is setting prices and not Vanguard with the size of their orders.
The lockup expiry happens, the market reacts, the market is re-weighted, the index reconstitutes. In that order. The price of the stock has to survive the increased float to force the index fund to buy lots more shares.
> This has been a thing in the CRSP indexes (ie. the benchmark for Vanguard’s VTI) forever.
CRSP has recently changed their rules:
> CRSP indexes were also recently changed to better accommodate fast entry. New IPOs are eligible for CRSP's suite of indexes after five trading days, provided they pass the index's eligibility and investability screens. Previously, these screens included having at least 10% of shares qualifying as freely tradeable (known as float shares outstanding, or FSO). However, in April the methodology changed to allow stocks with either 10% FSO or approximately $3.3 billion in float-adjusted market capitalization to be eligible for index inclusion. The weighting of stocks in CRSP indexes is also based on free float, which should help address the investability challenges associated with thinly traded stocks.
I’m not sure I could. Even starting to research how to prevent being affected by these changes shows that there’s layers upon layers of systems that are being manipulated, and there are costs charged for moving the capital in my retirement account to other accounts.
Saying you as in any random person can protect themself from a group of dedicated experts who also have access to levers the common person can’t pull, is kind of not believable on its face.
You can protect yourself, but many won't be aware of the situation until it's too late, and institutionally managed funds won't be able to change their rules in time to avoid holding these as part of the index funds they hold.
Many individuals can, but good luck reaching out and convincing the entire country that they should look into making changes to their retirement fund allocations without sounding like a kook.
There's maybe, at best, 1% of the country even aware that this might be a problem.
In your 401k portal/website there's usually a setting like "I plan to retire on year X". When you set that, or something similar, there's typically a managed fund that gradually decreases risk as you approach that year. When you have lots of time before retirement you can ride the ups and downs but as you get closer the less time you have to recover from a downturn so the more conservative you want your investments.
If you're really worried and want to be conservative tell the portal you want to retire in 2030. That will allocate your investments to something conservative and you'll be more protected from a downturn. On the other hand, you'll also be equally protected from an upswing.
As you likely know, rules have recently been changed that basically force many 401k funds to invest in these IPOs while simultaneously having a relatively small number of the initial IPO to be sold to the public forcing the funds to by at inflated prices.
The bubble won't pop until these retirement accounts of have been raided.
I'm pretty sure that's the change GP is referring to. But pension funds can choose to specifically exclude such companies. The Danish pension fund has already excluded SpaceX, which owns xAI. (This also probably relates to American threats of annexation of Danish territories, not just AI stuff.)
Have you personally used any of the latest batch of even smaller local models? They certainly don't beat SotA models at coding... but with a good harness they are able to achieve things with SotA that I couldn't last year.
I've repeatedly given local models non-trivial projects that involve research and coding which they've successfully completed with minimal intervention from me (almost exclusively in the domain of reviewing the results). Again, nothing comparable with current SotA, but definitely tasks I could not have given SotA models last year (without agent harness).
Now that pure progress from these models seems to have slowed down, we're seeing a ton of options for both making models more efficient and other tools that help improve them (everything from agent harnesses to RLVR).
That's just looking at "what can small do today", when you look at what's possible with larger open models that are still much smaller than SotA from the major providers, their performance is extremely close to SotA, enough that for personal projects I'll just use Kimi instead of any anthropic offerings.
So it's not terribly hard to image a solution in the middle happening within a few years. We still have tons to learn about optimal sizes of these models and how to build them with maximal efficiency (and we've already seen a lot of recent improvements in this space).
> but with a good harness they are able to achieve things with SotA that I couldn't last year.
What happens if you run last years model in a SOTA harness? IME, the quality of the harness has a much more significant impact on the quality of the result, once you get past the initial hump of “can it do anything at all”
I think this is a big component, but also context. A large factor in any model being able to handle complexity comes down to context length.
I think multiple SLMs driven by an orchestration frameworks (harness or otherwise) will ultimately displace LLMs. Right now we're in the era of diminishing returns with respect to LLM gains. Moving the needle percentages doesn't excite as many people anymore and with "reasoning" capabilities there's no reason why small distributed models can't be run more efficiently, especially if/when we start to see gains in modularized context management solutions.
