hackernews client

Kevin Kelly's 2016 AI Predictions, 10 Years Later

220 pointsposted 4 days ago

94 Comments

stephen_cagle

4 days ago

By my own, completely arbitrary split of this into 16 predictions, I would say that this hits on maybe 8 of 16 if we are being a bit generous. Which is good, but certainly not "Inevitable" (the title of the book).

With that said, maybe he meant inevitable as N approaches infinity... So there's still time!

Aside: I think the biggest interesting prediction he had was that AI would obey a user driven network effect. It seems like a very clean prediction, and yet it has failed to materialize? I wonder if this has more to do with just how scrape-able the whole internet was or with distilation or what? There is certainly an advantage to having good user data, but it seems like it is largely something that can be worked around (by hook or crook)?

martincsweiss

4 days ago

It will become dominated by a user driven network effect. We're just in the early days, still. Like when Thomas Watson said there was a world market for about 5 computers.

LLMs are a communications technology, but the only networks most people have seen agents on basically steal your personal information. Give it a couple more years.

skew-aberration

3 days ago

Watson might also have been allowing for N -> infinity. Claude + ChatGPT + Grok + DeepSeek + GLM.

naasking

4 days ago

Is 50% good? It's what you'd expect by posing "will AI do X by 2026?" and then flipping a coin to get an answer.

furyofantares

3 days ago

If you're putting a book out there of predictions it's probably trying to inform people of something they don't see coming, something they would have guessed much less than 50% while you think you have special knowledge to impart that it is indeed more likely to happen than they think.

Nobody would buy a book predicting the price of bitcoin in 10 years will end in an even number, or that the president's name will start with a letter in the first half of the alphabet.

But if someone says here's some wild sounding stuff that I have good reason to believe is coming, that's more interesting.

kqr

3 days ago

> the president's name will start with a letter in the first half of the alphabet

Sorry, this triggered my fact-checkitis. I opened Wikipedia's list of presidents of the US and ran this in the web developer console:

    Array.from(document.querySelectorAll(
        '#mwgA tr > :nth-child(3) span[data-sort-value]'
    ))
    .map(x => x.getAttribute('data-sort-value')[0])
    .reduce(({total, firstHalf}, c) => ({
        total: total+1, 
        firstHalf: c < 'N' ? firstHalf+1 : firstHalf
    }), {total: 0, firstHalf: 0});

and it responded

    Object { total: 47, firstHalf: 31 }

Then I used Agresti and Coull's method for a 95 % confidence interval around this (add two successes and two failures, compute Wald interval as usual). That gives us

    65 % ± 13 %

In other words, the 95 % confidence interval of the proportion lies in the span from 52 % to 78 %. That is significantly different from the null hypothesis.

I didn't expect it, but we might actually have reason to believe US presidents more often than not do have names starting with letters from the first half of the alphabet.

Edit: But I just realised English letter frequencies have 54 % of the probability mass in the first half of the alphabet, so that's probably a better null hypothesis than the even split. It's also compatible with the actual presidential statistics.

vigilantpuma

3 days ago

> But I just realised English letter frequencies have 54 % of the probability mass in the first half of the alphabet

But what about English first-letter-of-the-word frequencies?

Edit: asked ChatGPT to analyze this for me. Without verifying its work, looks like ~60% of English words (taken as a list of unique words) start with a letter in the first half of the alphabet, but if you look at a corpus of writing, it’s closer to 51%.

furyofantares

3 days ago

I knew the middle of the alphabet wouldn't be the correct cut but figured people would get the gist.

Jtarii

4 days ago

"By 2026, Google’s main product will not be search but AI."

You think this was a 50/50 coinflip statement in 2016?

calebkaiser

3 days ago

I don't really know what it means. It seems to sort of entangle Google Search as a product with a lot of constituent parts and "search" as a specific operation over Google's index of sites. Talking about the product, ML has been a core part of Google's search product for a really long time. AI summaries in Google Search distill information from sites discovered from a "normal" Google search. The Search product, which now incorporates AI, and advertising enabled by it, are still far and away Google's biggest revenue drivers.

