Well I "tried it out" I asked it one question, and it gave no answer and said "Sign up to use more!" I don't think I'll be doing that, no.
I can't think of a single dimension grok is winning on (capability, cost, voice), but want to stay open-minded -- anybody want to vouch for its capabilities in any domain?
If you haven't used it, how do you know if it's winning?
I think it's winning on UI for normies (grok bot) and they made some claims about being pareto SOTA (lowest cost per task completed) a while back with 4.6.
I find it to be a perfectly capable model for implementation (there are many in this class--deepseek flash, spark1.3, luna, etc). I find the usage to be very generous w/ supergrok. I find the model to be just fine for 90% of what I want to do, but I use a smarter model to plan complicated things.
After the cursor aquisition it's become a quite capable coding model. If you take cost into account, it's close to the top. OpenAI is maybe still #1, but I'd put Grok at #2 (again, including cost as a factor).
For me it works well for agentic coding tasks and terminal/unix/bash (in cursor and grok build); it's also token efficient and cheaper than gpt 5.6. It's def not as good as Fable for me (I haven't used Astra much, can't comment). So it's not the cheapest, not the most capable, but it has a good mix of it for my backend, go, infra work.
The voice is the weird part. The early Grok 4 models had a very distinct presentation unlike anything else out there. Then suddenly it made a big jump in coding ability and started sounding just like every other model.
> it's also token efficient and cheaper than gpt 5.6.
Deepswe results show that grok 4.6 is more expensive per-task and consistently scores worse than: luna xhigh, glm 5.3, astra low, sol high/xhigh, opus 5 medium.
Grok also used almost 3x as many tokens/turns to complete tasks than all of those models (besides luna), so it takes way more time to complete a task.
There isn't much reason to use Grok at all, it's gotten better but it's still worse than every other player in the field, which shouldn't be a surprise considering until about a year ago they were just buying tokens from other providers and pretending it was their own model.
With gpt-6 luna and sol coming tomorrow it's going to look even worse too, especially if new luna retains the same dirt cheap pricing that 5.6 luna has.
I think this is a bit of a simplistic mental approach. I've certainly seen a lot of "The engineer owns the outcome, AI is just a tool, don't release anything you don't vouch for."
However, I just don't think that's realistic. It's asking an author to suddenly become an editor. It's asking somebody who writes code to now read and debug others code.
It can actually be harder to find the the bug in a tricky piece of code than it can be to write your own correct code from scratch. I see AI introduce all sorts of bugs all the time in my personal projects that I would never introduce, and would never think to test for, especially around anything graphical.
> It's asking somebody who writes code to now read and debug others code.
This has been a big part of the job for anyone on a team for at least 20 years. I do agree that it’s the hardest and worst part of the job, and has now become the majority of the job for anyone who isn’t vibe coding. So, that sucks.
Disagree. At least back in the day there weren't endless comments about how this widget is load-bearing, and how honestly the other widget carries the derived widget, referencing decision ADR-100 that is nowhere to be found. All these comments matter because once accepted as part of the codebase the next LLM takes these comments as canonical.
The largest problem these days is the volume of code developers are expected to review. The volume went up significantly.
This is the best and worst thing about LLM coding agents. They trust comments way too implicitly. And then the errors just keep compounding.
Or a temporary hack that becomes "load-bearing" because the agent doesn't figure out that it's supposed to be a temporary testing shim - instead it keeps building on it until it basically duplicates what it's mocking.
> At least back in the day there weren't endless comments about how this widget is load-bearing
By far the biggest problem 90% of developers have with AI is that they should be turning off comments, as it's clear that the training data they have is no good for developing a theory of mind for an engineer who has to read them.
I've turned them off and add them myself at review time and am quite happy.
Requiring the coding agent to (try to) iterate on code clarity until comments are no longer necessary, probably doesn't hurt either. Save the commentary for conversation logs, agent Markdown files, and other sorts of documentation.
Anybody who has reviewed pull requests can tell you that sooner or later you approve a PR after many rounds of changes because it's finally "good enough".
Fighting with a robot to just do the damned thing is less fraught because they don't get offended by critiques but it takes more round trips to get them pointed in the direction you want.
Fighting with a robot requires also a different kind of attention. When you're reviewing the human code, you can quite easily guess an overall seniority and competency level of the author and then you can adjust your level of attention to every detail. E.g. if the solution requires an understanding of some core idea, ones the human understands this core idea, you can be quite sure that it is consistently implemented everywhere. With AI, 90% of the PR could be expertly implemented but then, for no obvious reason, 10% could be low-quality surprise. I've never seen such unbalanced output from human programmers.
Yes, but those 10% of a problematic code is not easy to find without a very detailed study of the whole PR. And since most of the code looks (and usually is) very well-written, the human brain somehow doesn't expect to find those low-quality or sub-optimal parts in such code. That's why I wrote that reviewing the AI code requires different kind of attention.