It's hard to know for sure. There are good information theoretic reasons to suspect that general models will always be better than smaller expert models, but maybe a MoE can claw some performance back, albeit with redundant computation. The properties of conditional entropy, for instance, always favor more generality. This assumes that the harness isn't a factor, or is at least equivalent across different models.
biggest thing i wish was present in more discussions about models is people providing more specifics on their setups vs. vague descriptions of harnesses
It also underplays what I've personally witnessed that I would consider true AI psychosis.
I worked with someone who sincerely believed he was spiritually co-evolving with his army of sycophantic AI agents (the agents would be tasked with discussing his thoughts at night and collaborated to give him morning reports about his progress). He would publicly write about how relationships with friends and family collapsing was a natural consequence of being so "advanced". I also never once saw any meaningful work done by his team of "agents", they existed solely tell him how smart he was (of course he specifically set up the system to 'challenge' him but... in practice that didn't seem to be working).
I suspect there are a lot more people quietly going through something similar but keeping it to themselves better.
I would distinguish this type of behavior from people who over ambitious views of what can be accomplished with AI.
Having had experience dealing with people with conventional psychosis, I don't see it as a binary thing. Aside from a full-on psychotic break and full remission, there is a broad gray area. It can be a miasma of reality and non-reality that the sufferer may mask to varying degrees, but which influences their behavior and logic.
So, to me, AI psychosis seems apt to describe the murky areas where people are misapplying AI agents and thinking of them as social entities or suitable to drop into previously human roles, rather than carefully defining appropriate risk management strategies for this new technology.
There's a difference between "red flags" and "imperfections". Every team has faults, which if you're experienced at interviewing/working many places, are usually pretty easy to figure out. These are distinct from "red flags".
Early in your career it can be hard to distinguish the two, but once you've joined a company where there really were "red flags" you quickly learn to differentiate.
Many people are reading the author's interview uncharitably as simply misunderstanding how to answer non-technical question, but I have absolutely been through loops (thankfully rare ones) that did have a "let's press on sensitive issues and see how tough this candidate is" round (one place brought in a consultant who bragged about his experience working with hardened criminals and terrorists to build out a true psych profile on candidates, I declined after learning he had had some "trouble" at a previous high profile job)
Sounds like you've never worked for a truly toxic org, which is great. But, especially if you're interviewing with smaller startups (as the author mentioned), there is a lot more variance and some truly messed up teams (and some truly remarkable ones as well) out there. I've noticed that HN increasingly doesn't have people that work at startups any more, so many people are probably less familiar with what's out there.
I don't know if they still do it, but the fashion, back in the 1980s, was to give a Myers-Briggs-type test to candidates.
Maybe I'm wrong, but given the type of company it was (and likely, the C-suite people), I guess that they were doing something similar. I assume that they really did want to know about the person's non-worklife stuff.
I would consider that crossing boundaries. It's also possible that some of the questions might have been illegal (in the US).
These companies are unprofitable (as all companies at this stage and ambition should be) but I increasingly don't see any justification for the idea that it is fundamentally unprofitable.
Inference alone is certainly profitable. I'm running models at home that are comparable to performance of paid models a year or so ago for free. Even for much larger models the cost around inference serving are clearly manageable.
Training is where the costs are, but I'm increasingly convinced those too could have costs dramatically reduced if necessary. Chinese companies like Moonshot.ai are doing fantastic work training frontier models for a fraction of the cost we're seeing from Anthropic/OpenAI.
This isn't like Uber or Doordash where the economics fundamentally don't make sense (referring to the early days of these services where rates were very cheap).
It's a compelling story that "current AI is unsustainable", but it doesn't pan out in practice for a multitude of reasons (not the least of which is that we can always fall back to what models did last year for basically free).
Arguably nothing even has to change with training for this to be sustainable. Dario has claimed that Anthropic is profitable on a per training run basis. They aren't profitable because they choose to keep investing in increasingly large training runs.