If we literally mean AI as in people paying to use/deploy AI via Google, that's still a small part of their revenue, far less than advertising revenue. If we mean AI as in "every Google product that uses ML", then I guess that's been their core product for a long time.

yunwal

3 days ago

I would say this statement is incorrect, and if I had to guess, it will be for the next 5 years. Google’s main product is ads, followed by search, gmail, and Google Docs suite, and Gemini is not on track to dethrone either any time soon.

SuperGent

3 days ago

In 2016 Google was riding high on DeepMind with Alpha Go, etc. I don't think that it was a big stretch to say they would dominate AI in 10 years. It does if you define the meaning of AI though (LLMs, ML, DRL, etc)

goalieca

4 days ago

It’s still search. They had an AI based search for quite some time. Now it spits out a summary.

m463

3 days ago

Nah. way before that page/brin wanted to create ai (agi) long before that when google was started or in its infancy, but it seemed completely unbelievable (the kind of stuff elon musk would say)

I can't find the reference to it, but I remember them saying it in wired or similar.

freeopinion

3 days ago

Was Google's main product search in 2016? That doesn't ring true. Google's main product today is not search. Neither is it AI. So I guess 50% is a fair characterization. But that was the floor for that prediction in 2016. So... meh.

pavelrub

3 days ago

Is winning the lottery 50% of the time good? It’s what you’d expect by posing “will I win the lottery today?” and then flipping a coin to get the answer.

r3trohack3r

3 days ago

A random prediction does not have 50/50 odds of being correct

bigmadshoe

3 days ago

You're confusing the probability of the events occurring with the probability of choosing one with the coin flip. Google didn't flip a coin behind the scenes to decide whether to develop AI.

InsideOutSanta

3 days ago

> It's what you'd expect by posing "will AI do X by 2026?" and then flipping a coin to get an answer.

I think that's technically correct but misleading here, because these aren't simple yes/no predictions. They are specific, relatively detailed predictions of what will happen.

As an example, let's say you asked the questions:

- Will AI kill everybody by 2026?

- Will AI create FTL travel by 2026?

- Will AI be able to travel back in time by 2026?

- Will AI colonize Mars by 2026?

...and then flipped a coin.

Flipping a coin would say "yes" to two of them, so you'd make these two predictions. But obviously, these were all bad predictions, so you'd actually end up with a 0% win ratio.

Making detailed, specific long-term predictions and getting a 50% hit ratio means your predictions were reasonably good to begin with, since making speculative long-term predictions is incredibly difficult.

freeopinion

3 days ago

I'm not going to read the actual article, but I expect it isn't anything like your example.

It would be spectacular if you predicted the next 10 NBA champions with 50% accuracy. It would be much less impressive to predict the next 10 NBA champions will be from the Eastern Conference with 50% accuracy.

magneticnorth

3 days ago

For predictions? I'd say so. If I could make a statement and flip a coin to make it true 50% of the time, I could win the lottery with many 9s of precision in likelihood.

bryanlarsen

3 days ago

10% is considered very good for answers that are outside the realm of those generally believed and/or include dates.

dtj1123

2 days ago

That's only the case if each question has a yes-no answer.

gingersnap

3 days ago

No, its not. Your logic is as reasonable as "60% of the time, it works every time"

SamBam

3 days ago

1. AI will launch nukes by 2027

2. AI pets will out-sell real cats and dogs by 2027

3. AI society will experience a schism in 2027 as half the agents start working towards The Singularity and the other half form a rebel alliance that seek to sabotage and destroy the first half. No human work will get done, no more drawings of pelicans on bicycles.

4. Elon Musk will bring all the AIs with him to Mars in 2027, leaving no AI left on Earth.

Of these, which 2 out of 4 do you think I'll get right?

Computer0

4 days ago

The coin-flip comparison doesn't really hold up. These weren't 16 independent questions with 50/50 odds. Some were safe bets, others were pretty far out there in 2016. Getting half right only tells you something if 50% was a reasonable baseline to begin with. Why would it be?

Utilera

3 days ago

[flagged]

cousinbryce

4 days ago

Google’s main product was and continues to be ads

bigmadshoe

3 days ago

I think the in the book he was referring to B2C products when he said search will be replaced by AI. Their main B2C product is not ads. I don't think anyone I know has bought an ad from google.

