I work for a huge bank and we tested voice recognition technology: Even under the best circumstances (high quality microphone with no ambient noise in a sound booth), it was far, far too easy to copy someone else’s voice by simply playing back a sliced up recording a la Sneakers (the movie). We ruled it out as an option over a decade ago.

The problem was fundamental and had nothing to do with the quality of the technology. If your bank is using your voice as a unique identifier they had better be using something else in addition to it! Because it’s super insecure.

From the perspective of human perception, people’s voices are only unique enough to about one in a few thousand. There’s a few outliers with much more unique voices but believe it or not, there’s a lot of people walking around on this earth that sound just like Morgan Freeman, James Earl Jones, and other voices people think are super unique.

I view an anti-cloning law as too risky: It sounds exactly like the type of thing that would prevent Grandma from cloning her own voice before going down for surgery because it just so happens to sound a lot like a famous person.

How do you implement voice cloning prevention? Human voices aren’t that unique. Also, AI voice cloning isn’t perfect. So… At what threshold is a voice considered, “cloned” from a legal perspective?

I mean, people couldn’t tell the difference between Scarlet Johansson and OpenAI’s “Sky” voice which was not cloned.

Before any of that can happen we need some non-ambiguous definitions of what “AI” is.

Mrs Brisbee is pleased.

I doubt that. New services that host the open models are cropping up all the time. They’re like VPS hosting providers (in fact, existing VPS hosts will soon break out into that space too).

It’s not like Big AI has some huge advantage over the open source models. In fact, for images they’re a little bit behind!

The FOSS coding models are getting pretty fantastic and they get better all the time. It seems like once a month a new, free model comes out that eclipses the previous generation once a month.

The mistakes it makes depends on the model and the language. GPT5 models can make horrific mistakes though where it randomly removes huge swaths of code for no reason. Every time it happens I’m like, “what the actual fuck?” Undoing the last change and trying usually fixes it though 🤷

They all make horrific security mistakes quite often. Though, that’s probably because they’re trained on human code that is *also" chock full of security mistakes (former security consultant, so I’m super biased on that front haha).

Schrodinger’s AI: It is both useless shit that can only generate “slop” while at the same time being so effective, it is the reason behind 50,000 layoffs/going to take everyone’s jobs.

You want to see someone using say, VS Code to write something using say, Claude Code?

There’s probably a thousand videos of that.

More interesting: I watched someone who was super cheap trying to use multiple AIs to code a project because he kept running out of free credits. Every now and again he’d switch accounts and use up those free credits.

That was an amazing dance, let me tell ya! Glorious!

I asked him which one he’d pay for if he had unlimited money and he said Claude Code. He has the $20/month plan but only uses it in special situations because he’ll run out of credits too fast. $20 really doesn’t get you much with Anthropic 🤷

That inspired me to try out all the code assist AIs and their respective plugins/CLI tools. He’s right: Claude Code was the best by a HUGE margin.

Gemini 3.0 is supposed to be nearly as good but I haven’t tried it yet so I dunno.

Now that I’ve said all that: I am severely disappointed in this article because it doesn’t say which AI models were used. In fact, the study authors don’t even know what AI models were used. So it’s 430 pull requests of random origin, made at some point in 2025.

For all we know, half of those could’ve been made with the Copilot gpt5-mini that everyone gets for free when they install the Copilot extension in VS Code.

Good games are orthogonal to AI usage. It’s possible to have a great game that was written with AI using AI-generated assets. Just as much as it’s possible to have a shitty one.

If AI makes creating games easier, we’re likely to see 1000 shitty games for every good one. But at the same time we’re also likely to see successful games made by people who had great ideas but never had the capital or skills to bring them to life before.

I can’t predict the future of AI but it’s easy to imagine a state where everyone has the power to make a game for basically no cost. Good or bad, that’s where we’re heading.

If making great games doesn’t require a shitton of capital, the ones who are most likely to suffer are the rich AAA game studios. Basically, the capitalists. Because when capital isn’t necessary to get something done anymore, capital becomes less useful.

