@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.

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.

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”.

I work at a small software company. There is a push to use AI but I would say in a reasonable way. It does speed up some tasks but no one is vibe codding and pushing things without proper review. So far no one is tracking the usage or pushing us to use it more. It’s just a new tool we’re encouraged to be familiar with and use reasonably.

The one I work at went “all in” about a month ago. I started noticing a dramatic increase in garbage/nonsensical code at the end of last week. I didn’t make the connection between the two until Tuesday.

I’ve got a manager that usually listens and they asked me to try it and take notes because they know I’ll tell them the truth. … I’ve got a lot of examples prepped for our next meeting.

The hard part is definitively blaming LLMs because I don’t have time to track down every single commit and analyze it for LLM usage but there’s 100% a correlation.

Software company here. There’s a strong external push for us to shove AI into every corner of our UI, but so far we’ve largely kept it out.

The one place we are using it is a pretty strong use-case (essentially sentiment analysis). We’ve had a chatbot in dev for a while, but are struggling to find a valid usecase for it. I think most of us are hoping the AI craze dies down and suddenly our lack of AI is no longer a marketing point our competitors use against us.

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.

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.

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.

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.

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

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

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.

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

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.