Human coders are still better than LLMs

This. I have ADHD and starting is the hardest part for me. With an LLM it gets me from 0 to 20% (or more) and I can nail it for the rest. It’s way less stressful for me to start now.

> LLMs can reliably solve the problem of a blank-page.

This has been the biggest boost for me. The number of choices available when facing a blank page is staggering. Even a bad/wrong implementation helps collapse those possibilities into a countable few that take far less time to think about.

Yeah, keeping me in the flow when I hit one of those silly tasks my brain just randomly says "no let's do something else" to has been the main productivity improving feature of LLMs.

So much this, the blank page problem is almost gone. Even if it's riddled with errors.

An LLM ability to do a task is roughly correlated to the number of times that task has been done on the internet before. If you want to see the hype version, you need to write a todo web app in typescript or similar. So it's probably not something you can fix with prompts, but having a model with more focus on relevant training data might help.

This honestly seems like something that could be better handled with pre-LLM technology, like a 15-line Perl script that reads one on stdin, applies some crufty regexes, and writes the other to stdout. Are there complexities I'm not seeing?

Yeah, I write a lot of little data analysis scripts and stuff, and I am happy just to read the numbers, but now I get nice PNGs of the distributions and so on from LLM, and people like that.

> LLMs are a decent search engine a la Google circa 2005.

Statistical text (token) generation made from an unknown (to the user) training data set is not the same as a keyword/faceted search of arbitrary content acquired from web crawlers.

> The problem is that the conversational interface, for some reason, seems to turn off the natural skepticism that people have when they use a search engine.

For me, my skepticism of using a statistical text generation algorithm as if it were a search engine is because a statistical text generation algorithm is not a search engine.

Except a search engine isn't voice controlled, and able to write code for me.

Recently I did some tests with coding agents, and being able to translate a full application from AT&T Assembly into Intel Assembly compatible with NASM, in about half an hour of talking with agent, and having the end result actually working with minor tweeks isn't something a "decent search engine a la Google circa 2005." would ever been able to achieve.

In the past I would have given such a task to a junior dev or intern, to keep them busy somehow, with a bit more tool maturity I have no reason to do it in the future.

And this is the point many developers haven't yet grasped about their future in the job market.

  > the conversational interface, for some reason, seems to turn off the natural skepticism that people have

So what you’re saying is you need to be very specific and detailed when writing your specifications for the LLM to spit out the code you want. Sounds like I can just skip the middle man and code it myself.

This seems to be what’s happened

People are expecting perfection from bad spec

Isn’t that what engineers are (rightfully) always complaining about to BD?

This hasn't been my experience (using the latest claude and gemini models). They'll produce poor code even when given a well defined easily achievable task with specific instructions. The code will usually more or less work with today's models, but it will do things like call a function to recreate a value that is already stored in a local variable... (and worse issues prop us the more design-work you leave to the LLM, even dead simple design work with really only one good answer)

I've definitely also found that the poor code can sometimes be a nice starting place. One thing I think it does for me is make me fix it up until it's actually good, instead of write the first thing that comes to mind and declare it good enough (after all my poorly written first draft is of course perfect). In contrast to the usual view of AI assisted coding, I think this style of programming for tedious tasks makes me "less productive" (I take longer) but produces better code.

> LLMs follow instructions.

Not really, not always. To anyone who’s used the latest LLMs extensively, it’s clear that this is not something you can reliably assume even with the constraints you mentioned.

> LLMs follow instructions.

They don't

> Garbage in = garbage out generally.

Generally, this statement is false

> When attention is managed and a problem is well defined and necessary materials are available to it, they can perform rather well.

Keyword: can.

They can also not perform really well despite all the management and materials.

They can also work really well with loosey-goosey approach.

The reason is that they are non-deterministic systems whose performance is affected more by compute availability than by your unscientific random attempts at reverse engineering their behavior https://dmitriid.com/prompting-llms-is-not-engineering

They should maybe have a verifiable specification for said instructions. Kinda like a programming language maybe!

> LLMs follow instructions.

No they don't, they generate a statistically plausible text response given a sequence of tokens.

I'm not sure if you're being sarcastic, but in case you're not... From https://arstechnica.com/health/2023/11/ai-with-90-error-rate...

"UnitedHealth uses AI model with 90% error rate to deny care, lawsuit alleges" Also "The use of faulty AI is not new for the health care industry."

