Ask Lemmy

Government - great at research, terrible at generation. If you ask it to find and summarise laws and regulation, does a great job, quotes info, can even generate reasonable overviews with a handhold.

Ask it to generate anything that isn't directly quoted in a specific doc and it goes WILD. Even with some solid training in prompt engineering, it makes you work for focused outputs unless you give it clear everything (data, prompt, target template, revision and scoring process). But once the workflow has been solidly validated a few times I'd rate it "usable".

Not in tech, but LLMs have been great for my safety and compliance consulting business. I can honestly say LLMs have made me thousands of euros.

Before LLMs, I would spend quite a bit of my regular workday on creating safety plans and coming up with systems to improve conditions and ensure compliance.

Now, with the power of LLMs, management can generate those plans themselves. So instead of me spending my normal workday on it, I get to bill my emergency rate when the hallucinated slop gets rejected and they need something actually legal at the last minute.

Work in a big multi national company. not a software company, but I'm on an engineering team.

Leadership makes a lot of noises about AI.

The engineers can't even use git competently. I've suggested quietly maybe we should focus on learning software fundamentals instead of chasing dreams but no one here listens to me.

My company is approaching AI like it's been approaching anything for the past 40 years: with extreme caution. It's coming alright, but the engineers are carefully evaluating it for coding, and it certainly isn't being rolled out recklessly.

I'm one of several die-hards who flat-out refuse to use it - not so much because it's AI, but because it's provided by an American company - and my choice is respected. Our CEO sees old-timers like me as the fallback is AI ends up shitting the company's bed.

My wife's at a major video game company that, oddly enough, hasn't gone crazy over AI. Since she's in localization, she uses DeepL which has some machine learning, but not really an LLM and LLMs aren't really being pushed on her since it's a downgrade. From what I can tell, their dev team is also just keeping things human made, although they're in Japan so that might contribute.

They aren't saints, they did try to union bust a few years back, but their stance on AI, as well as creativity first mentality and recent pay raise guarantees and whatnot, kinda show they're paying attention.

I run a tech company that doesn’t use any AI:

https://sciactive.com/human-contribution-policy/

We make an email service, and we have a hard stance against any AI in our product:

https://sciactive.com/2026/01/21/our-stance-on-ai-in-email/

I work at a renowned tech company that frequently reminds its employees that AI hallucinates. We do a lot of work for the army, a mistake caused by hallucinating AI would be a disaster.

I just use AI to fill in the stupid forms HR make us do and don't verify its output because I don't respect it. Kills 2 birds with 1 stone.

Medical device industry here. Some of our software and electrical engineers are using Claude as a sounding board for ideas, or as a starting point to find possible paths forward when they get stuck with a hard problem. Nobody trusts the model to give an accurate answer. Nobody is being encouraged to use AI models. At the end of the day, all work committed to a project is done by real humans with the normal review processes.

Management is cautiously looking at potential uses for AI in our products, but there is a healthy dose of skepticism all around. If your machine is displaying diagnostic data to a doctor there cannot be any question as to whether the machine is hallucinating.

Every time I hear stories like this I’m glad I work at a startup where everyone’s too busy to worry about shit like AI usage dashboards

I run a small (5-employees) tech firm. We ignored AI for the first couple of years. Last year we started paying the basic Cursor subscription for our employees. We encouraged them to try it out a bit for a couple of weeks however they saw fit to evaluate if they found it useful for their workflows but we said we didn't mind at all if they ended up deciding to adopt it long term or not. We also stressed we would continue reviewing code the same way so they would have to take responsibility for reviewing the AI's output. I started as the only coder in the company and I review every PR so I am extremely familiar with all our codebase and I haven't found it very useful personally but the people that joined more recently say it can be useful to point them towards parts of the code they are not familiar with yet. Right now each one uses it as a tool freely however they prefer and I don't usually ask them about it, same way I don't ask how often they use the "find and replace" function in VS Code.

My company uses copilot for code reviews. They encourage at least trying a number of other tools but do not require it. Some of our product does use LLMs for various things, though I don't personally work on those.