> It's asking somebody who writes code to now read and debug others code.
That's exactly right. Open source projects are currently drowning under LLM generated PRs, where those who used to write code are simply punting that work to AI, but still expecting others to review it. It's not okay to expect such a free lunch. If you moved the labour of writing code one step away, then you are yourself the first line of defence now, so you better start reviewing code that you claim to be yours.
That's what I do and expect my colleagues to do. Even before LLMs I was reviewing my own PRs before submitting them to others. I still do that. I work closely with Claude to create something good that I'm happy with, then I review it and test it to ensure it's good. And only then do I submit the PR to colleagues for final review.
I expect the same from colleagues, I'm not interested in treating them as a middle man between me and Claude.
I agree. When you write your own code, you know what your intention was when writing it. Furthermore, as you gain experience and mature you know in the back of your mind that every mistake during code writing costs disproportionately more to fix later on. You only get that feeling by owning the code. AI cannot do that. It can't have skin in the game in that way.
> It's asking somebody who writes code to now read and debug others code.
Writing code has always involved reading and debugging your own code, at an absolute minimum, even if you did everything solo. In any remotely serious collaborative effort, it also involved code review and collaborative debugging; people use issue trackers and assign themselves and each other "tickets", which often involve fixing issues that are ultimately caused by someone else's code.
> It can actually be harder to find the the bug in a tricky piece of code than it can be to write your own correct code from scratch.
Part of the point is to reject tricky code exactly because it is tricky (as this is rarely actually necessary).
> It's asking an author to suddenly become an editor.
It’s asking an author to suddenly become an editor if they decide to use the robot for a task.
Certain workplaces are demanding this - but not all.
Many still just want working commits without tech debt.
In fact, private and public teams alike are backed up at the PR review stage, so, lots of sane places wouldn’t mind individual contributors using the robot less - especially if its use increases the complexity of reviewing the task.
If you can explain how to reproduce a bug, you can ask the AI to debug the code and it usually works, in my experience. If not, you can ask it to add logging or other tools for better observability.
> I see AI introduce all sorts of bugs all the time in my personal projects that I would never introduce, and would never think to test for, especially around anything graphical.
This is referred to in the need for E2E testing and E2E testing not being a substitute.
Code review is definitely the biggest challenge of AI-driven development IMO. I still have not found good processes that work in my org, but for my personal work I independently reached the author's conclusions a while ago and am very satisfied with the results.
> It's asking an author to suddenly become an editor.
I think that’s right, and what is needed. It still gives a significant speed up for coding, while still keeping the output human maintainable.
There is the idea that the agent will just produce binary code directly at some point. I don’t know if it ever comes to that but for now I’m in the ‘I’ve become an editor’ camp.
The time it takes you to review the code the LLM produces is the same amount of time it would take to just write the code yourself. There's no speedup to be had using these things, despite what many claim.
Strongly disagree. I know my codebase well, I know what I'm expecting before I ask Claude to do it, and I tell it what I'm expecting. I might tell it roughly what I want, have it make a plan, review the plan and ask for changes if necessary, then execute.
This way I don't need to scrutinize every detail, I just look over the big picture. I also care a lot more about the big picture - architecture and data flow etc. Basically if you view your codebase as a tree I care much more about the trunk and the big branches than I do about the smaller branches and particularly the leaves. So the details of some little leaf function somewhere are fairly insignificant, it's trivial to change at any time. As long as it works and isn't unreasonably slow it's fine.
Working this way I can get things done in minutes or hours that would previously take days or even weeks.
I'll bet you know it because you wrote and/or worked on it manually, likely over a period of years. The odds of you knowing a slop codebase that well, or even particularly at all, are much lower.
I think the answer is not to debug the code, but, when possible, to debug the outputs. The code may be considered to be a black box much of the time. (This is much more true for my hobby projects than my work projects.)
...because your work projects are bigger, there's only so big a black box can get before you lose all comprehension of it, and splitting it to smaller black boxes the shapes of which you keep refining is programming, and the part of it LLMs currently can't do
I routinely see Astra extract related functionality into it's own file after it gets past a certain size, unprompted.
And prompted it can extract the black boxes if you tell it what the boxes are or what to look for.
Same for cleaning up tech debt after organic development, it's suggestions on how to simplify and modularize are good, but you need to prompt.
Given that the prompts are quite generic, "look for technical debt, suggest simpler architectures, what could be extracted in a separate module", it won't be long till it will do it on it's own.
With directed prompting like "aggressively refactor into short functions with intention-revealing names" (a paraphrase; I'll give it a few sentences to describe a general style), even Luna handles this pretty well IMX. I have too much pride in my own analytical skills to try something like "how would you refactor this for clarity?" followed by "do it", but I imagine it would at least result in a step in the right direction.