> If this happened every once in a while then it wouldn't be a big deal, but I'd guess that more than half of the answers and tutorials I've received through ChatGPT have ended up being plain wrong.

It would actually have been more pernicious that way, since it would lull people into a false sense of security.

There are so many examples where all current top models just will loop forever even if you instruct them literally the code. We know many of them, but for instance in a tailwind react project with some degree of complexity (nested components), if you ask for something to scroll in it's space, it will never figure out min-h-0 even if you tell it. It will just loop forever rewriting the code adding and removing things, to the point of it just putting comments like 'This will add overflow' and writing js to force scroll, and it will never work even if you literally tell it what to do. Don't know why, all big and small models have this, and I found Gemini is currently the only model that sometimes randomly has the right idea but then still cannot resolve it. For this we went back to not using tailwind and back to global vanilla css, which I never thought I would say, is rather nice.

I asked Chat GPT 4o to write an Emacs function to highlight a line. This involves setting the "mark" at the beginning, and the "point" at the end. It would only set the point, so I corrected it "no, you have to set both", but even after correction it would move the point to the beginning, and then moved the point again to the end, without ever touching the mark.

I’m self taught and don’t code that much but I feel like I benefit a ton from LLMs giving me specific answers to questions that would take me a lot of time to figure out with documentation and stack overflow. Or even generating snippets that I can evaluate whether or not will work.

But I actually can’t imagine how you can teach someone to code if they have access to an LLM from day one. It’s too easy to take the easy route and you lose the critical thinking and problem solving skills required to code in the first place and to actually make an LLM useful in the second. Best of luck to you… it’s a weird time for a lot of things.

*edit them/they

This was what promptly led me to turning off Jetbrains AI assistant: the multiline completion was incredibly distracting to my chain of thought, particularly when it would suggest things that looked right but weren't. Stopping and parsing the suggestion to realize if it was right or wrong would completely kill my flow.

What one would expect if they can't read the code because they haven't learned to code.

TBF, trial and error has usually been my path as well, it's just that I was generating the errors so I would know where to find them.

Tbf, there's a phase of learning to code where everything is pretty much an incantation you learn because someone told you "just trust me." You encounter "here's how to make the computer print text in Python" before you would ever discuss strings or defining and invoking functions, for instance. To get your start you kind of have to just accept some stuff uncritically.

It's hard to remember what it was like to be in that phase. Once simple things like using variables are second nature, it's difficult to put yourself back into the shoes of someone who doesn't understand the use of a variable yet.

Most of the times, people are led astray by following average advice on exceptional circumstances.

People with a minimum amount of expertise stop asking for advice for average circumstances very quickly.

It's mostly a question of experience. I've been writing software long enough that when I give chat models some code and a problem, I can immediately tell if they understood it or if they got hooked on something unrelated. But junior devs will have a hell of a hard time, because the raw code quality that LLMs generate is usually top notch, even if the functionality is completely off.

> When talking with reasonable people

When talking with reasonable people, they will tell you if they don't understand what you're saying.

When talking with reasonable people, they will tell you if they don't know the answer or if they are unsure about their answer.

LLMs do none of that.

They will very happily, and very confidently, spout complete bullshit at you.

It is essentially a lotto draw as to whether the answer is hallucinated, completely wrong, subtly wrong, not ideal, sort of right or correct.

An LLM is a bit like those spin the wheel game shows on TV really.

Why not just write the code at that point instead of cajoling an AI to do it.

I use a few strategies, but it's mostly the same as if I was mentoring a junior. A lot of my job already involved breaking up big features into small tickets. If the tasks are small enough, juniors and LLMs have an easier time implementing things and I have an easier time reviewing. If there's something I'm really unfamiliar with, it should be in a dedicated function backed by enough tests that my understanding of the implementation isn't required. In fact, LLMs do great with TDD!

> At least with humans I have some basic level of trust, so that even if I don't understand the code at that level, I can scan it and see that it's reasonable.

If you can't scan the code and see that it's reasonable, that's a smell. The task was too big or its implemented the wrong way. You'd feel bad telling a real person to go back and rewrite it a different way but the LLM has no ego to bruise.

I may have a different perspective because I already do a lot of review, but I think using LLMs means you have to do more of it. What's the excuse for merging code that is "off" in any way? The LLM did it? It takes a short time to review your code, give your feedback to the LLM and put up something actually production ready.