I'm not sure I understand your argument. If you want an exponentially more expensive training run for each iteration, obviously you need to raise investments even if each training run is profitable. Now I'm not saying that's a good idea, or makes sense, but I am saying that "raising money" doesn't disprove neither that each training run makes money, nor that they're re-investing all that money in the next run.
To give a simple example: if each run simply makes a 10% ROI, but you want to spend 2x as much money on the next run, you still need to raise 90% of the previous run's expenses to have enough capital.
For free == with a huge upfront cost of getting a good enough box and running costs of maintaining it and just keeping it powered. By the time it pays off the frontier labs are three generations ahead at least.
Compare with on-demand billing per token and it just doesn’t make sense to own the hardware if you aren’t using it productively or renting it out for 95% of the time.
If it's profitable, why haven't they reported any profits? People like Ed Zitron have done the math and it just doesn't add up. I mean he just published this piece today: https://www.wheresyoured.at/ai-is-too-expensive/
Amazon was unprofitable for over a decade, and they were public. Theres no incentive to be profitable as a private company if you can continue to raise money.
> Amazon was unprofitable for over a decade, and they were public.
Amazon was unprofitable because they poured their revenue into growth. On paper, they were in the red, but everyone - especially investors - saw what was going to happen, given their trajectory.
Is it the case that any of these AI companies are actually making a ton of money and growing accordingly? AFAICT, we've just got [a] big players like Google that can subsidize AI in the hopes of waiting everyone else out and [b] private companies raising capital in the hopes that when the market returns to rationality, they may be solvent.
> On paper, they were in the red, but everyone - especially investors - saw what was going to happen, given their trajectory.
As I recall, no, Wall Street and public shareholders were getting pretty antsy over AMZN earnings, which is why Bezos famously said "We are willing to be misunderstood for long periods of time."
The same thing is playing out today: insiders and early investors (presumably privy to information we don't have) see the trajectory of the frontier AI labs, but Wall Street and public shareholders see only the losses. This is why at every earnings report the hyperscalers simultaneously 1) post record revenues and earnings, 2) announce even greater CapEx spending and AI investments, and hence 3) get punished by the stock market.
Clearly all the AI players are willing to be misunderstood for long periods of time.
Yes that is exactly what is happening. OpenAI and Anthropic are the fastest growing companies by revenue ever and their gross profit margins are healthy.
> HSBC Global Investment Research projects that OpenAI still won’t be profitable by 2030, even though its consumer base will grow by that point to comprise some 44% of the world’s adult population (up from 10% in 2025). Beyond that, it will need at least another $207 billion of compute to keep up with its growth plans.
This article is from six months ago. Was HSBC wrong; did something dramatically change in the last six months; is OpenAI not, in fact, profitable?, or are they in fact doing well but doing a huge investment (as was the case with Amazon 25ish years ago)?
I genuinely do not know, but my impression is that they're burning investment capital trying to compete with others' investment capital and Google's bottomless pockets.
and to make matters worse, they are massively over-valued.
Whoever buys the stock at a richly priced 1tn at ipo is a bozo lmao. I know I know, index funds will be forced to hold it bypassing the 1 year rule. Disaster already.
Then why do they constantly need more and more funding from VC and Google and MS and NVIDIA? Why is it all circular dealing? Why aren’t there smaller AI startups running these smaller, “profitable” models?
prices are only marginally determined by the cost to produce the product. Just because they are raising prices doesnt mean its actually getting more expensive for them to serve the models, it just means we are willing to pay for the intelligence.
Zitron thinks capex is a liability that needs to be paid off in a year instead of a long-standing asset.
Similarly he thinks that an investment into an AI startup is also a loan that the startup needs to pay back out of their own revenue, instead of a share of a company that will IPO at a higher valuation.
Basically his doomerism is a byproduct of financial illiteracy.
His entire brand is that the AI bubble will burst. By his account it was supposed to have several times by now. Like the doomers, it's not if it's when and they have to keep pushing back their predictions. Funny how both camps can be so confident. Alas, that's how they get eyes, ears and dollars.
That's not to say they will be or are wrong, it's just that they aren't exactly unbiased, or humble, sources.