I do worry about the environmental impacts and ethical concerns around training data (especially pirated data used with neither consent nor compensation) so I don't use anything personally (aside from where some company has shoved it in somewhere).

I think that local models trained ethically can have a number of uses such as classification, data cleanup, and perhaps even checking code for security issues and exploits (I'm not sure if local models can do that yet or well).

Did your CEO have a "Fireside Chat" about how great AI is?

Not a tech company, but a petroleum exploration company, which involves a lot of tech. The petroleum industry in general is extremely conservative in terms of tech, in that older and proven technologies tend to stick around. For example, I often write data to magnetic tape.

However, the industry doesn't shy away from newer technologies where it does make sense. There is some AI at play, but it is limited in scope, and only deployed where it makes sense. Most of it is done on the processing side, so I don't know much about it, but I get the impression it's used in a similar manner to those headlines you see from time about AI predicting rectal cancer 99% correctly. Interpreting seismic survey data involves some geophysical wizardry that I've never quite understood - I just make sure the production servers offshore work.

I am employed by a tiny software dev shop that develops a few apps used in education. No AI at all, unless I proactively choose to and pay for it out of my own pocket.

@pageflight Small design company. We experiment with llms in different areas but so far there are marginal improvements and very little work-safe use cases. Totally not up to the hype.

I work as a developer for a smaller (~50 people) tech company and most devs use AI on a daily basis, but everyone is free to choose what, how and how much (until our tokens run out 😬, but we do have a decent budget).

We find we have to learn how to use this new, powerful (and sometimes also very annoying) tool properly. PR have been sent for review without properly being checked by the "author", bosses have raves about AI but also reality checked when they saw the bills they have to pay and we will probably make more mistakes as well as productivity boosts in the future. We will see where this all ends up.

For now I just enjoy having this companion when I need it to guide me through new fields I would have not dared to tread before, which is fun. Worst case I'll be building houses instead of web applications in a few years. Working outside instead of in a stuffy office. Could be worse.

We have AI built into some tools I believe, but I have never been told I had to use them. The truth is they don't work all the time for every situation and the client is more worried about user data accidentally getting scooped up and spending time warning us to never enter any users information anywere, even so much as notating a user saying they have a limitation that explains why we performed a task in a non standard fashion is a complete not happening.

So if someone said, "I am vision impaired," someone reading our notes would probably be wondering... Why the f didn't they just do a,b,c it would have been much easier. But they are worried if those notes get integrated into something the AI gobbles up in the future, they don't want to get sued for that user information to somehow be linked to them. As that could be considered medical data I guess.

The funny part is, if an AI does use that data for learning now, it may start trying to instruct or perform tasks based off of highly inefficient solutions designed to assist a specific disability

I work at a startup that classifies and extracts data from often very fuzzy sources.

We are encouraged to use agents for development. We use models in our services for things like pinpointing Coca-Cola* cans in YouTube videos. We offer our customers LLMs to discover how Coca-Cola and Pepsi are presented on YouTube.

*Soda scenario imaginary. I don't want to dox my niche, but it's similar enough problems that we solve.

For my pov at my work, there's definitely that disconnect between what the executives are saying and the ones lower down the chain who are actually tasked to implement and support those new technologies.

There's a company-wide mandate to use AI, so naturally everyone is trying to inject it into their projects. But the idea of putting AI into something is different from actually implementing it, and the latter is far more complicated with all the governance and security involved. And all these teams are escalating everything because of how long stuff takes to get reviewed and approved or how complicated it is for them (the non-tech people) to actually deploy it themselves. People think they can just deploy a local MCP server on their laptop, or deploy a cloud compute on their own and run it from there. Deploying something in production infrastructure is not as simple as creating a new compute and installing whatever you want.

The company I work for builds a product that uses AI extensively. The product would not be possible without AI, like the one main thing the product does is only possible because of AI. That said, AI use for coding is quite limited. We talk about it, some people do develop with AI, but there is no push for it. I feel like building a product on it has made developers acutely aware of just how flakey and unreliable AI is.