marsten

The SEC's job isn't to adjudicate on whether Anthropic is a good or bad bet for investors. Its job is to ensure that investors have adequate visibility, and then get out of the way.

It would be VERY BAD if we gave politicians arbitrary veto power over who IPOs and who doesn't. The threat of corruption should be obvious.

The simple explanation is that parents don't want to give their sons gender-ambiguous names, but are more open to it for daughters. This is consistent with all of the dynamics described in the article.

Why that is the case is another question.

The economics of that don't work out. Government is a small market and can't drive frontier innovation at the scale it's happening today, let alone in the future.

Look at the defense industry: The Big 5 contractors (Boeing, RTX, General Dynamics, Lockheed Martin, Northrop Grumman) capture most of the discretionary military budget, and the largest of them is in the $300B valuation range (Boeing, which has substantial non-government revenue). Fine businesses but nowhere near Anthropic's valuation.

Bottom line, the government doesn't have deep enough pockets.

In many ways investing in a very late IPO like SpaceX, Anthropic, or OpenAI is the worst of all worlds for the public investor.

On the one hand you'll never see a 100x return – that's been captured by private equity. But there's still a lot of long-term risk because these companies haven't proven they can deliver free cash flow to justify their valuations. Investing in SpaceX or Anthropic at $1T valuation is very different from investing in Apple or Nvidia.

I've been a continuous subscriber since the early 1980s.

The magazine's low point was the infamous op-ed in 2021 about E.O. Wilson a few days after the biologist's death. It was basically a woke hit piece accusing him of scientific racism and white supremacy, with no evidence from his body of writing. It also criticized the genetics community at large for the term "normal distribution", which it took as offensive for implying that anyone outside the peak is "abnormal". (Never mind that "normal" has a technical definition in statistics.)

It's improved a lot over the last few years.

Still, I fear it may soon go the way of BYTE. Not because the need isn't there or because the market is gone, but because people get information in such different ways now.

There's also the way AI itself tends to work: In my experience it very rarely refactors existing code when it's adding something new.

AI tends to layer new code on top and you get a steaming mess very quickly if you aren't careful.

Democracy never handles situations well where one side of an issue is a small highly motivated group, and the other side is a larger group that kinda-cares.

It's the reason we subsidize farmers. It's the reason our tax code remains so complicated. It's also why it's hard to get anything built: Existing owners are highly motivated to block development, and the potential beneficiaries (people who might someday live there, etc.) are too diffuse to mount a defense against them.

Math is likely moving into a big science era, much like experimental particle physics and astronomy did before it.

Once AI mines the easy unsolved problems, it will take large token budgets to make meaningful advances. That will need to be funded and planned at a societal level, like the great facilities of the other big sciences.

Mathematics will become more like an observational science, with some mathematicians specialized in interpreting the results that come out of the big AIs.

> The AI was able to race its car very well, but it took a lot of risks that a human player probably would not.

There is a parallel with autonomous vehicles in real life. On northbound 1 in SF going through GG Park, the left turn lane onto Crossover Drive is always backed up. Waymos often do a very late merge into that turn lane in order to jump the queue and save time. With 360 degree sensing they can do this safely in real time but it feels too risky for most humans to attempt.

This is the direction of experimental particle physics and observational astronomy, where the budgetary scale at which progress occurs means we now fund these efforts at a societal level. These fields have graduated beyond "tabletop science".

For 3000 years mathematics has only been a "tabletop science". Even big programs like the classification of finite simple groups have been comprised of small teams chipping away at different (publishable) parts of an overall program.

This latest Navier-Stokes advance cost something like $22m in tokens, already well beyond what a mathematician's research grant can fund. As the easy open problems get mined, the cost of frontier progress will continue to climb. Some part of mathematics as a field will need to transition from tabletop science to big science: Coordinated top-down programs addressing high-priority objectives.

TBD is what the role of individual mathematicians will look like in a "big science" paradigm, but we could look to experimental high energy physics for ideas. For all practical purposes, AI converts math from a theoretical field into an experimental/observational one.

What is extremely valuable in slime mold organizations is clear direction from above on the relative importance of different objectives.

This doesn't have to be very specific - senior people often aren't in a position to decide on specifics. But even very general feedback can help the rank and file cut through "what should we do?" disagreements. In my experience those disagreements drain productivity faster than anything.

I think most people see AI as an economic inevitability, immune to what any of us thinks. Why spend the time and energy making a reasoned critique if nobody has a real choice in the end? It's like railing against the concept of money or the existence of the mosquito.

Alas I suspect that no combination of business and technical decisions could have allowed Commodore to succeed long term. They never had the scale or product market fit to succeed in the 32 bit era. Apple barely made it.

I too learned C on an Amiga 1000. Somehow I scrounged together $300 to buy the Lattice C compiler and all its manuals – a steep price in 1985-86. Funny to think that much better tools are now free.

A lot of people believe in Musk and will invest in anything he wants to do. Is this rational? It depends on how it plays out.

I think we may be seeing a new type of capitalism that maybe Steve Jobs and Warren Buffet hinted at: A business empire built around the outsized ambitions of a single charismatic individual. The valuations of Tesla and SpaceX only make sense if you attach an enormous premium to Musk the individual.

Second-highest P/E is Tesla, currently at 97.

Apart from Palantir and Tesla, the other big companies are trading at what would historically be considered reasonable P/Es given their growth rates and profitability.

What's really changed in the last 20-30 years is the incredible profit generated by the tech industry, and the defensive moats the biggest companies have built.