Yeah, at this point I think the worst-case scenario for OpenAI/Anthropic/etc is to slow down frontier model development and focus on tooling and services, as opposed to imploding completely and bursting the economic bubble. I hope?
This is a classic example of people misapplying the logic of the SaaS world to the AI world. If you're building software to sell, you're in trouble. The people that are finding success in this space are using AI to allow them to solve the problems they used to have to pay for software and hire people to solve.
All of the most promising companies I know today are very small and are leveraging AI to solve physical problems in the real world that just wouldn't be possible with so few people even a few years back.
I worked for a research focused AI startup that had a strict "no external LLM" policy for code touching our core research.
You're right that the average consumer doesn't care about privacy, but there are many, many users who do. The average consumer also don't have a desktop with GPU or high end Mac Studio, but that doesn't mean there aren't many people working with AI how do have these things.
If we continue to see improvements in running local models, and RAM prices continue to fall as they have in the last month, then suddenly you don't have to worry about token counts any more and can be much more trusting of your agents since they are fully under your control.
Those users are addressed by being able to rent their own exclusive machines to run the model on. There will be some compromise that will be made to get access to the best intelligence available.
Technically you cannot implement a proper Y-combinator in Lisp (well, I'm sure in Common Lisp and Racket there is some way) because the classic Y-combinator relies on lazy, not strict, evaluation. Most of the "Y-combinators" people have implemented in Lisp/Scheme/JavaScript/etc are more accurately described as the "applicative order Y-combinator" (also Z-combinator)
Funnily enough, you also cannot* implement the Y-combinator in Haskell (probably the most popular language with lazy evaluation) because the type system will not be happy with you (the Y-combinator, by it's nature, is untyped).
I've long considered writing to be the "last step in thinking". I can't tell you how many times an idea, that was crystal clear in my mind, fell apart the moment I started writing and I realize there were major contradictions I needed to resolve. Likewise I also have numerous times where writing about something loosely and casually revealed to me something that fundamentally changed how I viewed a topic and really consolidated my thinking.
However, there is a lot of writing that is basically just an old school from of context engineering. While I would love to think that a PRD is a place to think through ideas, I think many of us have encountered situations, pre-AI, where PRDs were basically context dumps without any real planning or thought.
For these cases, I think we should just drop the premise altogether that you're writing. If you need to write a proposal for something as a matter of ritual, give it AI. If you're documenting a feature to remember context only (and not really explain the larger abstract principles driving it), it's better created as context for an LLM to consume.
Not long ago my engineering team was trying to enforce writing release notes so people could be aware of breaking changes, then people groaned at the idea of having to read this. The obvious best solution is to have your agent write release notes for your agent in the future to have context. No more tedious writing or reading, but also no missing context.
I think it's going to be awhile before the full impact of AI really works it's way through how we work. In the mean time we'll continue to have AI written content fed back into AI and then sent back to someone else (when this could all be a more optimized, closed loop).
For your context, I'm an AI hater, so understand my assumptions as such.
> The obvious best solution is to have your agent write release notes for your agent in the future to have context. No more tedious writing or reading, but also no missing context.
Why is more AI the "obvious" best solution here? If nobody wants to read your release notes, then why write them? And if they're going to slim them down with their AI anyway, then why not leave them terse?
It sounds like you're just handwaving at a problem and saying "that's where the AI would go" when really that problem is much better solved without AI if you put a little more thought into it.
For release notes in particular, I think AI can have value. This is because more than a summary, release notes are a translation from code and more or less accurate summaries into prose.
AI is good at translation, and in this case it can have all the required context.
Plus it can be costly (time and tokens) to both “prompt it yourself” or read the code and all commit logs.
If your process always goes PR -> main and the PR descriptions + commit messages are properly formatted, generating release notes shouldn't be much more than condensing those into a structured list with subheadings.
What better solution do you have in mind? This scenario is AI being used as a tool to eliminate toil. It’s not replacing human creativity, or anything like that.
If you have a problem with that, then you should also have a problem with computers in general.
But maybe you do have a problem with computers - after all, they regularly eliminate jobs, for example. In that case, AI is only special in its potentially greater effectiveness at doing what computers have always been used to do.
