Complete off the top of my head thing, no source/research, just anecdotes and pontificating.

As i've been doing some heavy editing today of a 300+ line SQL query, one thing I'm wondering about with these AI usage stats are how much of the "adoption" falls into things like intellisense suggestions.

There's two parts to this, with the first being just bad suggestions.

I've found them to be "okay" for something like F#, but for SQL, which has always required me to knife fight for an alias to begin with, they're just utter garbage.

I normally don't blow through my free github copilot suggestions in my month (I'm not in the code mines as much anyways), but I blew through it in a day of shitty SQL suggestions (and then just turned it off). This was last month, so not even while dealing with the current monster, and I'm left wondering how many people just have it vomit out useless stuff they change anyways.

The second part, is just the usual "you don't really know your tools".

While doing this query I considered turning it back on or using it. I have several CTE's for readability as this is a prototype but it necessitates an annoying pattern of taking the names of your columns, uplifting them to the next query select, and then summing and renaming them AGAIN such as SUM(COALESCE(a.example,0)) AS [example]

When you have 84 columns to do this with, it can be tempting to let AI notice the pattern and just do it. However it's not actually necessary, and now that they're clocking the tokens as we knew they would, I'm back to just using my multi line editor skills. Middle click select, some home/end to get a starting point, then ctrl+shift+arrows mixed with Alt+arrows and some copy paste and I'm mostly done. Few Ctrl + D's or straight up find and replaces and I'd edited 80 lines in maybe 30 seconds?

AI would've been a bit easier, and from what i've seen of MANY coders, something I suspect they only think is doable through AI. However much like how AI is getting people to use features they never even knew existed in their business tools, I wonder how much adoption on the coder side is the same. All these text editor helper tools many coders don't use (please keep all VIM manifestos under 400 pages) suddenly being automated out by the VASTLY more expensive AI.

And like some of the other AI solutions, as the money starts to hit the budgets, I think we'll see a lot more "Look you need to learn how to do this normally" (or "hey guess what we're removing from VS code!").

Not sure I have an overall point to this, but I'd be curious to hear what other people are seeing in their environments.

I currently work in an organization that is very AI forward. It is common for individuals to forward AI generated documents, meeting notes, or etc, with no critical thinking or review, in lieu of actual work.

This practice is insanely counter-productive, as it means that any good-faith attempt to interact with the individual pushing such documentation, really just pushes the burden of putting together said documentation onto the receiver, except now they also need to edit and verify the document they were forwarded.

I need a shorthand way to refer to this practice, that calls it out as a bad practice.

A few months ago I found an article that explained that it was bad manners to reply into a conversation anything akin to the phrase "I asked ChatGPT and it said X", for exactly the reason mentioned above. Can anyone find a link? I can't seem to find it.

This article (https://tombedor.dev/human-attention-and-human-effort/) seems to hit the nail on the head, though it does so so succintly and at such a surface level, I don't think it really gets the point across. The reason we use books as reference points for knowledge, is because they are difficult to make, and therefore we trust that the author put real work into ensuring their work was credible. If we knew they did not, their work would not be credit worthy. Neither is an unreviewed AI generated message. By this rule, the more obviously something is AI generated, the less likely it is worth reading.

I would love a law (like Conway's law is a law), that said something like: "It is never worthwhile to spend more time reading a document, than it took to write." that I could point people at when they send me AI slop, with explanations of the above.

Is anyone aware of such a thing or website?

It's been annoying me lately how often I see people declare that certain blog posts or articles are AI-generated due to the presence of certain "tell-tale signs," (emdashes being the #1 example). It annoys me even if I agree that the thing in question is probably is AI-generated—mainly because I really like emdashes and I use them in my own writing all the time, and it grinds my gears knowing that some percentage of the population will assume I used AI because "hurr durr emdash == AI."

I'm not necessarily talking about Tildes. I'm sure I've seen it on here, but the worst offender by far is Hacker News where often one of the top comments on a linked article that has an emdash somewhere in the body will be a snarky comment about how it's clearly AI generated.

Anyway, I blew off some steam this weekend by making this silly satirical website/art project called GENIUS AI Detector that makes me giggle, so I'm sharing it in the hopes that it brings a bit of joy to any fellow emdash users who are likewise depressed about the shadow that has been cast over one of my favorite punctuation marks.

I haven't been closely following the releases of new models and the research papers that sometimes go along with them. So I was wondering: is power consumption ever seriously talked about by OpenAI, Google, Anthropic, and others? Do we get some specific numbers of how much power their models actually consume to produce 100 tokens? The cost of training some of these models? Or we're not yet at that stage yet and nobody cares and at best we can just get really rough estimates based on "trust me bro" tweets by their respective CEO's?

Some time ago, I came across this post. GPT-OSS (2OB, 120B) are meager, yet energy efficient models and I was surprised that the power consumption is still larger than what I had estimated before.