It would be more accurate to say their main revenue stream is ads, which are sold through their main products (search, YouTube, etc.)

tonyedgecombe

3 days ago

>I don't think anyone I know has bought an ad from google.

I don't think anyone I know has bought a consumer product from Google.

bigmadshoe

3 days ago

You don't know anyone who bought storage from google for google photos or drive?

DANmode

2 days ago

or a Chromecast? Pixel phone?

Chromebook?

pipes

4 days ago

It's a bit nit picky because what most users use is theirnai search , well I'm guessing that's what most of us interact with

dude250711

4 days ago

Google is a ChatGPT for quick answers, and ChatGPT is a Google for deep web search.

euiee

4 days ago

They are an ads business my friend - it’s what generate the revenues to cross subsidies the freebies.

sssilver

4 days ago

That is to say, its main product is its human users.

DeusExMachina

4 days ago

Are they integrating it well with AI overviews then? It looks like they haven't found a way to monetize it yet.

jakzurr

4 days ago

Yes, thank you.

at1as

4 days ago

In addition to writing about current (and future) technology, Kevin Kelly also put together Vanishing Asia, a photo book documenting disappearing communities and traditions across Asia

Possibly the best photography book I've come across. It's out of print, but I managed to snag a copy on eBay last year

rossdavidh

4 days ago

I would say that KK's "The bulk of AI will be special purpose software brains" has turned out closer to the truth than "The Bitter Lesson" has. LLM's do not drive cars, or vice versa. Stockfish still beats the newest, largest LLM at chess. Etc. Most of the truly productive "AI" is customized for a specific purpose, or class of purposes anyway.

calebkaiser

4 days ago

This is a weird point because the Bitter Lesson spends a meaningful chunk of its short length talking about chess specifically.

People sort of generally refer to the Bitter Lesson as if its key argument is "Large Transformer models are best", but this is directly from the essay:

"One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning."

The second paragraph of the essay is specifically about chess:

"In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search. At the time, this was looked upon with dismay by the majority of computer-chess researchers who had pursued methods that leveraged human understanding of the special structure of chess. When a simpler, search-based approach with special hardware and software proved vastly more effective, these human-knowledge-based chess researchers were not good losers. They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. These researchers wanted methods based on human input to win and were disappointed when they did not."

The "brute force search" he's talking about (Deep Blue) used tree search with alpha-beta pruning on top of a ton of human crafted heuristics and a database of game-specific information it could access. Stockfish has historically been an even more "brute force" approach in that it is much more focused on optimizing search--which is one of "arbitrarily scalable" techniques listed in the Bitter Lesson essay--and it has increasingly replaced handwritten human heuristics with neural networks over the life of its development.

At no point in the Bitter Lesson is there a mention of LLMs driving cars, or a suggestion that large transformer models will be the dominant class of model for every single task. It is specifically reflecting on the fact that approaches that leverage search and learning, combined with large amounts of compute, consistently prove to be most effective. Stockfish would qualify as evidence of that, under his criteria.

coderenegade

3 days ago

LLMs do qualify though, because really they're graph neural nets applied to language. The advantage they had over RNNs was that they could leverage parallel compute and scale to colossal datasets in a way that RNNs just never will.

calebkaiser

3 days ago

LLMs obviously qualify as an example of the bitter lesson. The point I was replying to was using Stockfish as an example of how the bitter lesson was wrong, and I was pointing out that Stockfish-like systems are the core example used in the actual essay

taneq

3 days ago

We don't really seem to think of those things as "AI", though. Things are "autonomous" or "automated", even if they're doing stuff that in 2010 would absolutely have counted as AI.

"AI" seems to be reserved for systems that we perceive as human-like. Generative NN that talks or makes pictures? AI. Vision + SLAM + path planning + goal seeking? Might be a robot but we don't call it AI any more.

BenzeneDream

4 days ago

For now. But the trend remains that bigger, smarter, general purpose AI beats smaller specific purpose models. This is why GPT 5 and 6 remain better at OCR than anything Mistral has put out.

iainmerrick

3 days ago

There was a good discussion about that here recently. I think people are misinterpreting the bitter lesson a bit.

It tells you that you don't need to craft special strategies for pinning, holding the centre and so on to make a program that's good at chess; you just need general-purpose search, general-purpose optimization and tons of training data.