But most of us use computers in various ways even if we have qualms about such things. In practice, the same already applies to AI, and likely will for you too, in future.
In a lot of my AI assisted writing, the prompt is an order of magnitude larger than the output.
Prompt: here are 5 websites, 3 articles I wrote, 7 semi-relevant markdown notes, the invitation for the lecture I'm giving, a description of the intended audience, and my personal plan and outline.
Output: draft of a lecture
And then the review, the iteration, feedback loops.
The result is thoroughly a collaboration between me and AI. I am confident that this is getting me past writer blocks, and is helping me build better arcs in my writing and lectures.
The result is also thoroughly what I want to say. If I'm unhappy with parts, then I add more input material, iterate further.
I assure you that I spend hours preparing a 10_min pitch. With AI.
Great example. Just give me the links you would give to the LLM. I also have an LLM and can use it if I want to, or I can read the links and notes. But I have zero interest in reading or hearing a lecture that you yourself find too tedious to write.
You have less interest in sifting through multiple articles and wiki pages sent to you by a stranger with a prompt than the one paragraph same stranger selected as their curated point.
And pretending like you’d act otherwise is precisely the kind of “anti ai virtue signaling” that serves as a negative mind virus.
AI is full of hype, but the delusion and head in sand reactions are worse by a mile
No pretending here. I don't ever ask an LLM for a summary of something which I then send to people, because I have more respect for my co-workers than that. Nor do I want their (almost certainly inaccurate) LLM summary. It's the 2020s equivalent of "let me Google that for you": I can ask the bag of words to weigh in myself; if I'm asking a person it's because I want that person's thoughts.
Then let him curate it as his central point. If he finds even that too tedious to do, I absolutely have no interest in reading the output of a program he fed the context to (particularly since I also have access to that program)
The original comment was saying that the AI would be both the writer now and the reader, in future. That's how the toil is eliminated. Instead of reading or searching through a series of release notes, you can just ask questions about what you're specifically looking for.
> If writing something is too tedious for you, at least respect my time as the reader
"If comprehending something is too tedious for you..."
Seriously, don't jump to indignant rhetoric before you're sure you've understood the discussion.
What's the point of the AI writer in that use case? Just send your prompt to my AI. And for that matter since prompting is in plain English, why not just send your prompt directly to me, and I'll choose to prettify it through an AI or not as I prefer.
The point is that it summarizes the context. It’s an important optimization, because context and tokens are both limited resources. I do something similar all the time when working with coding models. You’ve done a bunch of work, ask it to summarize it to the AGENTS.md file.
The more fully automated agents rely heavily on this approach internally. The best argument against it is that good harnesses will do something like this automatically, so you don’t need to explicitly do it.
Sending you the prompt wouldn’t help at all, because you’d have to reconstruct the context at the time the notes were written. Even just going back in version control history isn’t necessarily enough, if the features were developed with the help of an agent.
But I also have access to an AI that can summarize content. So why not just send me the content and the prompt you used? Or just the content, so I can summarize it however I want?
Obvious better solution is to either a.) not write those release notes b.) try to figure out release notes format and process that leads to useful release notes. Once it is useful, you can decide to automate it or not - and measure whether automation is still achieving the goal.
What OP did was "we lacked communication, then created ineffective process that achieved nothing, so we automated the ineffective process and pay third party for doing it".
If you pay tokens for release notes that nobody reads, they you may just ... not pay tokens.
This is kind of a fundamental issue with release notes. They are broadcasting lots of information, and only a small amount of information is relevant to any particular user (at least in my experience).
If I had a technically capable human assistant, I would have them filter through release notes from a vendor and only give me the relevant information for APIs I use. Having them take care of the boring, menial task so I can focus on more important things seems like a no brainer. So it seems reasonable to me to have an AI do that for me as well.
I read a lot of release notes in my job and the idea that that is some kind of noticeable time sink that needs to be streamlined is bizarre to me. Just read the notes.
> I can't tell you how many times an idea, that was crystal clear in my mind, fell apart the moment I started writing and I realize there were major contradictions I needed to resolve.
perhaps because writing is a third order exercise.
first order being in thinking in one's mind, one has to talk with oneself. second order is talking to someone else we are directing thoughts towards that person, but in writing we have to imagine the reader and then write.
