peterbonney

> I'm baffled that you find the mechanics of human motivation hard to understand, (presumably ;) being a human yourself.

But that’s exactly it! The mathematicians are framing this as being bad for human understanding of math, but it’s obviously really about their own motivations for developing that understanding! Which, again, goes back to the obvious conclusion that what they have lost is the extrinsic motivation for being first, not the intrinsic motivation of developing understanding.

That is totally fine! I wouldn’t be motivated to do my job if I wasn’t extrinsically rewarded for doing so. But I am also not feeding anyone a line of BS about how it’s actually some other higher-minded motivation driving my work.

I’m empathetic to the instability of the extrinsic rewards for doing math research as a profession in the current moment. I am not at all empathetic to an attempt to manipulate me into thinking that the real problem is something other than that.

It’s like an entrepreneur complaining that they can’t build a business that would better humanity because of high taxes. That’s fine - but the motivation lacking there is obviously making money, not bettering humanity.

> It is developing new methods and deepening understanding to drive useful applications.

Knowing that a solution exists is not an impediment to deepening your understanding of a problem.

For example: Knowing that there is a proof of Pythagoras’ theorem is not an impediment to thousands (millions?) of geometry students developing the intuition that comes from drawing the famous diagram where the number of squares visually add up to one another.

What about the existence of an AI-generated proof prevents a mathematician from working through the problem on their own to deepen their understanding, as if the proof didn’t exist in the first place?

That’s what is hard to understand.

> We are being robbed of the opportunity to find the solution by being handed the solutions.

I find this argument difficult to understand. As a math student in college with no ambitions (or realistic opportunity or aptitude) to pursue original contributions to the field, everything I learned was by definition already solved. Nevertheless I was able to - required to, in fact - work through each solution in order to develop understanding. And I did in fact learn by this method, as countless other students did before and after me.

So when I hear this argument from mathematicians, all I can hear is that they are frustrated at being out back into the position of being students, where the end result of the hard work of understanding is just the understanding itself without the prize of a new discovery. If they want to find the solution without reading it, they are still very much able to do so, just as their students do every day.

I’ll do it later

The total open interest of all major prediction markets is barely over a billion dollars. (1) That's the total amount of money currently wagered on ALL contracts across all platforms.

There are roughly 2,500 individual companies listed in the US alone with a market cap that exceeds 1 billion dollars. Tesla's market cap is 1.4 trillion - about 1,000 times the size of all prediction markets put together.

In other words, to the extent this incentive is of societal concern, it is not because of prediction markets - that is the tail wagging the dog.

Edit: to be clear I think these markets are a scourge as they currently operate, but not for this reason.

(1) https://predik.io/en/blog/open-interest-mercados-prediccion-...

I had the incredible good fortune to take one of his classes in college, and I loved it so much I took another just to learn from him again. A tremendous intellect AND an incredibly engaging and talented instructor. It would be an exaggeration to say that I knew him, but nevertheless he had a great impact on my education and my life. He will be missed.

I’m not saying it is definitely a hoax. But I am saying my prior is that this is much more likely to be in the vein of a hoax (ie operator driven, either by explicit or standing instruction) than it is to be the emergent behavior that would warrant giving it this kind of attention.

Correct, I haven’t set it up that way. That’s my point: I’d have to set it up to behave in this way, which is a conscious operator decision, not an emergent behavior of the bot.

Well I lol’d :)

Yes, this is the only plausible “the bot acted in its own” scenario: that it had some standing instructions awaiting the right trigger.

And yes, it’s worrisome in its own way, but not in any of the ways that all of this attention and engagement is suggesting.

Of course it’s capable.

But observing my own Openclaw bot’s interactions with GitHub, it is very clear to me that it would never take an action like this unless I told it to do so. And it would never use language like this unless unless I prompted it to do so, either explicitly for the task or in its config files or in prior interactions.

This is obviously human-driven. Either because the operator gave it specific instructions in this specific case, or acted as the bot, or has given it general standing instructions to respond in this way should such a situation arise.

Whatever the actual process, it’s almost certainly a human puppeteer using the capabilities of AI to create a viral moment. To conclude otherwise carries a heavy burden of proof.

This whole situation is almost certainly driven by a human puppeteer. There is absolutely no evidence to disprove the strong prior that a human posted (or directed the posting of) the blog post, possibly using AI to draft it but also likely adding human touches and/or going through multiple revisions to make it maximally dramatic.

This whole thing reeks of engineered virality driven by the person behind the bot behind the PR, and I really wish we would stop giving so much attention to the situation.

Edit: “Hoax” is the word I was reaching for but couldn’t find as I was writing. I fear we’re primed to fall hard for the wave of AI hoaxes we’re starting to see.

My understanding is that the existence of the nuclear triad is entirely about maximizing the likelihood of maintaining a second strike capability in the event of a preemptive nuclear attack, thus providing mutually assured destruction even if the first strike succeeds.

Dark stuff.