Effort builds skill but it does not build quality. You could put in a ton of effort and still fail or just make something terrible. What breeds success is iteration (and luck). Because AI makes iteration faster and easier, it’s likely we’re going to see a lot of great things created using it.

FYI: Stuff like this is for automated testing, not “playing games for you” 🤣

Also, I won’t consider it realistic until it can type out, “lol git gud scrub” after ganking someone who just spawned.

Yet for some reason it still left me kinda hyped 🤷

I meant that the current reactor designs were made using AI running in big data centers with supercomputers/GPUs.

8% of total global aviation emissions doesn’t put it in second place. It’s not even in the top 100. I don’t think it ever will be… Because building huge data centers takes years and by the time there’s enough data centers to make a huge dent, the previous AI data centers will have been used to make fusion power a reality.

Today’s fusion reactor designs were all made thanks to AI. The kind running in big data centers.

It takes a lot of computing power to simulate fusion reactor designs!

I don’t know about the carbon emissions, the water thing in the article is extremely misleading. It claims that AI is using up more water than the entire yearly consumption of bottled water. The water usage estimates include the water used to cool the power plants generating the power (running the data center).

The last study on this said that the actual usage of water in the data centers is 12% of the total water usage estimate. Data centers don’t normally use that much water. It would be like Niagra Falls pouring water over every data center.

Simple reality check: If you look at the cooling system outside any given data center—if they’re using as much water as d article suggests—they’d be emitting a massive cloud of water, 24/7. It would be so much, they’d need a cooling tower on par with a nuclear power station.

So what’s with the statistic? If you look at any given power plant on Google Maps you’ll see cooling ponds all around it. That’s the water they’re talking about. It’s part of the build of the power plant. It’s not using potable water that would be going into people’s houses.

Having said that, 12% of the water usage is potable water—in the worst-case data center/power plant matchup scenario. Where you have an older data center that doesn’t use modern closed loop cooling systems that don’t lose as much water to evaporation. I don’t know what the statistic is, but I can sure you it’s a lot better than 12%. A wild guess would be 4-6%.

Background: I was a security consultant for many years and traveled all over the US going into many data centers (sometimes, by breaking in! Hah). Inside, they’re loud AF (think: standing next to a jet engine) and outside they’ll have some big ass cooling units that are also kinda loud but not as loud as some of these articles make them out to be.

That was about 7 years ago but I doubt much has changed since then. I guarantee that those data centers are still being used and have been renovated to support AI-style hardware. The power from the utility was just increased and more cooling units were added. I seriously doubt they did much more than that.

From what I’ve read about the new “giga scale” data centers, they’re much more efficient (and quieter… Outside). Those are the ones we want. If we replaced all the old stuff with new stuff, the statistics in articles like this would drop by and order of magnitude (just a guess).

Historians of the future will mark this event as the beginning of the end of civilization.

Regulate data centers already! Make it mandatory that they be self-powered by renewable energy. Problem solved.

We learned this lesson in the 90s: If you put something on the (public) Internet, assume it will be scraped (and copied and used in various ways without your consent). If you don’t want that, don’t put it on the Internet.

There’s all sorts of clever things you can do to prevent scraping but none of them are 100% effective and all have negative tradeoffs.

For reference, the big AI players aren’t scraping the Internet to train their LLMs anymore. That creates too many problems, not the least of which is making yourself vulnerable to poisoning. If an AI is scraping your content at this point it’s either amateurs or they’re just indexing it like Google would (or both) so the AI knows where to find it without having to rely on 3rd parties like Google.

Remember: Scraping the Internet is everyone’s right. Trying to stop it is futile and only benefits the biggest of the big search engines/companies.

Well they didn’t cause an accident which is better than many drivers 🤷

It’s not a shame. Have you tried this? Try it now! It only takes a minute.

Test a bunch of images against ChatGPT, Gemini, and Claude. Ask it if the image was AI-generated. I think you’ll be surprised.

Gemini is the current king of that sort of image analysis but the others should do well too.

What do you think the experts use? LOL! They’re going to run an image through the same exact process that the chatbots would use plus some additional steps if they didn’t find anything obvious on the first pass.