It’s widely rumored that both OpenAI and Anthropic will go public within the next year or so, triggering massive capital inflows. I think this unleashes an underrated financial dynamics.
An optimal portfolio hedges your personal risks. Unless you’re independently wealthy, the risk of job loss due to AI automation is one your largest personal financial risks, and it makes a lot of sense to buy insurance against that (i.e., to buy into AI companies). Right now, individual investors can’t do that. And the investors that have access to OpenAI/Anthropic on the private market are precisely the ones that don’t need to hedge that risk. Once you open up to retail, a rational investor should park a sizable percentage of their net worth in those shares, opening up a capital gusher.
This is especially true for Anthropic—there’s basically no way for a retail investor to get meaningful Anthropic exposure if they want it. It’s a little different for OpenAI given that SoftBank is kinda an OpenAI proxy at this point (albeit in ways that make it a less attractive investment than the real thing).
Seems like this could lead to substantial acceleration of progress. It may also change the politics. If stopping AI progress mostly means financially soaking billionaires, then it’s politically a lot more palatable than if it will also screw up the 401ks of the mass affluent.
Note that this is not a perfect, or perhaps even a good hedge. There are very believable paths where AI reduces your income, but the current big AI companies don’t capture that value, or don’t return it to shareholders. Also quite believable that it’ll IPO with all that growth already priced in.
You want anti-correlated investments, but there’s really no possible way for most people to know that. Investing in broad indexes remains the safest bet for those without inside knowledge or serious resources (time, knowledge, energy) for research and decision-making.
There are very believable paths where AI reduces your income, but the current big AI companies don’t capture that value, or don’t return it to shareholders.
I suppose it depends on exactly what you do, but if the threat to you is something along the lines of “AI broadly replaces white collar workers” then it’s very hard to come up with a scenario where AI can replace most white collar workers but can’t find a way to make money for the companies building it.
You have to tell a really contorted story for that work—I guess it’s “believable” to imagine something like: AI gets good enough to replace most white collar workers, but there’s such intense commoditization and competition that there is no pricing power so the companies make no money, and the one white collar thing that AI can’t do is design semiconductors, so all the economic gains flow to Nvidia. Could happen but you have to rig the assumptions to get that result. And aside from that, most of the forms I can come up with are of the “money won’t matter anymore” variety in which case, there is no financial hedge.
Also quite believable that it’ll IPO with all that growth already priced in.
The point I’m trying to make is that Anthropic shares are worth more to your average upper middle class white collar worker than they are to the current ultra-wealthy or institutional shareholders. So, let’s assume Anthropic is currently valued in a way that perfectly captures it’s future growth prospects, it should still IPO at a higher valuation (and raise tons of money in the process) because retail investors will rationally put a higher value on the stock than institutions given that it is useful to retail but not institutions as a hedge.
I’m sure institutional shareholders, to the extent they can, are doing some amount of arbitrage in expectation of that dynamic. Everyone knows retail is desperate to buy AI. So, maybe, a fair secondary market valuation of Anthropic already accounts for that premium multiplied by some assessment of the probability they’ll actually list.
But, either way, the point is that the average company is worth about the same to retail vs. institutional investors, and AI companies have very different valuations for those two groups.
I’m also concerned about fiduciary duty and related mechanisms causing systematic enshittification due to putting pressure on employees to be able to justify their choices to a court.
Also, I don’t know if a judge would see it this way, but internally pushing for safety measures is 100% in shareholder interests because shareholders have an interest in not being killed by misaligned ASI.
Amazon is quite a good proxy for Antrhopic. They do own a big stake, Anthropic does use their chips and cloud + they have hypserscaler optionality for any AI future.
I don’t think openai/anthropic are the only ways to hedge your future. you can do it now buy going all-in all ai infra + chips + bottlenecks. Openai doesn’t operate on hardware of their own etc.
Anthropic recently announced higher Claude usage limits for off-peak hours, which is everything except weekdays from 8 AM − 2 PM ET.
I was surprised by the specific times here. This is night time in Asia, the tail end of the workday in Europe, and the start of the work day in North America. The beginning of the window is too early for much West Coast activity, and it winds down at 11 AM in California. That seems inconsistent with the idea that coding is driving power use?
The timing seems most consistent with the period when you’d see overlapping demand from the US East Coast and from Western Europe—neither of which are super tech heavy regions. Maybe this timing implies a lot of the power users are financial firms working on New York/London time?