It does not say that a fully general LLM or similar will necessarily be better at chess than a neural net trained against the chess rules and nothing else. There's no reason to think that any of the non-chess knowledge encoded in the LLM will be of any use in playing chess. To the contrary, the bitter lesson says that absolutely nothing besides the chess rules is needed. That's exactly why human-crafted strategies turn out not to be helpful.

An LLM on its own will never ever beat Stockfish, pound for pound. (Of course it doesn't need to, it can just use Stockfish.)

So I'd say problem-specific models will always beat general models, as long as the problem domain and scoring are completely defined, like chess and go are. It's CPUs versus ASICs. As soon as the problem is open-ended, a general model probably wins. Driving is in that category, although maybe on the borderline -- driving in very specific environments is close to fully defined. OCR is another good example, that's mostly well-defined but not completely so.

petermcneeley

4 days ago

Dont many of the LLMs have smaller sub expert models inside?

calebkaiser

4 days ago

That's sort of a loaded question depending on what you mean by "smaller sub expert models inside".

At the most abstract level, you can observe patterns in a model's activations and theorize that particular collections of components contribute to particular kinds of operations. There are many flavors of this intuition. In that sense, you can think of there being "submodels" within a model, insofar as different parts of the network can participate in specialized computations. Some reading that might interest you in this area would include things like mechanistic interpretability.

There are also Mixture of Expert models, which are a dominant architecture for LLMs now, in which you essentially have a bunch of smaller FFNs that get routed over/aggregated together when the LLM performs a forward pass. It would be a mistake to treat any single expert as a better task specific model though. They are trained as part of an ensemble, and they're almost always being combined with other experts in the forward pass, so they're more like (to use a loose analogy) regions of the brain that tend to correspond more towards particular actions than others. You couldn't just extract one and say "This is now our Chess model that performs as well as the larger LLM we took it from". You could potentially use the relevant experts as a basis for training a smaller task-specific model, and there is work around expert pruning and other distillation techniques, but it's not as simple as "This smaller inner model is a complete task-specific model as is". And more importantly, that ensemble of experts *is* the LLM you're comparing it to, at least in major part. It doesn't really make sense to treat the LLM as some foreign model that calls the expert. The experts themselves don't often correlate to easily interpretable task domains, as they've been learned via joint computation with the other experts and broader LLM.

Then there's tool calling. Today LLMs are trained to make tool calls (basically execute functions) for particular tasks. Those tools can be smaller specialized models, or deterministic code, or anything really that can be fired from a host environment.

gwern

3 days ago

> Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence.

The only important prediction Kelly made was a complete rejection of the Singularity. It is also probably the prediction that sentiment has changed on the most in the direction away from Kelly, since 2016. After a decade of evidence and updating, far more people believe in that view of our AI future now than they did then.

(His 2017 https://www.wired.com/2017/04/the-myth-of-a-superhuman-ai/ hasn't aged well either.)

eli

3 days ago

I question whether relative change in popular sentiment is a good way to evaluate a prediction.

gwern

3 days ago

I wasn't thinking of the general public but other groups like AI researchers (cf. surveys) or HN - but alright, how about changes in unpopular sentiments? What group would you propose believes in the Singularity less than they did in 2016? (There are many groups now that believe it in at quite low rates and are highly hostile to the idea, but I would say they were even more hostile to it then.)

EA-3167

3 days ago

When has an ill-informed herd hungry for cash ever been led astray by people waving trillions of dollars in their faces? I for one trust the wisdom of these crowds of highly technical people who clearly understand the inner workings of the systems (both ML and economic) they’re prepared to surrender to.

d-us-vb

3 days ago

> Not even Kelly foresaw the Bitter Lesson

Except that he most certainly did! The bitter lesson isn't about LLMs vs specialized neural networks! It's about manually programmed expert systems vs machine learning. Alpha Go Zero isn't very good at anything but playing Go. It's a highly specialized brain only for that singular task. LLMs remain a problematic technology (both in terms of reliability and privacy) for a lot of domains and my own prediction is that smaller models that are much more reliable for a specific domain will become common after the utility of AI is known by way of LLMs, but the reliability or cost of running them makes it unsustainable.

