I myself am saving a small fortune on design and photography and getting better results while doing it.

If this is not all that well I can’t wait until we get to mediocre!

Generative AI, as we know it, has only existed ~5-6 years, and it has improved substantially, and is likely to keep improving.

Yes, people have probably been deploying it in spots where it's not quite ready but it's myopic to act like it's "not going all that well" when it's pretty clear that it actually is going pretty well, just that we need to work out the kinks. New technology is always buggy for awhile, and eventually it becomes boring.

Then Gemini got good (around 2.5?), like I-turned-my-head good. I started to use it every week-ish, not to write code. But more like a tool (as you would a calculator).

More recently Opus 4.5 was released and now I'm using it every day to assist in code. It is regularly helping me take tasks that would have taken 6-12 hours down to 15-30 minutes with some minor prompting and hand holding.

I've not yet reached the point where I feel letting is loose and do the entire PR for me. But it's getting there.

I work commercializing AI in some very specific use cases where it extremely valuable. Where people are being lead astray is layering generalizations: general use cases (copilots) deployed across general populations and generally not doing very well. But that's PMF stuff, not a failure of the underlying tech.

> In 2029, AI will not be able to watch a movie and tell you accurately what is going on (what I called the comprehension challenge in The New Yorker, in 2014). Who are the characters? What are their conflicts and motivations? etc.

> In 2029, AI will not be able to read a novel and reliably answer questions about plot, character, conflicts, motivations, etc. Key will be going beyond the literal text, as Davis and I explain in Rebooting AI.

> In 2029, AI will not be able to work as a competent cook in an arbitrary kitchen (extending Steve Wozniak’s cup of coffee benchmark).

> In 2029, AI will not be able to reliably construct bug-free code of more than 10,000 lines from natural language specification or by interactions with a non-expert user. [Gluing together code from existing libraries doesn’t count.]

> In 2029, AI will not be able to take arbitrary proofs from the mathematical literature written in natural language and convert them into a symbolic form suitable for symbolic verification.

Many of these have already been achieved, and it's only early 2026.

[1]https://garymarcus.substack.com/p/dear-elon-musk-here-are-fi...

The goalposts keep getting pushed further and further every month. How many math and coding Olympiads and other benchmarks will LLMs need to dominate before people will actually admit that in some domains it's really quite good.

Sure, if you're a Nobel prize winner or PhD then LLMs aren't as good as you yet, but for 99% of the people in the world, LLMs are better than you at Math, Science, Coding, and every language probably except your native language, and it's probably better at you at that too...

Even as I use it, and I use it everyday, I can't really assess its true impact. Am I more productive or less overall? I'm not too sure. Do I do higher quality work or lower quality work overall? I'm not too sure.

All I know, it's pretty cool, and using it is super easy. I probably use it too much, in a way, that it actually slows things down sometimes, when I use it for trivial things for example.

At least when it comes to productivity/quality I feel we don't really know yet.

But there are definite cool use-cases for it, I mean, I can edit photos/videos in ways I simply could not before, or generate a logo for a birthday party, I couldn't do that before. I can make a tune that I like, even if it's not the best song in the world, but it can have the lyrics I want. I can have it extract whatever from a PDF. I can have it tell me what to watch out for in a gigantic lease agreement I would not have bothered reading otherwise.

I can have it fix my tests, or write my tests, not sure if it saves me time, but I hate doing that, so it definitely makes it more fun and I can kind of just watch videos at the same time, what I couldn't before. Coding quality of life improvements are there too, I want to generate a sample JSON out of a JSONSchema, and so on. If I want, I can write the a method using English prompts instead of the code itself, might not truly be faster or not, not sure, but sometimes it's less mentally taxing, depending on my mood, it can be more fun or less fun, etc.

All those are pretty awesome wins and a sign that for sure those things will remain and I will happily pay for them. So maybe it depends on what you expected.

The irony of a five sentence article making giant claims isn't lost on me. Don't get me wrong: I'm amenable to the idea; but, y'know, my kids wrote longer essays in 4th grade.

COULD I do this stuff before? Sure. But I wouldn’t have. Life gets in the way. Now, the bar is low so why not build stuff? Some of it ships, some of it is just experimentation. It’s all building.

Trying to quantify that shift is impossible. It’s not a multiplier to productivity you measure by commits. It’s a builder mind shift.

And yes, I do understand the code and what is happening and did have to make a couple of adjustments manually.