> But every piece of LLM generated code I've seen to date hasn't been trustworthy once I put in the effort to really understand it.

That's why your code needs tests. More tests. If you can't test it, it's wrong and needs to be rewritten.

Keep using it and you'll see. Also that depends on the model and prompting.

My approach is to describe the task in great detail, which also helps me completing my own understanding of the problem, in case I hadn't considered an edge case or how to handle something specific. The more you do that the closer the result you get is to your own personal taste, experience and design.

Of course you're trading writing code vs writing a prompt but it's common to make architectural docs before making a sizeable feature, now you can feed that to the LLM instead of just having it be there.

Trust, but verify ;]

> I find it particularly useful if I have to work with a language of framework that I'm not fully experienced in

Yep - my number 1 use case for LLMs is as a template and example generator. It actually seems like a fairly reasonable use for probabilistic text generation!

I find it very much depends on the LLM you're using. Gemini feels more likely to push back than claude 3.7 is. Haven't tried claude 4 yet

This is key. Humans each have a personality and some sense of mood. When you ask for help, you choose ask and that person can sense your situation. LLM has every personality and doesn't know your situation. You have to tell it which personality to use and what your situation is.

> Perhaps you remember that language models were completely useless at coding some years ago, and now they can do quite a lot of things, even if they are not perfect.

IMO, they're still useless today, with the only progress being that they can produce a more convincing facade of usefulness. I wouldn't call that very meaningful progress.

Why state the same arguments everybody has been repeating for ages?

LLMs can only give you code that somebody has wrote before. This is inherent. This is useful for a bunch of stuff, but that bunch won't change if OpenAI decides to spend the GDP of Germany training one instead of Costa Rica.

Progress sure, but the rate the’ve improved hasn’t been particularly fast recently.

Programming has become vastly more efficient in terms of programmer effort over decades, but making some aspects of the job more efficient just means all your effort it spent on what didn’t improve.

> Perhaps you remember that language models were completely useless at coding some years ago

no i don't remember that. They are doing similar things now that they did 3 yrs ago. They were still a decent rubber duck 3 yrs ago.

> llms nor those juniors for such an important work.

Yeah we shouldn’t and I limit my usage to stuff that is easily verifiable.

But there’s no guardrails on this stuff, and one thing that’s not well considered is how these things which make us more powerful and productive can be destructive in the hands of well intentioned people.

> Is it just me or are we heading into a period of explosion of software done, but also a massive drop of its quality? Not uniformly, just a bit of chaotic spread

I think we are, especially with executives mandating the use LLMs use and expecting it to massively reduce costs and increase output.

For the most part they don't actually seem to care that much about software quality, and tend to push to decrease quality at every opportunity.

Which is frightening, because it's not like our industry is known for producing really high quality code at the starting point before LLM authored code.

> those juniors

I'm betting they're the most senior people on the team.

I don't think it is only (or even mostly) not wanting to accept it, I think it is at least equal measure just plain skepticism. We've seen all sorts of wild statements about how much something is going to revolutionize X and then turns out to be nothing. Most people disbelieve these sorts of claims until they see real evidence for themselves... and that is a good default position.

> If technology folk cannot see the INCREDIBLE LEAP FORWARD made by LLMs since ChatGPT came on the market, they're not seeing the forest through the trees because their heads are buried in the sand.

Look, we see the forest. We are just not impressed by it.

Having unlimited chaos monkeys at will is not revolutionizing anything.

Lawyers don't even use version control software a lot of the time. They burn hundreds of paralegal hours reconciling revisions, a task that could be made 100x faster and easier with Git.

There's no guarantee a technology will take off, even if it's really, really good. Because we don't decide if that tech takes off - the lawyers do. And they might not care, or they might decide billing more hours is better, actually.

Many of us would prefer to see the technological leaps to be evenly distributed (so e.g. even clean drinking water that does not need to be boiled before consumption is not a baseline in 2025). So if you want to adapt to your new and improved position where you are just pushing buttons fine - but some of us are actually interested in how computers work (and are actually really uninterested in most companies' bottom lines). It's just how it is ;)

I think many people just settled in while we had no real technological change for 15 years. Real change, not an update to a web framework.