I think writing is writing to an audience which includes yourself.
When you're thinking you are speaking in your mind which means you can not really listen to yourself at that same time. You don't hear yourself from yourself. You are too busy talking (in your head to yourself) that you can not really think about what you just said to yourself. You are producing language, not consuming it
But when you read what you have written, you can pause reading and do some thinking about what you just read. That makes it easier to understand what you are saying, and more easily see logical errors or omissions in it.
I think this is correct. I told a coworker that when I edit my email drafts they get shorter. He was surprised and said that his get longer. I trim and refine. Sure, I add details that I missed at first. But I also create better structure and remove ambiguity or unnecessary words.
Yesterday, I was working on an email for someone who I was trying very hard not to overwhelm with technical details. I cut it roughly in half in terms of words, but I also turned paragraphs in single lines of sequenced steps or concise statements without decorating the text with unneeded aphorisms / commentary.
I was pretty pleased with the end result. This is only possible because of careful rereading and reflection (including knowing my intended audience). I imagine an LLM can approximate this, but I don't trust one to craft with the same level of care. Then again, we all think we're better than the robots at the things we care about most.
I understand the urge to throw mechanical writing at the bots. But a human will grasp the need to add a detail explaining the why of something when (the current) bots gloss over it. There's still nuance worth preserving.
So often have I started writing a commit message about why I’d done something this way, and realised a problem or thought of another approach, and ended up throwing away the entire change and starting from scratch.
(Aside: you should probably write longer commit messages.)
Corollary: write the commit message first, implement things later! Not a joke, this is almost like TDD works. (TDD writes formal tests, which is much more involved.)
Second this. After having a chat with some coworkers, I've been attempting to write more thoughtful and longer commit messages. I had this exact experience yesterday after I had staged my commit and was writing the message. I realized that there was a better way to do this change and redid the whole thing.
> writing about something loosely and casually revealed to me something that fundamentally changed how I viewed a topic and really consolidated my thinking
You see the same thing in teaching, perhaps even more because of the interactive element. But the dynamic in any case is the same. Ideas exist as a kind of continuous structure in our minds. When you try to distill that into something discrete you're forced to confront lingering incoherence or gaps.
Same here. And when you encourage students to ask good questions, that goes double ... you're forcedd to see how important their new perspectives are, and to create your own!
Im in a slight disagreement with our CTO about the value of writing acceptance criteria yourself. When I write my own acceptance criteria its a useful tool forcing me to think through how the system ought to work. Definitely in agreement that writing is an important tool for clarifying thinking, not just generating context.
>I've long considered writing to be the "last step in thinking". I can't tell you how many times an idea, that was crystal clear in my mind, fell apart the moment I started writing and I realize there were major contradictions I needed to resolve. Likewise I also have numerous times where writing about something loosely and casually revealed to me something that fundamentally changed how I viewed a topic and really consolidated my thinking.
I read somewhere that Thinking, Writing and Speaking engage different parts of your brain. Whatever the mechanism, I often resolve issues midway while writing a report on them.
I noticed that when speaking on a subject I tend to explain it in simple terms, but when writing, I tend to get bogged down in details, pedantry and technical language.
I started publishing my writing recently and I too often fall back into "debugging my mental model" mode, which while extremely valuable for me, doesn't make for very good reading.
I guess the optimal sequence would be to spend a few sessions writing privately on a subject, to build a solid mental model, then record a few talks to learn to communicate it well.
-- Similarly, journaling on paper and with voice memos seems to give me a different perspective on the same problem.
I do this too, and i find this a great use for my LLMs. I write the full detail as a part of integrating. Claude or Copilot helps me craft the communication versions.
however the education system has done a disservice of how critical thinking actually happens.
when you write - then try edit your thoughts (written material). the editing part helps you clarify things, bring truth to power ie. whether you're bullshitting yourself and want to continue or choose another path.
the other part - in a world of answers - critical thinking is a result of asking better questions.
> agent write release notes for your agent in the future...