I like this piece. Doing hard things for the simple reason that they're hard is good for the psyche. I truly believe that.

The devil is really in the details on how the orders were executed in the backtest, slippage, etc. Instead of comparing to the S&P 500 I'd love to see it benchmarked against a range of active strategies, including common non-AI approaches (e.g. mean reversion, momentum, basic value focus, basic growth focus, etc.) and some simple predictive (non-generative) AI models. This would help shake out whether there is selection alpha coming out of the models, or whether there is execution alpha coming out of the backtest.

Having selected a 401k provider for a small (<15 person) company and also for a larger (>100 person) one, I can say that the big names make it prohibitively expensive for small companies to use them. And that expense ultimately comes out of peoples’ retirement funds in the form of fees. They frankly don’t want the business - too much compliance overhead for a small asset pool.

Believe me, I would prefer to have my own 401k at Fidelity too.

I have no dog in this fight, I just know from experience that setting up a 401k for your company is vastly different from setting up a brokerage account, and the reason a lot of small companies end up with off-the-run vendors is because those are the ones that will take the business.

"weird, overconfident interns" -> exactly the mental model I try to get people to use when thinking about LLM capabilities in ALL domains, not just coding.

A good intern is really valuable. An army of good interns is even more valuable. But interns are still interns, and you have to check their work. Carefully.

You're right, my view is way out of date. I didn't realize CBOE had grown so much in straight equities trading. IEX is the best of the rest, but it's NYSE, NASDAQ and (to my surprise) CBOE as the clear top 3.

I'd say IEX has done remarkably well - it's not likely to displace NASDAQ or NYSE but it has solidified its place as the #3 US exchange by any reasonable measure. If TXSE achieves comparable market share I'd call that a wild success.

You're not wrong to say that most participants don't care about what IEX offers, but enough do to make a meaningful dent in trading volume.

Exactly. Government entities have a funny habit of making their own decisions about what (and who) is and is not subject to their jurisdiction.

The more I learn about how AI companies trained their models, the more obvious it is that the rest of us are just suckers. We're out here assuming that laws matter, that we should never misrepresent or hide what we're doing for our work, that we should honor our own terms of use and the terms of use of other sites/products, that if we register for a website or piece of content we should always use our work email address so that the person or company on the other side of that exchange can make a reasonable decision about whether we can or should have access to it.

What we should have been doing all along is YOLO-ing everything. It's only illegal if you get caught. And if you get big enough before you get caught then the rules never have to apply to you anyway.

Suckers. All of us.

Thank you. The New York City subway has its issues, but most of them boil down to the fact that the system is really old and hard to modernize.

It also has unique strengths arising from the extreme population density and the inherent 24/7-ness of NYC (not to mention Manhattan's unique geography) but people don't talk about them as much as its flaws.

If your only reference point is transit in other US cities it's hard to grasp how different the NYC subway experience is, at least in Manhattan. Trains come every 5-10 minutes even at off-peak hours, and it's almost always busy. It's just not a very conducive environment for crime, unless you're riding in the middle of the night and/or at the tail ends of the system where density is lower.

When it comes to thinking about my own personal safety, I don't worry about crime on the subway, I worry about getting hit by a truck or e-bike rider.

That might be the case in other cities, but in NYC the socioeconomic dynamics are less clear. It’s mostly affluent-to-rich suburbanites that drive to work from outside the city, with rich and poor city residents primarily taking public transit (and to a lesser extent using taxis and car services). Almost no city residents - rich, poor or in between - drive to and from a 9-5 job in Manhattan.

Pricing is hard, no question about it.

One solution to the “not all users are equal” problem is to create different types of users and price accordingly. That’s what we’re doing, and it’s working out well so far. It depends on the product and use case, of course.

If you looked at how the average accountant spent their time before the arrival of the digital spreadsheet, you might have predicted that automated calculation would make the profession obsolete. But it didn't.

This time could be different, of course. But I'll need a lot more evidence before I start telling people to base their major life decisions on projected technological change.

That's before we even consider that only a very slim minority of the people who study math (or physics or statistics or biology or literature or...) go on to work in the field of math (or physics or statistics or biology or literature or...). AI could completely take over math research and still have next to impact on the value of the skills one acquires from studying math.

Or if you want to be more fatalistic about it: if AI is going to put everyone out of work then it doesn't really matter what you do now to prepare for it. Might as well follow your interests in the meantime.

Correct.

I used to have an orchard with c. 35 apple trees, 2 of which were honeycrisp. I can confirm that they are tricky trees to manage. I was theoretically in a good area for honeycrisps. But the trees were prone to all sorts of maladies that didn’t affect my other varietals, including antique varietals that are traditionally thought of as “difficult”. And when they did grow fruit it was usually small and misshapen.

Apples are interesting and this is a great example of the unexpected challenges you can face growing them. Every honeycrisp tree is a perfect clone of the very first one, but the environment of each is not a perfect clone of their original environment. And the interplay of genetics and growing conditions can have very unpredictable results.