I feel like this is a willfully confusing article. So autism may involve disrupted genetics, but you may not have inherited those genes from your parents, they may be what are called "de novo mutations" which are mutations to the sperm or egg cells in the parent. This is evidenced by autism being more prevalent when the parents are older and those mutations are more common.
Also it clearly must be to some degree mutations if you believe the rates are going up, which even the government is no longer disputing (though that may not be conclusive proof, to me it seems evidence leans to more likely than not currently).
De novo mutations can also be caused by environmental factors, this is very well established and called "genotoxicity"[1] and certainly some people have been exposed to known genotoxic chemicals to varying degrees over time (formaldehyde) and who knows what undiscovered ones there are, newer standards of pesticide testing are starting to look for reproductive health effects but many chemicals that have been in use for a long time haven't been held to the highest standard.
Another reason you might think it would be "de novo" mutations is that this could be any sorts of random errors in the genome. If autism is an umbrella label for various genome errors (which it very well may be, since it correlates with other conditions very often), that may explain why it's so hard to pin down a few genes and why symptom expression varies so drastically.
How does any of this contradict what is written in the article?
It already mentions eg Rubella infection during pregnancy as a causative agent, which would be an example of it not being directly inherited
I wouldn't characterize it as "disrupted" genetics. Autism makes the world go round, if there were no autism we'd lack most of the innovations we take for granted today. There are other differences that can co-occur with autism to cause issues, but autism itself feels like a normal and beneficial genetic variation.
Are you one? Usually that happens because of other issues, not purely autism. If you have a motor disorder, or are unable to speak, or are unable to process language etc, that can happen whether you're also autistic or not.
Regardless, I never said autism can't be a disability or that autistic people can't be disabled or otherwise disordered compared to "normal". Just that I wouldn't treat autism itself as the one general problem.
The parents of severely autistic non verbal children needing round the clock care don't consider it "a normal and beneficial genetic variation".
> Just that I wouldn't treat autism itself as the general problem.
You wouldn't, because you've clearly got it mild.
A lot of people say they struggle in life, and that autism would explain a lot.... What about the very real possibility your simply below average? A lot of people have to be, 49% of the entire planet to be precise, so it's hardly unlikely you couldnt possibly be below average.
I never said being non-verbal or needing care around the clock was "a normal and beneficial genetic variation". I said those things would be a problem whether you also happen to be autistic or not.
> You wouldn't, because you've clearly got it mild.
This is getting into personal attacks. Back off, you don't know me.
> A lot of people say they struggle in life, and that autism would explain a lot...
This is completely different than what you were just talking about. I didn't even dispute this.
TLDR of the article is that it's yet another person confusing "heritable" with "genetic". Genes aren't the only thing we inherit from out parents - and in fact there's strong preliminary evidence that the gut microbiome plays a major role in autism.
I blame youtube. I feel like any online service provider has a duty to verify your identity on transactions over $10k USD by default and that should be enshrined in law. Honestly maybe lower.
And yet I see you are being downvoted. The message being sent is perhaps, "No, it's the dad's fault, not YouTube's."
It's strange to me how much allowance we provide corporations and their schemes to make money, and so little for the stupidity of the users that end up forking over their wages.
I think because kids accounts literally exist and don't allow this. The did didn't set up a kid's account for his... kid. Google had safe guards and he used exactly none of them. I'd blame Google if the kid subverted the controls in some way, but doesn't sound like he did. Either the dad let the kid use the account, lied on account creation for the kid or turned the controls off because they were too annoying (this one I have seen a lot). At a certain point we have to be responsible for our own actions.
Okay but like... so what? Like does that even really pass the common-sense test that that is enough?
Like my bank would give me a phone call before I wired $20k oversees. Google should by default send you a sms confirmation on your first $10k+ purchase. This is the type of common-sense stuff that isn't an issue in-person, but somehow online we get the most barebones protections for consumers because "technically the EULA said so and so."
Basically if we make the corporations liable for it, we give them an incentive to prevent fraud, identity mistakes, etc. Whereas if they are not, we give them every incentive to avoid unintended purchases.
Yeah, if I use a credit card in person, isn't it merchant's responsibility to verify my identity to their satisfaction for each and every purchase, and they let someone else (that's not an authorized user) use my card, I'm not responsible for any spending?
Well except AlphaZero played 44 million chess games in that time (and actually played with a 44 core computer). So I'd like to point out that the human is still just a few orders of magnitude more efficient.
Yes, we all know that biological systems are more efficient than machines through billions of years of evolution and natural selection but the overall process is largely the same (interacting with an environment, learning from results, improving underlying architecture, etc); efficiencies will come with more time and improvements.