For 120B to generate a 1000 tokens (which is really not a lot) it would take up around 83 Wh of electricity. For context my PC consumes ~100Wh when idling. So it's almost equivalent as leaving my PC on for an hour. Considering that proprietary models are TRILLIONS of parameters and probably not as energy efficient the true power consumption of these models is concerning. Of course, these big data centers do a lot of things to maximize the efficiency that this "test" fails to do. But even if you half the energy consumption it's still significant given their size and their pervasiveness in handling everyday, trivial tasks.

I haven't come across similar posts or studies for newer open source models, so if somebody has, please share.

Also, I don't seem to fully understand how does context size fit into all of this. The larger the context size the more power it would take to produce those 100 tokens?

I was musing about this recently with the recent models becoming more capable. The core of gen AI is the model, which is trained on a massive dataset. To date, gen AI has improved because the models have become larger, more efficient, the data they are trained on has become better and the software/harnesses around them has improved to help query them.

As I see it, surely the bottleneck will soon become the data they are trained on? If we imagine a scenario where a models could consume an infinite amount of training data, and there is no limit to the training time or quality. The sum of human skill/knowledge is the limiting factor. Gen AI should (in theory) never be able to out preform or push the boundary of the sum of humanity at time of training.

Or, counterpoint, is there enough randomness and speed to iterate that gen AI can actually step change and improve if training times/cost were less prohibitive? Most companies/models today will save good output and feed it back into the next iteration, but right now that's taking months. What if that took minutes?

What do you think?

Is gen AI going to take us to general intelligence?
Will gen AI get to a place where it's "intelligence" and reasoning is actually better than the sum of Humanity?

I've been seeing this claim repeated across social media, blogs, and various online communities these days. However, I haven't yet found a discussion that digs into the evidence behind it or provides reliable sources.

Where can I learn more about this topic?

I'm increasingly skeptical of mainstream media coverage and a lot of what I encounter online, so I'm looking for sources that are as rigorous and unbiased as possible. I'd especially appreciate:

• Academic papers and research studies
• Industry reports with transparent methodologies
• Independent analyses that critically examine the claim
• Any insights from people who work in web infrastructure, cybersecurity, search, analytics, or related fields

If you know of high-quality resources, I'd love to read about them.

I have a friend that is lucky enough to have retired at 40. A year ago he was adamant he'd never work again, having been burnt out from his time at big tech. Back then he was also an absolute AI hater and wouldn't listen to anyone who claimed LLMs were useful for programming.

He finally tried LLMs when Claude Opus 4.6 released and immediately changed his mind in the face of the overwhelming evidence that LLMs can in fact program pretty well. And now with the release of Fable 5 he's giddily creating all sorts of things that would have taken far too long to make prior to AI-accelerated software development. He actually plans to try and found his own business now. He's a very smart guy, so I hope he can make something interesting that people want.

There are a lot of AI doomers and haters. In person I mostly see people doing the same thing they've always done, but now saving time on various tasks. But this is the first time I've seen someone go from grumpy and checked out to giddy and optimistic thanks to LLMs.

My daughter (11) is doing a week long Python class, which is not using LLMs.

It got me thinking about how I learned to program in the pre-internet days (laboriously, from books), and then what a marvel it was when you could just search for information, especially for troubleshooting. But for her, the first answer in the Google search is going to be the AI summary, and most of her search tools are going to be AI tools.

I wonder if it would be possible to make an LLM that has a didactic/socratic mode. So if you said, "help me write a program to do madlibs" maybe it would give you a skeleton of a function, then prompt you to come to with a plan, then critique that plan. Or if you said, "I'm getting this error", it wouldn't just fix it, it would explain what the error means and nudge you towards the answer.

Thinking in a larger sense, it could have a rubric of important concepts, even tiers of understanding. It could be using the interactions to track the user's understanding, which could let it then tune how it answers future questions, or even be used to customize assignments.

I recognize that this is potentially replacing a teacher with a machine, which wouldn't be my goal. Good teachers are more holistic in their teaching than a machine is ever likely to be. But for people who don't have access to good teachers, or need more directed support than is available from a teacher, or just want to self study, it seems like it could be a valuable addition.

Until they solve the obsequiousness problem, it would be vulnerable to prompt hacking, so really more of a tool for someone who recognizes the value of learning over just being given the answer.

What do folks think about using such a tool? What would you want it to do, or not do?

Aside: I forgot until I reached the end of this post, but this is also (somewhat) the plot of The Diamond Age, or A Young Lady's Illustrates Primer by Neal Stephenson.

Much to my chagrin, the company I work for has done a lot in terms of steering/ pushing all software development be done through AI for some time now.

And what gives me much grin, GitHub changed their pricing structure for copilot. I'll skip the details the key fact is what used to be about $30/month per person + maybe few bucks in overages is now resulting in us hitting our usage cap on the 2nd day of the month. Overage costs this month will be hundreds of dollars per developer. I know this is an unexpected expense as I mentioned it casually to our CTO who had no idea.

I'm curious if this is going to force them to rethink the AI strategy. The incessant pushing to use more and more AI maybe will finally bite them on the ass so much they have to ask us to stop or pull back? Or maybe they'll just plunder our salaries, who knows.

I'm curious if anyone else is in the same situation.