claytongulick

> It is looking entirely plausible to me that Anthropic will make a profit in 2027.

Then you should invest heavily in their IPO, I suppose.

You'll do much better than those of us who believe there is no way their financials can ever work.

I came to my conclusion a different way, I ran different models locally and measured the amout of resources required for basic things. From that I extrapolated some napkin unit economics, and I couldn't find any way to build a profitable business from it.

I don't assume I'm the smartest guy in the room though, so for all of our sake, I hope you're right.

I'm working on acoustic levitation using ultrasound.

Starting with a parabolic approach for this initial version, but the next version I'm working on uses a much more complex flat plane with phased array of 40khz ultrasound transmitters.

Just for research purposes, no real goal in mind other than learning about it.

From personal experience, what I've seen is the opposite. The less "gifted" attempt to lean on AI to close capability gaps, with results that are amusing at best, and disastrous at worst.

Oh, I'm well aware of many AI crap apps, and that lots of people use these things instead of their brains.

Doesn't change anything about my point.

I'd argue that it's shown something slightly different, it's related but maybe not in the way some people hoped.

I think it sums up to something along the lines of "you get what you pay for".

> It’s not a straight line from AI -> $$$

That seems like it might be a bit of a problem for those frontier model CEOs who have been making exactly the opposite claims.

In the "theory of mind" sense, yes, probably.

These fall under the class of problems that have solutions which can be inferred more easily from applying massive computation, which is a subset of problems that human minds are trying to solve.

It's like being a CIA analyst, maybe. You have an overwhelming number of information sources of varying quality and non-obvious relationships. Applying massive computation will definitely allow you to draw new insights and identify trends that would have been difficult, or nearly impossible for unassisted human reasoning.

From what I understand, this is the sort of thing that the LLMs have been particularly effective at in mathematics, applying massive computation to the existing field and with a combination of impressive vector space search and runtime evaluation, deriving plausible solutions to certain narrowly defined problems.

Here's a harder one: you're in a bar, sitting having drinks with friends. There's an attractive girl sitting with some of her friends that has glanced at you a couple of times. What should you do?

Taking into account, of course, the entire human experience from marriage status, age, rejection likelihood, your friends opinions, her friends opinions, your family, your hormones, everyone's intoxication level, how well you slept, the noise level, how long her glance lingered, and the billions of other things that go into it - entire lifetimes of experience, genetic behavior predisposition, etc...

This is just a small example of a brief problem that a normal human has to solve hundreds or thousands of times per day that don't fit into the class of solutions that LLMs would be particularly adept at. It's easy to come up with a lot more.

So yes, I'd argue that those neatly formulated, clean problems with clearly provable solutions are low hanging fruit.

I think Sutton agrees [1].

[1] https://youtu.be/ThFq87Rp21s?is=xvgU-cKCpK0KwG_M

This is cool, I remember either OkCupid of Plenty of Fish, used to be a really interesting exercise in self reflection.

You could only see someone else's answer to a question if you answered it.

The questions would sometimes get deeply personal.

It would make me self-reflect a lot on what an honest answer was, and whether I wanted other people to see my answer.

And if not, why? Was there something I was embarrassed about? Should I be? If so, should I fix that thing?

I didn't really care about the dating aspect as much as the exercise in critical self evaluation.

Damn, thank you for that.

Sums up my feelings of the last few years nicely.

That reply felt like it reinforced the parent's point.

I apologize for my skepticism, but I have literally heard this same claim made with every single generation of models that comes out.

For a while, I dutifully believed it, and would go try again, and it would be awful.

I'd then have another conversation like this, where someone would say something along the lines of, "sure, sure but this NEW model now fixes all that and is really awesome!"

I'll revisit it when I start seeing some real studies that show real productivity increases rather than stuff that just shifts the burden of broken slop onto PR reviewers while claiming awesome speed.

I wonder.

In my optimistic days, I think we'll start putting mechanisms in place that will have LLMs help develop cognition, skills and expertise. It'll usher in an era of mastery and wonder.

On my bad days, I despair that the siren song of "good enough" slop will overwhelm even the most disciplined minds, and we're facing the atrophy of civilization.

Will this just end up exaggerating the pereto distribution? Will there end up being this tiny percentage of people that are driven by natural curiosity to do the very hard work of really understanding hard things? Will society even have any sort of reward for those people?

Coding in assembly is absolutely fantastic for understanding how programs execute, and different chip architectures.

It's also a lot of fun to do on tiny 8 bit microcontrollers like the AVR. It's really neat to be able to understand exactly how many clock cycles some code will use. This came in super handy for me once when I wrote a small bit-banging serial interface and had to debug why some characters would randomly be wrong.

One thing I had difficulty with was understanding how strict I need to be.

I have to read the ingredients on everything I buy, I have to ask and (embarrassingly) tell restaurants about the allergy.