A brief look at the Anthropic Economic Index suggests that the US is about 22% of global absolute Claude usage, and CA is 20% of US usage. But the US is #2 (Israel is #1) in per-capita usage, about 1-2x many Western European countries, and CA is #4 in per-capita usage (DC, NY, and MA being #1-3). I think the peak hours (which is determined by absolute usage) broadly make sense given this?
Hypothesis I am not confident about: maybe Silicon Valley usage is a big slice of the cake, but it’s stable because efficient power users keep agents running at night or do shifts to manage them? And so the variation in demand is driven by non-power-users like employees at non-tech companies.
Could be true, but the API price doesn’t vary by time of day, so there’s no particular incentive to do that for power users and so I’d be kinda surprised if they did anything active to smooth their usage.
As a non-coder, I found AI pretty useless before Opus 4.6. It was definitely having a net negative effect on my productivity because of the time I’d waste trying to get it to do things that didn’t work out or required massive corrections. It was much worse for my projects than an intern with an hour of training.
Now, all of the sudden, it actually works. And this was a step change from “no amount of scaffolding I do can get this to happen short of my manual intervention every time” to “I just describe what I want and it happens.” I’m hearing the same from colleagues.
Up until now, it seems like the only thing the models could actually accomplish beyond being a chatbot was writing code. At least for me, I had no idea what to make of that and whether vibe coding ought to really be considered impressive or not. It was also very hard to tell if that would translate to anything else in the medium term—whether the ability to do stuff in the “clinical” world of coding and math and taking tests would actually turn into the ability to do messier real world stuff.
Obviously other people have a different task distribution, but I think Opus 4.6 is the inflection point in terms of getting the word out to non-coders that AI can actually do stuff and will be able to do even more stuff soon and so on. For people not extremely on board with the “this is the worst it will ever be” school of thinking, I think interacting with previous models often left a “This is obviously unimpressive crap” response where it was really hard to tell if coders saying something different was real or hype.
This also leads me to concur (as a former staffer) that “get people in DC to play with Claude Code for a while” is now a high impact intervention whereas I think previously that kind of thing was likely to backfire.
(Could be totally wrong maybe we just happen to have hit my threshold now and not anyone else’s, but hitting that has definitely been a worldview shift for me).
I work in policy analysis, so a lot of what I want is messy data work. Some examples:
I need to download a lot of PDFs from a particular site (that has a sucky interface and can’t be easily scraped). Then I need to distinguish the ones that are relevant to my task from the ones that are not. The relevant ones probably (but not definitely) contain two particular pieces of information. They’re very inconsistently formatted/structured so the only way to get it is to parse each in a bespoke way. And the two pieces I need are not near each other but where they are matters and it’s easy (for an LLM) to pair them wrong. Banged my head with previous generation AI on this kind of stuff to endless frustration. Now, Claude Code can just do it.
I need information on X for each of the fifty states. There are going to end up being fifty different methods needed to get this because states are annoying. If I have to hold an LLMs hand through the fifty different approaches, it’s not worth my time and it’s faster to just do it manually. If I can pass off a single inquiry to someone competent, it’s a big help.
I want to stress test a dataset to see if it’s actually any good—you need to do a mix of work that’s internal to the data and comparison to other resources and try to merge it against stuff that’s in a totally different format and so on. This needs to iterate based on results so far, so you need a model that can actually check well as it goes.
Finally able to do help with analytical writing based on specified sources.
There’s a fairly large (1,800 forecaster) market on AGI timelines at Metaculus (albeit with a contestable definition).
The market has had a fairly stable forecast of mid-to-late 2033 over the last six months or so. In the last couple of days, the median has shifted from May 2033 (Tuesday) to October 2032 this morning.
I wonder what that’s about. The date of the move doesn’t really align with any new releases or any breakthroughs that I’m aware of (unless it was partially an anticipatory move on GPT-5.4 rumors). Maybe it’s just noise?
Note it was forecast for May 2030 exactly one year ago. It’s been fluctuating from 2030-2034 ever since GPT-4 was dropped almost exactly 3 years ago, with a few extended periods closer to both the high and low ends. I think it’s mostly noise.
It’s widely rumored that both OpenAI and Anthropic will go public within the next year or so, triggering massive capital inflows. I think this unleashes an underrated financial dynamics.