I don't know that reducing coding work justifies the current valuations, but I wouldn't say it's "not going all that well".

Right around then, we can send a bunch of reconnaissance teams out to the abandoned Japanese islands to rescue them from the war that’s been over for 10 years - hopefully they can rejoin society, merge back with reality and get on with their lives

...and yet we still see these articles claiming LLMs are dying/overhyped/major issues/whatever.

Cool man, I'll just be over here building my AI based business with AI and solving real problems in the very real manufacturing sector.

1) https://en.wikipedia.org/wiki/Gartner_hype_cycle

or

2) "First they ignore you, then they laugh at you, then they fight you, then you win."

or maybe originally:

"First they ignore you. Then they ridicule you. And then they attack you and want to burn you. And then they build monuments to you"

Second of all, GenAI is going well or not depending on how we frame it.

In terms of saving time, money and effort when coding, writing, analysing, researching, etc. It’s extremely successful.

In terms of leading us to AGI… GenAI alone won’t reach that. Current ROI is plateauing, and we need to start investing more somewhere else.

Yeah you could ask ChatGPT or Claude to write code, but it wasn't really there.

It needs a while to adopt the model AND the UI. As in software are the first one because we are both makers and users.

But can they write grammatically correct statements?

Almost everyone around me, even the primary school kids use ChatGPT/Perplexity/Gemini/Claude in some form on almost a daily basis. The daily engagement is v strong.

The models keep improving every year. Nano banana gets text spot on, human anatomy of digits and toes is spot on. Deep Research mode is mind boggling. All the major vendors have some form of voice interaction, and it feels pretty good. I use perplexity talk feature while driving to learn deep about a topic of interest.

The trend is strong, betting against the trend isn't wise.

I can paste entire books and ask questions about certain pieces. The context windows nowadays are wild.

Price per token keeps on dropping, more capability keeps on coming online.

Gary offers no solutions, just complaints.

Then you consider the massive spend in data centers, the ram shortage, etc. The writing is on the wall.

As said in the article, a conservative estimate is that Gen AI can currently do 2.5% of all jobs in the entire economy. A technology that is really only a couple of years old. This is supposed to be _disappointing_? That’s millions of jobs _today_, in a totally nascent form.

I mean I understand skepticism, I’m not exactly in love with AI myself, but the world has literally been transformed.

gpt-oss isn't bad, but even models you cannot run are worth getting since you may be able to run them in the future.

I'm hedging against models being so nerfed they are useless. (This is unlikely, but drives are cheap and data is expensive.)

I hate generative AI, but its inarguable what we have now would have been considered pure magic 5 years ago.

Seems like black and white thinking to me. I had it make suggestions for 10 triage issues for my team today and agreed with all of its routings. That’s certainly better than 6 months ago.

I just used ChatGPT to diagnose a very serious but ultimately not-dangerous health situation last week and it was perfect. It literally guided me perfectly without making me panic and helped me understand what was going on.

We use ChatGPT at work to do things that we have literally laid people off for, because we don't need them anymore. This included fixing bugs at a level that is at least E5/senior software engineer. Sometimes it does something really bad but it definitely saves times and helps avoid adding headcount.

Generative AI is years beyond what I would have expected even 1 year ago. This guy doesn't know what he's talking about, he's just picking and choosing one-off articles that make it seem like it's supporting his points.

You're not losing your job unless you work on trivial codebases. There's a very clear pattern what those are: startups, greenfield, games, junk apps, mindless busywork that probably has an existing better tool on github, etc. Basically anything that doesn't have any concrete business requirements or legal liability.

This isn't to say those codebases will always be trivial, but good luck cleaning that up or facing the reality of having to rewrite it properly. At least you have AI to help with boilerplate. Maybe you'll learn to read docs along the way.

The people claiming to be significantly more productive are either novice programmers or optimistic for unexplained reasons they're still trying to figure out. When they want to let us know, most people still won't care because it's not even the good kind of unreasonable that brings innovation.

The only real value in modern LLMs is that natural language processing is a lot better than it used to be.

Are we done now?

The same goes for code as well.

I’ve explored Claude code/antigravity/etc, found them mostly useless, tried a more interactive approach with copilot/local models/ tried less interactive “agents”/etc. it’s largely all slop.

My coworkers who claim they’re shipping at warp speed using generative AI are almost categorically our worst developers by a mile.