If I get any milk product at all it'll trigger blisters.

Took my wife a long time to convince me, but she was right.

Brigading is a real thing and hard to combat, so the votes don't necessarily carry the signal they appear to.

I really wish they had a SOHO offering, would love to run this with NextCloud and personal projects from home.

Hi, sorry if this is OT, but I wanted to chime in here on the off chance I can help, because I had maybe the same problem for years and it got really bad - no doctor or dermatologist could help.

Do you get small blisters on your hands and maybe feet? If so, it's called dyshydrotic eczema.

I was told for years that it was an autoimmune disorder, had tons of different treatments and specialists, only to discover it was an allergy to milk protein. I can't have any mammal milk product, no butter, cheese, etc... but I'm completely cured.

As part of this journey I also found out that a lot of women develop it as a result of nickel allergy and Low Nickel Diet helps.

Sorry to go off the rails, I just wanted to take the chance that I could help someone avoid everything I went through.

I knew a burner (Burning Man) that went by that moniker.

> claiming baselessly things like "polyamory" was widespread among "the movements" adherents.

Which is especially weird to do in 2026. I mean, I get the salacious clickbait, but seriously, at this point does anyone really give a shit what consenting adults do in their private lives?

I remember when outrage peddler Laura Loomer did a big pearl clutch on X about some other conservative being into consensual non-monagamy, and the collective response from everyone from all political stripes was a giant shrug.

I guess I'm old, and there's a lot of grey in my beard but... whatever happened to just, you know, writing code?

Simple code. Nothing clever, just logic and clear expression of what needs to be done.

It's so pleasant to just write code that does things.

Code that you can put a breakpoint on to debug.

Code that doesn't go through a library abstraction, a framework, a philosophy, three AI agents, a complex type system, metaprogramming and a cross compiler to run.

I understand that maintainable code needs organization, but I think we go off the rails when the systems of organization dominate the work, and the actual execution logic seems secondary.

I see so many teams spend endless cycles debating theories of frameworks, fp vs oop, type systems, AI rigs, etc..., and barely any time at all on solving the actual problem.

I'm interested in this, but I can't understand what it is at all from the website.

Could it be that training is really expensive?

An industry trying to figure out how to cut expenses, reduces the pace of training under the guise of "safety".

Coincidence?

I think the (currently) intangible skill that we need to develop is when and how the LLMs are appropriate to use. I'm not sure that's possible- I agree with the author's drug analogy.

I generally don't use it for code - I've been writing code for 30 years, so I'm pretty quick, and it's more productive when I look at total time for me to just write things myself.

But not always.

For example, I was recently writing some firmware in MicroPython and needed to implement Bluetooth to control the device.

It's been years since I've written python, since before asyncio. I'd never written MicroPython and didn't really know anything about Bluetooth or BLE.

The AI taught me about how to do it, taught me about how asyncio works (I'm deeply familiar with the model, just not in python), taught me what how Bluetooth works, what GATT characteristics are and gave me some example code that I then took and rewrote to fit my architecture.

I tried to do the normal "find a tutorial on the internet thing" first, but it's filled with worthless AI slop, ads and examples that are so trivial they're only clickbait. It's incredibly frustrating.

Meanwhile, the LLM was succinct, helpful and able to iteratively answer each question I had as learned and got deeper into it, with links to source references I could use to verify the information.

So, basically, a better search engine.

I've been writing code professionally since the 90s, and struggled through learning new things more times than I can count. This was by far the best experience I've had. It was where LLMs really shine.

But, that being said, at the end of the day I wrote the actual code myself and structured it to fit in with the rest of the architecture for the firmware.

I understood everything and when I had to pass it off to another engineer, I was able to describe everything, how it works, and why it's structured the way it is.

I don't think I agree with this, most of the regulation I see is for patient protection. Stark, AKS, HIPPA, etc... are all strong regulation aimed at protecting individuals.

From fee-splitting prohibitions to FDA regulated medical devices, almost all of the day to day regulations I've dealt with in healthcare are squarely aimed at ensuring safety, protecting privacy, preventing fraud and controlling costs.

I'm not sure anyone has solved the problem, though.

"For profit" isn't really the problem, fee for service is.

Value based arrangements and capitated payment structures are real improvements.

When you don't have any market forces constraining utilization, you get massive access problems.

> Because it's mostly not true. It is likely that it used the professors work

These two statements appear contradictory.

I simply said that it may have trained on a NYU professor's work.

Work that the professor did not believe he was releasing for model training purposes. That feels worthy of mention.

And it may have trained on a NYU professor's work.

Looks really interesting, but I notice that Bluetooth isn't in the supported services.

Sort of limits how useful it would be for me - Bluetooth is one of the only reasons I'd do a native app instead of PWA.

And the 6 year old doesn't cost more than the GDP of a medium sized country.