An optimal portfolio hedges your personal risks. Unless you’re independently wealthy, the risk of job loss due to AI automation is one your largest personal financial risks, and it makes a lot of sense to buy insurance against that (i.e., to buy into AI companies). Right now, individual investors can’t do that. And the investors that have access to OpenAI/Anthropic on the private market are precisely the ones that don’t need to hedge that risk. Once you open up to retail, a rational investor should park a sizable percentage of their net worth in those shares, opening up a capital gusher.
This is especially true for Anthropic—there’s basically no way for a retail investor to get meaningful Anthropic exposure if they want it. It’s a little different for OpenAI given that SoftBank is kinda an OpenAI proxy at this point (albeit in ways that make it a less attractive investment than the real thing).
Seems like this could lead to substantial acceleration of progress. It may also change the politics. If stopping AI progress mostly means financially soaking billionaires, then it’s politically a lot more palatable than if it will also screw up the 401ks of the mass affluent.
Note that this is not a perfect, or perhaps even a good hedge. There are very believable paths where AI reduces your income, but the current big AI companies don’t capture that value, or don’t return it to shareholders. Also quite believable that it’ll IPO with all that growth already priced in.
You want anti-correlated investments, but there’s really no possible way for most people to know that. Investing in broad indexes remains the safest bet for those without inside knowledge or serious resources (time, knowledge, energy) for research and decision-making.
I suppose it depends on exactly what you do, but if the threat to you is something along the lines of “AI broadly replaces white collar workers” then it’s very hard to come up with a scenario where AI can replace most white collar workers but can’t find a way to make money for the companies building it.
You have to tell a really contorted story for that work—I guess it’s “believable” to imagine something like: AI gets good enough to replace most white collar workers, but there’s such intense commoditization and competition that there is no pricing power so the companies make no money, and the one white collar thing that AI can’t do is design semiconductors, so all the economic gains flow to Nvidia. Could happen but you have to rig the assumptions to get that result. And aside from that, most of the forms I can come up with are of the “money won’t matter anymore” variety in which case, there is no financial hedge.
The point I’m trying to make is that Anthropic shares are worth more to your average upper middle class white collar worker than they are to the current ultra-wealthy or institutional shareholders. So, let’s assume Anthropic is currently valued in a way that perfectly captures it’s future growth prospects, it should still IPO at a higher valuation (and raise tons of money in the process) because retail investors will rationally put a higher value on the stock than institutions given that it is useful to retail but not institutions as a hedge.
I’m sure institutional shareholders, to the extent they can, are doing some amount of arbitrage in expectation of that dynamic. Everyone knows retail is desperate to buy AI. So, maybe, a fair secondary market valuation of Anthropic already accounts for that premium multiplied by some assessment of the probability they’ll actually list.
But, either way, the point is that the average company is worth about the same to retail vs. institutional investors, and AI companies have very different valuations for those two groups.
I’m also concerned about fiduciary duty and related mechanisms causing systematic enshittification due to putting pressure on employees to be able to justify their choices to a court.
By default, employees don’t have a fiduciary duty to shareholders. The only people with a fiduciary duty are (IIUC) (1) the board of directors and (2) corporate officers who represent the company, such as the CEO. See e.g. https://www.alllaw.com/articles/nolo/business/corporate-fiduciary-duties.html
Also, I don’t know if a judge would see it this way, but internally pushing for safety measures is 100% in shareholder interests because shareholders have an interest in not being killed by misaligned ASI.
Sure. This also isn’t the only mechanism by which going public typically produces enshittification.
Amazon is quite a good proxy for Antrhopic. They do own a big stake, Anthropic does use their chips and cloud + they have hypserscaler optionality for any AI future.
I don’t think openai/anthropic are the only ways to hedge your future. you can do it now buy going all-in all ai infra + chips + bottlenecks. Openai doesn’t operate on hardware of their own etc.
Anthropic recently announced higher Claude usage limits for off-peak hours, which is everything except weekdays from 8 AM − 2 PM ET.
I was surprised by the specific times here. This is night time in Asia, the tail end of the workday in Europe, and the start of the work day in North America. The beginning of the window is too early for much West Coast activity, and it winds down at 11 AM in California. That seems inconsistent with the idea that coding is driving power use?
The timing seems most consistent with the period when you’d see overlapping demand from the US East Coast and from Western Europe—neither of which are super tech heavy regions. Maybe this timing implies a lot of the power users are financial firms working on New York/London time?