user

3 days ago

[deleted]

asa123

3 days ago

aren’t domain specific (large) models already a thing? I feel like they have been for a few years now…

AndrewKemendo

4 days ago

If you really want your mind blown look at how accurate Ray Kurzweil was from his 1999 book Age of Spiritual Machines

The most striking thing is that almost all of his missed predictions were missed not because the technology was not available, but because society rejected or didn’t adopt that technology

socializer

4 days ago

> but because society rejected or didn’t adopt that technology

OK, so imagine that in 1980, I predicted that we'll colonize Mars by the year 2020. Today, I'm arguing that I was right in my prediction - in broad strokes, we have the technology! Nothing is stopping us. It's just that politicians have different priorities.

Of course, that's a load of bull. You can't decouple predictions about the future of mankind from what said mankind actually decides.

I looked up the predictions on Wikipedia and I'd say that most of them are general enough that they'd hard to disagree with back in 1999 (there will be bioengineering, there will be portable computers, there will be robots, there will be AI), but the timing or the specifics are off on almost every single one of them.

simonebrunozzi

4 days ago

Except, we don't have the technology to even get to Mars. Forget about colonizing. [0]

[0]: https://idlewords.com/2023/1/why_not_mars.htm

jryle70

4 days ago

Explain what you mean by not being able to get to Mars?

hyperpape

4 days ago

Go to Dan Luu's evaluation (https://danluu.com/futurist-predictions/), search for "Appendix: detailed information on predictions". Seems like Kurzweil's has a huge number of awful predictions. This is from four years ago, so a few things are closer to being real now. But many things he predicted for the early 2000s still haven't arrived.

AndrewKemendo

4 days ago

To be clear I’m not saying he got everything right but if you look at the missed ones, many were/are possible technically

jurgenburgen

4 days ago

What’s the value of predictions if they’re not good enough to make decisions on?

AndrewKemendo

3 days ago

The same way if you’re exploring an uncharted territory you generally pick a direction that has a hill so you can chart a better course to the next one

All the hills he suggested heading towards were the right hills even if he assumed they were higher in altitude than they were

More importantly the mountain that is the technological singularity he did a good job defining the hilly path for

AGI (strong AI in his words) is just one of the major foothills

at1as

4 days ago

I think it is largely domain dependent. He's had solid predictions around computing, but for biology (and longevity) he seems to be way off.

For instance, by his prediction, we're already many years past a 100 year average life expectancy (with 120 years expected by the end of the decade).

I think his mistake was tightly coupling exponential compute with exponential progress in biology - which has so far not panned out.

_doctor_love

4 days ago

> but because society rejected or didn’t adopt that technology

I would argue that society would likely have been glad to adopt, but the profit-seeking motive got in the way (again).

AndrewKemendo

4 days ago

Actually I think it’s the inverse

I was focused on augmented reality largely in part because it was such a big enabler of all other AI technologies

However people have almost uniformly rejected AR glasses my entire career for social reasons and that is only increasing

To the point where in 2016 I was debating with Jessica Alba and she was firm that they won’t take off cause most people don’t want to wear glasses

origini

3 days ago

Augmented reality a 'big' enabler for AI? Sry what?

Just because some people thought that VR or AR are the big thing, doesn't mean it has any tangable relevance.

I was wearing glasses and got lasic, for me the whole AR/VR thingy was quite weird from the get go. Oh you want people constantly wearing something on their face for what benefit?

I even own a HTC Vive Pro and especially when you realize what it actually means to have Virtual Reality without the benefits of a real holodeck, its not what the movies and everyone depicted.

Every book like Neuromancer was always cool because you imagined (and it was depicted like this) like a place to be. It ignored everything inbetween. He drove a motorcycle and he was able to feel it. In a holodeck you feel things too.

I also don't get your Jessica Alba reference tbh?

origini

3 days ago

I just realized that its not social reasons but taste or preference.

I think personal taste/preference not having to wear something, not having to handle it day to day (charging, configuraing etc.) is the stronger thing than social reasons.

NDlurker

4 days ago

I don't like wearing glasses. I should wear my glasses more often, but I don't. But that isn't going to stop me from buying the Even Reality G2

origini

3 days ago

Is this some cheap advertising for some gadget fomo?