When I graduated high school, I had never been or knew anyone who had ever been on the internet at all. The internet was this vague "information superhighway" that I didn't know really what to make of.

If you are of a certain age though you would think a pointless update to react was all the change ever coming.

That time is over and we are back to reality.

> you are ... le LUDDITE

Or maybe they just know the nitty-gritty inherent limitations of technology better than you.

(inb4: "LLMs can't have limitations! Wait a few years and they will solve literally every possible problem!")

Friendly reminder that people like you were saying the exact same thing about metaverse, VR, web3, crypto, etc.

Ah yes, please enjoy living in your moment and anticipating your entirely new world. I also hear all cars will be driving themselves soon and Jesus is coming back any day now.

You are arguing based on the merits of the technology, which is fine, but wasn't my point. I was arguing that derision tends to happen no matter what, and thus doesn't indicate much about the merits of the technology one way or the other.

It's true that manually constraining an LLM with contextual data increases their performance on that data (and reduces performance elsewhere), but that conflicts with the promise of AI as an everything machine. We were promised an everything machine but if we have to not only provide it the proper context, but already know what constitutes the proper context, then it is not in any way an everything machine.

Which means it's back to being a very useful tool, but not the earth-shattering disruptor we hoped (or worried) it would be.

I haven't used it extensively, but toyed around with it for Elixir code and I wasn't particularly impressed.

This. MCP/tool usage in agentic mode is insanely powerful. Let the agent ingest a Gitlab issue, tell it how it can run commands, tests etc. in the local environment and half of the time it can just iterate towards a solution all by itself (but watching and intervening when it starts going the wrong way is still advisable).

Recently I converted all the (Google Docs) documentation of a project to markdown files and added those to the workspace. It now indexes it with RAG and can easily find relevant bits of documentation, especially in agent mode.

It really stresses the importance of getting your documentation and processes in order as well as making sure the tasks at hand are well-specified. It soon might be the main thing that requires human input or action.

Yes! It’s much better at using functional logic than I am - which I appreciate!

What kind of things is it doing?

Wrote me:

- a SPI deserializer that sets a bit after 12 bits read in, to trigger a prefetch

- an SDC constraints file for the deserializer that correctly identified the SPI clock and bus clock as separate domains requiring their own statement

- a test bench that validated both that the prefetch bit was being set, and that it was being set at the proper time relative to the SPI clock

- a makefile with commands for build, headless test, and debug by loading the VCD into a waveform viewer

It always feels like LLMs can do lots of the easy stuff, but if they can't do everything you need the skilled engineer anyway, who'd knock the easy things out in a week anyway.

Nearly every part of the tool flow I just described, I would consider “tricky to get right”. Been doing this for ~15 years and it’s still tough to bootstrap something like this from scratch. ChatGPT-4o did this for me from zero in about 15 minutes.

I won’t lie: I love it. I can focus on the actual, bigger problems at hand, and not the tricky little details of HDLs.

People are either deluding themselves or ignorant of the capabilities of frontier models if they don’t believe LLMs offer a speedup in workflow.

I personally believe that most of the doubt and cynicism is due to:

1) a pretty big collective identity crisis among software professionals, and

2) a suspicion that LLMs make it so that anyone who is good at articulating the problem precisely no longer needs a software engineer as a translation specialist from specs to code.

I say this as an EE of ~15 years who’s always been able to articulate what I want, specifically, to a firmware counterpart, who then writes the code I need. I can turn years of practice in this skill into great prompts for an LLM, which effectively cuts out the middleman.

I really like it. It’s helped me take on a lot of projects that are just outside of my innate level of capability. It’s also helped me learn a lot of new things about these new software adjacent areas. ChatGPT is a great tutor!

It's more like better-than-senior 99% of the time. Makes mistakes 1% of the time. Most of the 'bad results' I've seen people struggle with ended up being the fault of the human, in the form of horrible context given to the AI or else ambiguous or otherwise flawed prompts.

Any skilled developer with a decade of experience can write prompts that return back precisely what we wanted almost every single time. I do it all day long. "Claude 4" rarely messes up.

I want to be neither. I either want to continue being a software engineer who doesn't need a tricycle for the mind, or move to law or medicine; two professions that have successfully defended themselves against extreme versions of the kind of anxiety, obedience and self hate that is so prevalent among software engineers.

