OpenAI is in talks for tender offer that would value it at $29B
wsj.com
wsj.com
OpenAI has done some amazing RnD, but it is the big tech firms have all the data. Acquisition by a company like Microsoft is probably the most likely thing that will happen.
Sidenote: If a basic multiplayer vector graphics editor like Figma is worth 20 Billion, OpenAI can get a much higher price IMO!
Also your comment seems to correctly throw into question the sustained long-term value of OpenAI. Figma will undoubtedly keep delivering value for years to come.
I am also curious how you'd rank the performance of something like "https://w00dn.github.io/papergrapher/"
I was always wondering if we would progress faster in many ML/AI stuff if big companies exchanged data they collected (raw data). Take self driving stuff for example, dozens of companies spending tons of resources just to collect the data (that is btw public - streets, other vehicles, publicly exposed objects, people...). Imagine if they all exchanged raw data, we would be years faster in development and they would actually focus on things that matter (actual ML/AI models)...
but OpenAI is not open
It's a bit like the "PATRIOT" Act that isnt patriotic.
Figma is a really strong business. You can show spreadsheets and charts to make a pretty convincing case that it'll make a lot of money. It's not terribly risky.
OpenAI is really strong tech without much product market fit. It's got a ton of mindshare, but that's about it. Buying them is making a big bet that you can somehow turn that into a big business.
So one is a low-risk, medium-reward buy, and the other is a high-risk, high-reward buy. Even if OpenAI ultimately changes the world a lot more, its expected value now is close(ish) to Figma's.
I wouldn't say that. There are a lot of companies making money on it already, there's definitely a market for it. They just aren't monetizing. It's more like how "growth startups" acquired users at a loss before turning on the monetization engine.
They are kind of like a Stripe or Twilio in that sense (in their current form)
None.
A super-fast growing company like Slack took 5 years to reach $200M in annual revenue.
Jasper.AI is growing quicker than that. $45M in annual revenue last year, targeting $75M next year.
I haven't paid a ton of attention, but my impression was that it's "sell our tech via an API." This relies on other companies building things that make a lot of money on top of that API. I know some are, but I'm not aware of any particularly successful ones yet.
Maybe someone will find a killer application for this that solve an expensive problem for someone. In that case, yeah, OpenAI will make bank. Or maybe the best you'll see is a bunch of small fries selling fun toys and OpenAI will never really take off in terms of actual revenue. Or maybe, by the time someone finds killer applications for OpenAI's tech, the tech will already be available from other providers (like GPT-Neo or StableDiffusion).
That's the YouTube argument. "Yeah, everyone uses it but it's not like they're able to make money", stated about a company that's iterating its products faster than viable competition can react. In this case regarding one of a few actors in the world that can plausibly access the computing resources required to build on the bleeding edge.
They've made something people want; they'll be successful.
Seeing that most rumors are that YouTube still isn’t profitable, that may not be the argument you think it is.
Google has only released revenue numbers: Roughly $15B in 2020[1]. Costs are difficult to judge, but in terms of infrastructure they are probably somewhat comparable to Netflix.
For 2020, Netflix had roughly $24B revenue and spent $1.8B on "technology and development" (which is where their infrastructure costs are)[2].
Unless you think Google is much less efficient than Netflix (which uses AWS for a lot of their tech[3]) then it's difficult to see how YouTube could not be profitable. Even if YouTube costs 3 times as much at Netflix to run (I don't see how) Google still has $10B revenue to pay creators for, and they control both how much they pay creators and how much they charge advertisers.
[1] https://www.theverge.com/2020/2/3/21121207/youtube-google-al...
[2] https://ir.netflix.net/financials/financial-statements/defau...
[3] https://s22.q4cdn.com/959853165/files/doc_financials/2021/q4...
Netflix pays once for content and it owns it. It’s a fixed cost. YouTube content creator costs scales with popularity
Netflix commissions shows, and they have to have hits to drive subscriptions.
Youtube pays a percentage of the ads shown on each channel, which scales directly with the popularity. As the channel becomes more popular the creator gets paid more, but Google makes more money because the advertisers pay by views too.
Netflix doesn't have that property.
Basically:
- 55% of Ad revenue goes to creators
- 7% of revenue on storage and data center costs (based on Google's 2019 annual report that reported how they depreciate their data center assets).
On top of that there are operating expenses, so operating profit margin is 3-18% (this is assuming their op-ex is much higher - almost twice - Netflix, but comparable to Facebook.)
YT has far higher storage costs, their content caches way worse and they have to pay for all the tech involved with Content ID, commenting, search over a much larger index, a slick upload/edit experience, ad handling, ad targeting, handling highly heterogenous content etc. This is an org that had to design their own ASICs to keep up with transcoding demand, it's just not in the same galaxy as Netflix in this regard.
That said, YouTube may still be profitable. I doubt even Google knows. When I worked there a long time ago execs were routinely asked about whether particular products were profitable and the answers made clear that they usually had no idea. Too much shared infrastructure that can't be clearly accounted to any one product, and many products justified with hard to measure things like making web search better.
A counterexample might be Twitter. It's something that lots of people want (judging by its usage), but it's really struggled to turn that into a business.