Japan is a classic example. You can drop your wallet there and someone will send it to you with all the cash intact. It makes you realize how much overhead it causes when you need to guard against cheaters and thieves.

I was an undergrad at Caltech in the late 80s and likewise it never even occurred to us to cheat on take-home exams. Maybe things have changed.

People did plenty of collaboration on homework sets. Some of the harder ones were almost impossible unless you did, like those 20 page Phys98 homework sets...

Academic integrity committees at prestigious schools are horribly lax. They want these types of issues to go away quietly.

I have a friend who in college had another student take his test from the "complete" pile, erase my friend's name, and put on his own instead. It was only through blind luck that my friend figured it out. He, the TA, and the professor reported it – with smoking gun proof – but nothing happened.

The same laxness applies to academic research integrity. Universities rarely punish academics who are discovered to falsify data.

This is exactly right. Gone are the days when you could get a C+ average at Harvard and still land a good job or a spot in a prestigious law program – purely by virtue of having gone to Harvard.

Everyone is in competition now. Everyone has to prove their worth, all the time. It's more egalitarian but it also creates a lot of stress.

If you posit that Altman suffers from main character syndrome (as many CEOs do), then he likely believes that he alone can lead OpenAI to success. In this case, doing whatever it takes to get himself back into the job is by definition justified. It's obviously worth stepping on a few toes if the success of the company is at stake.

Anthropic asking hypothetical questions in an interview doesn't seem like a very good signal. Everybody knows what they're supposed to say. If they want an unfakeable signal they should make offers with no equity component.

CEOs are hired to run companies and make themselves, and their investors, wealthy. That is their prime directive and CEOs are the ultimate partisans.

If a CEO feels that bending the truth, or outright lying, will advance the prime directive – then that is what they will do. Applying adjectives like "honest" or "untrustworthy" to them is a category error. Altman will say whatever benefits OpenAI, full stop. Musk will say whatever benefits his interests, full stop.

CEOs can't be good or bad people in a moral sense, or have the best interests of society at heart. (Despite what they may try to convey.) Better to think of them as automatons carrying out well-defined, and ultimately simple, goals.

> You cannot be sure that anyone other than yourself is conscious. It is only basic human empathy that allows people to believe that.

In Bayesian terms what makes it reasonable to ascribe consciousness to other people is that (a) other people have an origin that is objectively very similar to your own (genetic origin, embryonic development, birth, gradual acculturation, education, etc.), and (b) you have a firsthand experience of your OWN consciousness.

It would be remarkable if the very small differences (relatively speaking) between you and other people were enough to destroy the experience of consciousness.

Generalizing, the farther you stray from a "common origin story", the more a leap of faith consciousness becomes. Needless to say an LLM is quite a different thing than a human being.

Judging from online reactions to robot testing (e.g., engineers kicking the Spot robot "dog" to test its balance), we humans trigger on some fairly superficial cues when deciding how much to empathize. People express more sympathy for the robot dog than they do for the chicken they ate for lunch – despite the fact that the chicken has a far better claim to consciousness than the robot.

This then is how I interpret "do not anthropomorphize": We should try to ignore the superficial cues when judging the similarity of other beings to ourselves.

Bitcoin attempted to replace cash, but failed because the transaction costs are orders of magnitude too high. The high transaction cost of zero-trust makes it a desirable medium of exchange only for criminals and scammers.

In an effort to make Bitcoin a reasonable medium of exchange, various businesses arose to act as intermediaries/market makers. But this violates the trust-free model – and many of those intermediaries have proven to be outright scams. It turns out that trusting an intermediary to handle your cryptographically untraceable asset is not a wise thing to do.

So that leaves Bitcoin in a similar category as gold. You're either a paranoid type who doesn't trust anyone, in which case holding and transacting the asset is a hassle. OR you extend trust to various intermediaries (gold ETFs, bitcoin ETFs for example) and treat it as just another tradable financial asset.

Bitcoin is undeniably the cleverest way anyone ever became a billionaire. Nakamoto's sole contribution was posting an anonymous 9 page whitepaper to the internet and voila, today he (or she, or it) is worth $80+ billion.

I can see Apple's position: The Mac has been through four major ISAs and the value to most users of maintaining that entire stack indefinitely in the current macOS is nil.

A number of digital preservation standards are emerging (Wasm, MAME/MESS, EaaSI, Olive, ...) and I would like to see a legal requirement on OS vendors that platforms that are no longer supported must be made available to archivists.

More likely they would buy the assets of AI companies for pennies on the dollar. There could be a lot of H100s floating around at fire sale prices. Or they would acquire these companies for talent.

Google did this for several years in the early 2000s – snapping up talent and data center capacity from the casualties of the dotcom bust.

Unless they change their tack, Apple is unlikely to go head to head against Microsoft 365 or Google Workspace because they only target their own ecosystem (macOS/iOS). For MS and Google these Apple-owned platforms are not their primary user base.

In fairness to all concerned, the MacOS to MacOS X transition was brilliantly executed. These days we take VMs for granted, but back then it was a novel idea to run MacOS 8 as a process inside of MacOS X (the "blue box"). For most users it was seamless.

In the grand scheme it's good to invent things that replace human labor. It frees up people to do more interesting things. The goal should be to put everyone out of a job.

Agents playing the iterated prisoner's dilemma learn to cooperate. It's usually not a dominant strategy to be entirely sociopathic when other players are involved.