A brief look at the Anthropic Economic Index suggests that the US is about 22% of global absolute Claude usage, and CA is 20% of US usage. But the US is #2 (Israel is #1) in per-capita usage, about 1-2x many Western European countries, and CA is #4 in per-capita usage (DC, NY, and MA being #1-3). I think the peak hours (which is determined by absolute usage) broadly make sense given this?
Bearish on AI takeoff happening right now.
Hypothesis I am not confident about: maybe Silicon Valley usage is a big slice of the cake, but it’s stable because efficient power users keep agents running at night or do shifts to manage them? And so the variation in demand is driven by non-power-users like employees at non-tech companies.
Could be true, but the API price doesn’t vary by time of day, so there’s no particular incentive to do that for power users and so I’d be kinda surprised if they did anything active to smooth their usage.
“The start of the work day in North America” is not usually the time when software developers are the most productive.
As a non-coder, I found AI pretty useless before Opus 4.6. It was definitely having a net negative effect on my productivity because of the time I’d waste trying to get it to do things that didn’t work out or required massive corrections. It was much worse for my projects than an intern with an hour of training.
Now, all of the sudden, it actually works. And this was a step change from “no amount of scaffolding I do can get this to happen short of my manual intervention every time” to “I just describe what I want and it happens.” I’m hearing the same from colleagues.
Up until now, it seems like the only thing the models could actually accomplish beyond being a chatbot was writing code. At least for me, I had no idea what to make of that and whether vibe coding ought to really be considered impressive or not. It was also very hard to tell if that would translate to anything else in the medium term—whether the ability to do stuff in the “clinical” world of coding and math and taking tests would actually turn into the ability to do messier real world stuff.
Obviously other people have a different task distribution, but I think Opus 4.6 is the inflection point in terms of getting the word out to non-coders that AI can actually do stuff and will be able to do even more stuff soon and so on. For people not extremely on board with the “this is the worst it will ever be” school of thinking, I think interacting with previous models often left a “This is obviously unimpressive crap” response where it was really hard to tell if coders saying something different was real or hype.
This also leads me to concur (as a former staffer) that “get people in DC to play with Claude Code for a while” is now a high impact intervention whereas I think previously that kind of thing was likely to backfire.
(Could be totally wrong maybe we just happen to have hit my threshold now and not anyone else’s, but hitting that has definitely been a worldview shift for me).
What kind of non-coding task are you applying the AI to, could you share some more details?
I work in policy analysis, so a lot of what I want is messy data work. Some examples:
I need to download a lot of PDFs from a particular site (that has a sucky interface and can’t be easily scraped). Then I need to distinguish the ones that are relevant to my task from the ones that are not. The relevant ones probably (but not definitely) contain two particular pieces of information. They’re very inconsistently formatted/structured so the only way to get it is to parse each in a bespoke way. And the two pieces I need are not near each other but where they are matters and it’s easy (for an LLM) to pair them wrong. Banged my head with previous generation AI on this kind of stuff to endless frustration. Now, Claude Code can just do it.
I need information on X for each of the fifty states. There are going to end up being fifty different methods needed to get this because states are annoying. If I have to hold an LLMs hand through the fifty different approaches, it’s not worth my time and it’s faster to just do it manually. If I can pass off a single inquiry to someone competent, it’s a big help.
I want to stress test a dataset to see if it’s actually any good—you need to do a mix of work that’s internal to the data and comparison to other resources and try to merge it against stuff that’s in a totally different format and so on. This needs to iterate based on results so far, so you need a model that can actually check well as it goes.
Finally able to do help with analytical writing based on specified sources.
There’s a fairly large (1,800 forecaster) market on AGI timelines at Metaculus (albeit with a contestable definition).
The market has had a fairly stable forecast of mid-to-late 2033 over the last six months or so. In the last couple of days, the median has shifted from May 2033 (Tuesday) to October 2032 this morning.
I wonder what that’s about. The date of the move doesn’t really align with any new releases or any breakthroughs that I’m aware of (unless it was partially an anticipatory move on GPT-5.4 rumors). Maybe it’s just noise?
Note it was forecast for May 2030 exactly one year ago. It’s been fluctuating from 2030-2034 ever since GPT-4 was dropped almost exactly 3 years ago, with a few extended periods closer to both the high and low ends. I think it’s mostly noise.