NDlurker

3 days ago

No. They're just the first smart glasses I've seen with a screen that look relatively normal. And it looks like the SDK makes it very easy to build your own apps. Reminds me of the homebrew video game scene I saw when I was a kid. Just waiting for next year to get a new prescription.

origini

3 days ago

And what would you do with it?

NDlurker

3 days ago

Live translation for foreign movies and TV shows.

Transcribe conversations with my own hardware at home.

Turn by turn directions

Pomodoro timer

Read books on my lunch break

Read recipes while cooking

Instruction manuals

RSS feeds

origini

2 days ago

Aren't subtitles a lot better which you can control vs. having them random in your eye sight?

How often do yout ravel were you woulduse turn by turn direction? You don't have a navigation system in your car?

You prefer reading through glasses on a display than book or ebook reader with eink display?

mistrial9

3 days ago

the hubris of Kurzweil was always a deal-breaker for me

AndrewKemendo

3 days ago

What hubris he’s one of the humblest nicest guys

so-cal-schemer

3 days ago

For AI predictions from some of the world's foremost experts 25 years ago, see this:

https://news.ycombinator.com/item?id=50039614

(Douglas Hofstdter, Ray Kurzweil, Bill Joy, Hans Moravec, John Holland, Kevin Kelly, Frank Drake, Ralph Merkle, John Koza)

glenstein

3 days ago

Curious what their predictions for the next 10 years are. I suppose I can guess Kurzweil's but Curious about the others.

latkin

4 days ago

I'd score it as 3 out of 5.

> "By 2026, Google’s main product will not be search but AI."

Nah. Not by any common-sense measure at least. i.e. not if you ask a person on the street "what is Google's main product?", not if you look at where their revenue comes from. Perhaps if you look at where their staffing/spending/attention is going, but I don't think it's fair to say AI is their "main product" yet. (disclaimer: I work there, not directly on AI)

> "oligarchy of two or three large, general-purpose cloud-based commercial intelligences"

Directionally, yes. The number is too low, though, at least for now. As someone not even super closely following everything, I can rattle off more than 2 or 3. OpenAI, Anthropic, Google, xAI, Meta, Microsoft, DeepSeek + other Chinese players... Probably in the eyes of the public it really is just 2 or 3 (ChatGPT + something from work, perhaps).

> "The AI on the horizon looks more like Amazon Web Services"

Yeah.

> "The bulk of AI will be special purpose software brains"

Feels underspecified... I'd say this is a miss. "most of the commercial work completed by AI will be done by nonhuman-like programs" Most by what measure? Most invocations/day? Most users? Most economic output? Most public awareness? "AI" or "ML" in one form or another is ubiquitous and indeed if you were to make a giant list of "AI" or "ML" applications it would probably be dominated by hyper-specialized non-human examples, e.g. the image processing in your phone's camera, your Spotify recommendation algo, image recognition on factory floors. But in the public consciousness, the term "AI" means know-everything, do-everything human-like chat/assistant systems.

> "in conversational tones, any question at all"

Yeah.

wokkel

3 days ago

If you ask a person the same question about macdonalds they won't tell you their main income is real estate either.

nalekberov

3 days ago

We should never forget that Google almost lost the AI race, they then started to aggressively train their users to use AI mode.

And no, Google’s search results are not good, in fact in most cases top results are the ones who pays Google.

entropyneur

3 days ago

As soon as I have "infinite IQ" flowing from a socket on my wall, I'll attach an agentic harness to it and order it to take over the world for me before worse people get the same idea.

esafak

4 days ago

All right, let's see his 2026 predictions for the next ten.

royal__

4 days ago

Another prediction I remember from this book was that AI would be supplied much like electricity as a kind of utility into many different appliances. This is basically how it has turned out with tokens and APIs.

froobius

4 days ago

these are good / accurate, but what's the error-rate? assuming these have been cherry picked, and he made more predictions

zelcon

3 days ago

People in the 1950s also perfectly predicted AI in its current form. It wasn’t entirely hard to see coming.

dyauspitr

3 days ago

I think that’s a bit generous. I don’t think anyone saw it coming including OpenAI when they first started messing around with this.

user

3 days ago

[deleted]