I think it's the wrong analogy. The prompt engineer who uses the AI to make code maps to the poorly-paid, low-skill power loom machine tender. The "engineer" is the person who created the model. But it's also not totally clear to me that we'll need humans for that either, in the near future.

I would absolutely love to write my own placement. When I can have AI do my job while I go to the beach you better believe I will be at the beach.

Compiler is the more apt analogy to a mechanical loom.

An LLM is more like outsourcing to a consultancy. Results may vary.

> Either way you’re out of a job.

Tools and systems which increase productivity famously always put everyone out of a job, which is why after a couple centuries of industrial revolution we're all unemployed.

No, nothing fundamentally new is created. Programmers have always been obsessed with "new" tooling and processes to distract from that fact.

"AI" is the latest iteration of snake oil that is foisted upon us by management. The problem is not "AI" per se, but the amount of of friction and productivity loss that comes with it.

Most of the productivity loss comes from being forced to engage with it and push back against that nonsense. One has to learn the hype language, debunk it, etc.

Why do you think IT has gotten better? Amazon had a better and faster website with far better search and products 20 years ago. No amount of "AI" will fix that.

> It is wise to work on your replacement...

Depends on the context. You have to keep in mind: it is not a goal of our society or economic system to provide you with a stable, rewarding job. In fact, the incentives are to take that away from you ASAP.

Before software engineers go celebrate this tech, they need realize they're going to end up like rust-belt factory workers the day after the plant closed. They're not special, and society won't be any kinder to them.

> ...and even wiser to be in charge of and operate the replacement.

You'll likely only get to do that if your boss doesn't know about it.

It does when it is called unionizing, however for some reason software developers have a mental block towards the concept.

You can work with consulting oriented engineers who get shit done with relatively little stress and significant productivity. Productivity enhanced by AI but not replaced by it. If interested, reach out to me.

I’ll work on fixing the vibe coders mess and make bank. Experience will prove valuable even more than before

Personally I used them for a while and then just stopped using them because actually no, unfortunately those assistants don't work. They appear to work at first glance but there's so much babysitting needed that it's just not worth it.

This "vibe coding" seems just another way to say that people spend more time refining the output of these tools over and over again that what they would normally code.

I'm not as bearish on AI, but its hard to tell if you can really extrapolate future performance based on past improvements.

Personally, I'm more interested in the political angle. I can see that AI will be disruptive because there's a ton of money and possibly other political outcomes depending on it doing exactly that.

Did you actually mean to say AI Vibrators?

Question is, how autonomous decision making works, nobody argues that llm can finish any sentence, but can it push a red button?

Maybe automated testing of all forms will just become much more ubiquitous as a safeguard against the worst of AI hallucinations? I feel that would solve a lot of people's worries about LLMs. I'm imagining a world where a software developer is a person who gathers requirements, writes some tests, asks the AI to modify the codebase, ensures the tests still work, makes sure they are a human who understands the change the AI just made, and continues with the next requirement.

> the vast majority of software out there barely needs to scale or be super efficient

That was the way I saw it for a while. In recent months I've begun to wonder if I need to reevaluate that, because it's become clear to me that scaling doesn't actually start from zero. By zero I mean that I was naive enough to think that all programs, even the most googled programmed one by a completely new junior would at least have, some, efficiency... but some of these LLM services I get to work on today are so bad they didn't start at zero but at some negative number. It would have been less of an issue if our non-developer-developers didn't use Python (or at least used Python with ruff/pyrefly/whateveryoulike, but some of the things they write can't even scale to do minimal BI reporting.

Still terrible result. Multi modal = actually understands the image

Actually, you've reinforced their point. It's only bad at things the user is actually good at because the user actually knows enough in that domain to find the flaws and issues. It appears to be good in domains the user is bad at because the user doesn't know any better. In reality, the LLM is just bad at all domains; it's simply whether a user has the skill to discern it. Of course, I don't believe it's as black and white as that but I just wanted to point it out.

Getting CSS keywords right is not the actual point of writing CSS. And you can have a linter that helps you in that regards. The endgame of writing CSS is to style an HTML page according to the specifications of a design. Which can be as detailed as a figma file or as flimsy as a drawing on a whiteboard.

I'm one of those weirdos who really likes handwriting CSS. I frequently find ChatGPT getting my requests wrong.