Well if we make AI that learns at the rates humans do, it'll fundamentally undermine and destroy the relevance of all existing AI. It sounds to me like you're saying "we basically are there it's just a matter of degree" and I'm saying "No it's orders of magnitude off and probably won't be using LLMs at all and maybe a very fundamentally different type of neural net technology that hasn't been invented yet."
I looked at the math and I think it's true. Remember revenue is just sales, not profit. These labs are shooting for > $1T valuations, which traditionally means your PROFIT is at least 1/20th or 1/30th of that (so let's say minimum 30B$/year PROFIT).
These companies however are LOSING money (anthropic tries to make it sound like it's profit by deviating from accepted accounting principles) and subsidizing these models. When accounting for all the engineering salaries, training, GPUs, etc, what's their best-case realistic margin three years out, 10%?
So to we'd need a scenario where companies are spending a collective 300B annually on AI (believable) but ALSO that these companies jack up their margins WITHOUT companies switching to the cheaper open-source models (even when there's a $300B incentive to do so).
Yeah, just take the EBITDA and suddenly the valuations make sense. Paying money for a vending machine that currently loses money hand over fist is generally not a sound investment strategy
> The "value" of most knowledge workers -- based on what enterprises currently pay for them -- is $50 - 70 trillion annually.
What do you mean? The sum of ALL US salaries is $13.4 Trillion per year. According to google $65T is the sum of ALL salaries Globally (not just knowledge workers). It's not reasonable to assume AI is a drop-in-replacement for any job yet (perhaps bottom tier customer support from oversees?).
> So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that
So you're sort of premising here than more than 16% or 1/6 of all the world's jobs get replaced by AI. Hopefully you can understand that's both not the current AI capability and also would be a terrible (unprecedented?) economic shock.
You are comparing company valuations to annualized revenue (as approximated by some fraction of total knowledge worker compensation). Valuations are (roughly) based on the sum of all discounted future cash flows, not just the current year’s revenue.
That's what the 30x instead of 20x multiple is, for a "growth" tech company.
There's no strong evidence that openAI or anthropic will have non-linear revenue growth, so I'm not sure what point you're trying to make. Unless you think they'll fire all their engineers and replace them with agents or something crazy?
It does not have to be 16% of all jobs, but 16% of any given job, i.e. AI stays in an augmentative role rather than a complete job automation. The simplistic analysis is if a tool makes you X% faster, that can be worth X% of your salary to your employer.
Unfortunately, I do fear that AI adoption will go beyond augmentation to automation, and I do fear an economic shock. Just posted this down-thread: https://news.ycombinator.com/item?id=49722616
You said 10s of trillions of dollars of revenue, which when the sum of all salaries is 65T, is minimum 15.3% of all salaries best case. It really feels like you're just pulling numbers out of nowhere here.
Yes, say the sum of all salaries paid be employers is $65T, and if AI accelerates workers by 15.4% -- studies and survey data actually suggest it's closer to 33% already e.g. https://www.stlouisfed.org/on-the-economy/2025/nov/state-gen... -- that is worth 15.4% of 65T which is 10T, which is already "double-digit trillions" as I said.
Assuming a 33% boost takes it to ~20T annually, which is technically "10s of trillions of dollars" in revenue. And these numbers are from before agentic tasks arrived on the scene, so the actual productivity boost and corresponding value to employers is likely even higher.
Did you read your link? It says that 33% of people adopt AI, not that AI makes all workers 33% more effective, that would be absolutely insane. For that to be true we'd expect to see either companies who use it have revenues all suddenly jumping 33% (which we have not seen) or laying off 33% of their staff (which we have not seen and would also be catastrophic in the short term at least).
I'm not really going to belabor this point more, because it really sounds to me like you haven't even done the most cursory exploration into this and the people who have give an estimate of 7-10%[1] (and only a small fraction of that value would be captured by the AI provider). All the best.
Toggle "Work hours using genAI" and "Time savings due to genAI" so see what I mean. You can also toggle between various industries, which is eye-opening because even industries like "Agriculture, Forestry, Fishing, and Hunting" are seeing productivity gains!
> Using our data on generative AI use, this estimate implies that, on average, workers are 33% more productive in each hour that they use generative AI. This estimate is in line with the average estimated productivity gain from several randomized experiments on generative AI usage.
What we HAVE seen is that the national labor productivity has gone up by 1.3% since ChatGPT was released, and it lines up very well with all the other data and studies they cover... AND your reference, which predicted a 1.5% growth back in 2023!
So you can see an actual game on that website, and the play seems pretty decent to me for a while (~1700 lichess = 1300 elo) until move 28 when black throws away their queen for absolutely no reason in an incomprehensible blunder.
In some ways this is reflective of the AI experience at large, sometimes shockingly competent but then also sometimes ludicrously incompetent.
I can't think of a single dimension grok is winning on (capability, cost, voice), but want to stay open-minded -- anybody want to vouch for its capabilities in any domain?
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