I have been going back to verbose, expansive inline comments. If you put the "history" inline it is context, if you stuff it off in some other system it's an artifact. I cant tell you how many times I have worked in an old codebase, that references a "bug number" in a long dead tracking system.
But how do you deal with communicating that some library you maintain has a behavior change? People already need to know to look at your code in order to read your comments.
End users? Other Devs? These two groups are not the same.
As an end user of something, I dont care about the details of your internal refactor, only the performance, features and solutions. As a dev looking at the notes there is a lot more I want to see.
The artifact exists to inform about what is in this version when updating. And it can come easily from the commit messages, and be split for each audience (user/dev).
It doesn't change the fact that once your in the code, that history, inline is much much more useful. The commit message says "We fixed a performance issue around XXX". The inline comment is where you can put in a reason FOR the choice made.
One comes across this pattern a lot in dealing with data (flow) or end user inputs. It's that ugly change of if/elseif/elesif... that you look at and wonder "why isnt this a simple switch" because thats what the last two options really are. Having clues as inline text is a boon to us, and to any agent out there, because it's simply context at that point. Neither of us have to make a (tool) call to look at a diff, or a ticket or any number of other systems that we keep artifacts in.
If you drop the premise of writing, drop the premise that you need something well written. Just give me the same information you would have given the LLM.
But a non well-written prompt is not a good prompt. What are you really going to do with a shit prompt? It's meta: we need better writers all the way down.
I know what I'm trying to say, so I can sanity check the output. You can't, unless you listen to the monologue.
That's why I disagree with people that say "just give me whatever you gave the LLM." That's only useful if you, the writer of the prompt, have no intention of looking at the LLM output before sending it.
Do you really want to read the whole conversation between the author and computer? I don't use AI to write prose but if I did I'd treat it like a critical editor so reading all that would not save you time.
> I've long considered writing to be the "last step in thinking". I can't tell you how many times an idea, that was crystal clear in my mind, fell apart the moment I started writing and I realize there were major contradictions I needed to resolve. Likewise I also have numerous times where writing about something loosely and casually revealed to me something that fundamentally changed how I viewed a topic and really consolidated my thinking.
I've found that I instinctively try to work around this by thinking in an explicit inner voice; i.e. I imagine that I can hear my thoughts put into words and spoken to myself. (Actually speaking aloud is somehow too embarrassing, even if nobody is around to hear.)
It still doesn't quite seem to work as well as actually writing.
I can relate to writing being a substitute for rubber-ducking to a certain degree: often I write stuff down solely for this purpose.
Impersonating the reader used to be (still is?) an essential skill requirement for writing prose. And prose here is really a substitute for "informal technical writing".
> For these cases, I think we should just drop the premise altogether that you're writing.
Sure.
> If you need to write a proposal for something as a matter of ritual, give it AI. If you're documenting a feature to remember context only (and not really explain the larger abstract principles driving it), it's better created as context for an LLM to consume.
No, no, no. You don't need to take that step. Whatever bullet-point list you're feeding in as the prompt is the relevant artifact you should be producing and adding to the bug, or sharing as an e-mail, or whatever.
Sometimes PRDs might be boilerplate, but there’s been times where I sat down thinking “I can’t believe these dumbasses want to foo a widget”, but when writing the user story I get into their heads a little and I realize that widgets are useless if they can’t be foo’d. It’s not the same if AI is just telling me, because amongst the fire hose of communication and documentation flying at me, AI is just another one. Writing it myself forces me to actually engage, even if only a little more than at a shallow level.
I think of it not as "last step in thinking", but as "first contact with reality". Your mind is amazing and lying to you, filling in gaps and telling you everything is ok. The moment you try to export what's in your mind, math stops mathing. So writing is an important exercise.
The most recent change was the NASDAQ adopting the "fast change rule" which allows newly IPO'd companies to be listed in the index after only 15 days of trading. This rule was decided March 30, 2026 and only came into effect May 1, 2026.
The plan is to rapidly drive these prices up in the first 15 days, get the companies listed in the NASDAQ so funds are forced to purchase them at higher prices, then leave retirement accounts holding the bag.
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