This is like saying that LLMs are better at knowing the name of that one obscure API. It's not wrong, but it's also not the hard part about CSS

The point is that I don't dread it anymore, because now there are tools that make it a lot easier the one or two times a year I have some reason to use it.

> Most of us (probably) couldn't tell you exactly how an optimizing compiler picks optimizations or exactly how JavaScript maps to processor instructions,

That's because other people are making those working well. It's like how you don't care about how the bread is being made because you trust your baker (or the regulations). It's a chain of trust that is easily broken when LLMs are brought in.

> Here's a kid out hoeing rows for corn. He sees someone planting with a tractor, and decides that's the way to go. Someone tells him, "If you get a tractor, you'll never develop the muscles that would make you really great at hoeing

In this example the idea that losing the muscles that make you great at hoeing" seems kind of like a silly thing to worry about

But I think there's a second order effect here. The kid gets a job driving the tractor instead. He spends his days seated instead of working. His lifestyle is more sedentary. He works just as many hours as before, and he makes about the same as he did before, so he doesn't really see much benefit from the increased productivity of the tractor.

However now he's gaining weight from being more sedentary, losing muscle from not moving his body, developing lower back problems from being seated all day, developing hearing loss from the noisy machinery. His quality of life is now lower, right?

Edit: Yes, there are also health problems from working hard moving dirt all day. You can overwork yourself, no question. It's hard on your body, being in the sun all day is bad for you.

I would argue it's still objectively a physically healthier lifestyle than driving a tractor for hours though.

Edit 2: my point is that I think after driving a tractor for a while, the kid would really struggle to go hoe by hand like he used to, if he ever needed to

The analogy I would use is that coding via LLM is like learning to drive in a self-driving car that has manual controls as an option that drives overly cautiously (Leaves excessively large following distances, takes corners slower, etc.) while in self-driving mode.

You can let it self-drive, but you'd probably learn nothing, and it will actually take you longer. Put an expert driver behind the wheel, and they'll drive faster and only use automation features for the boring parts.

>I think the difference is how much of the job the tool can take over.

I think it is about how utilitarian the output is. For food no one cares how the sausage is made. For a painting the story behind it is more important than the picture itself. All of Picasso's paintings are famous because they were painted by Picasso. Picasso style painting by Bill? Suddenly it isn't museum worthy anymore.

No one cares about the story or people behind Word, they just want to edit documents. The Demo scene probably has a good shot at being on the side of art.

For me the creativity in software engineering doesn't come from coding, that's an implementation detail. It comes from architecture, from thinking about "what do I want to build, how should it behave, how should it look, what or who is it for?" and driving that forward. Bolting it together in code is hoeing, for that vast majority of us. The creative endeavor sits higher up on the abstraction ladder.

If progress continues at the rate that AI boosters expect, then soon you won't have to use them smartly to get value (all existing workflows will churn and be replaced by newer, smarter workflows within months), and everybody who is behind will immediately catch up the moment they start to use the tool.

It is the survivor bias: "by looking at what is still alive today, majority of code is pretty bad"

It eludes all of those who died in the process : those still alives are here despite bad IT, not due to a bad IT

The vast majority of code that makes money is pretty shitty.

I think benchmark targeting is going to be a serious problem going forward. The recent Nate Silver podcast on poker performance is interesting. Basically, the LLM models still suck at playing poker.

Poker tests intelligence. So what gives? One interesting thing is that for whatever reason, poker performance isn't used a benchmark in the LLM showdown between big tech companies.

The models have definitely improved in the past few years. I'm skeptical that there's been a "break-through", and I'm growing more skeptical of the exponential growth theory. It looks to me like the big tech companies are just throwing huge compute and engineering budgets at the existing transformer tech, to improve benchmarks one by one.

I'm sure if Google allocated 10 engineers a dozen million dollars to improve Gemini's poker performance, it would increase. The idea before AGI and the exponential growth hypothesis is that you don't have to do that because the AI gets smarter in a general sense all on it's own.

> But that already happened to lots of industries and lots of people, we never cared before about them

We did. Why do you think labor laws, unions, etc. exist? Why do you think communism was appealing as an idea in the beginning to many? Whether the effects were good or bad or enough or not, that’s a different question. But these changes have demonstrably, grave consequences.

The difference is in how many industries AI is threatening. It's not just coding on the chopping block

You're correct. Tapping the globe icon encourages the model to use it more often.