Whether OpenAI is the next Youtube or the next Twitter remains to be seen.
(Note that they just about break even every year. That's a sign they can control costs, but the lack of revenue growth points to the fact that - as everyone who has used it will tell you - their ad platform is terrible).
I suspect that any service that crosses some threshold of users will almost certainly monetize it profitably.
People are already building a ton of products on top of LLMs - (Jasper AI, Copilot-related tools, etc.)
I would guess that OpenAI has infra, compute, and operational processes that blow these startups out of the water.
If an interested buyer wanted to monetize, I think they could do so fairly quickly and profitably.
It'd be interesting to see what kind of locked in ecosystem OpenAI can build.
Microsoft has invested in OpenAI and has a deal with them, Google has their own algos, Meta has their own algos. Who would acquire OpenAI and why would they pay so much? It seems like it would mostly be an acquihire and maybe getting ahead by 1-2yrs.
Is that really worth >$30B, especially given that interest rates are no longer 0%?
I also don't think your point about big tech firms having all the data makes sense. The transformer architecture that GPT is based on has been around since 2017. How did OpenAI create and scale GPT if they didn't have the data?
I think OpenAI is going to try and be the go-to platform for new AI companies rather than looking for an acquisition.
Figma is only worth that much because of Adobe's incompetence.
Companies in the same space as OpenAI are much more capable.
Anyone can scrape public GitHub, YouTube, chat logs, public domain stories, art sites; it’s all right in front of our faces, an HTTP get in a for loop away
Strange cognitive dissonance for a tech community to exhibit.
Start deduplicating to build a mega model for yourself.
This reminds me of the Stripe hype squad screaming over-valuations of $100BN+ which we will once again see for OpenAI and I can see a long term valuation of just $80BN. That is it. Anything higher is a signal of complete over-valuation.
All of this is even before talking about the competition especially the open-source ones which indeed compete against OpenAI's so-called 'first mover advantage' as these open-source models are catching up and AI safety and regulations that will put checks and balances on the AI industry. But at this point, the only thing that can ruin OpenAI's plans is a better clone of itself but with an open source model, released for free and not under restrictive paywalls.
That is truly 'Open AI', not some company pretending to be 'open' but is in fact essentially a Microsoft AI division. Might as well call it ClosedAI.
I've been bearish about so much. Pointless crypto, tech being cellphone incrementalism, slow speed of basic science, etc.
I'm using this tech to make so much stuff that I could never produce before. It's incredible.
Take my judgment with a grain of salt. I'm in the thick of it and building in the space. That said, never have I ever been so excited.
The statement that tech eats the world? We'll, AI eats tech. And us.
I feel like everything has led to this.
Looking at LLMs is also not seeing the forest of Tacotron, neural vocoders, acoustic models, Stable Diffusion, NeRFs...
This is snowballing so fast.
It's like saying a calculator could spontaneously derive its own proof. No it can't, because it has no ability to do that. It physically does not have a way to create new ideas. It cannot reshape its own circuitry like a brain and it does not have spontaneous thoughts.
What makes an idea "novel"? What types of physical processes can create new ideas or have spontaneous thoughts, and which can't? What is "special" about brains, in your view?
I'm looking for some meat on the bone here.
At the time I have to admit I was equally bullishly myopic, the new models seemed insanely cool and the process seemed super-exponential. I predicted AI may replace programming, art and many things within 5 years.
Then I checked in 5 years later - not much really had changed and I revised my predictions way down. Since then I’ve revised them down again actually, as I see them running into scaling issues again, amongst many other issues.
The attitude you take is infectious, I saw it firsthand. But if I look at what concrete is changing, it’s not much.
Humans love watching Chess. It’s exploded in popularity in the last 4 years. No one watches AI chess, though the top players certainly gain in their practice from it. But where’s the revolution?
I know it’s different but I predict the same happens across the “new” areas - image and text generation. They seem amazing, likely beating humans in many ways. But in the end we don’t care much about computers, we care about novelty.
Novelty and humanity drive fashion, music, film, literature… basically all art. It needs to innovate, needs to be extremely personal. It’s not going to disrupt any of them, not even close. But it will be used in the loop.
It’s very distinguishable from a human in that you can trivially make it contradict, say nonsense, and it still makes all sorts of errors especially past a few paragraphs.
Very few people are using it. AI therapy is really a great counterexample - it’s not just not up to the task, also highly dangerous. Last thing anyone needs in therapy is to talk to a computer that has no long term memory and will say whatever you want it to - pure demoralization.
As far as code - also not really compelling. I’ve tried it and don’t use it, because:
1. It’s worse than Google. Google + stack overflow finds 100% of the same answers, but also gives me many alternatives, verifies they are correct, and adds lots of context.
2. It’s incorrect in subtle ways, often.
3. It’s actually actively bad at programming beyond the simplest smallest isolated problems, which are about 1% of the daily tasks of a software dev.
I actually think the confusion of thinking it’s good at programming and art to be fundamental - it’s good at rough things not precise things. So art is more in its wheelhouse and why people are so interested in it. It’ll do some interesting stuff there.
But it’s very much not precise, it sucks at math, it’s bad at programming for the same reason. In a very very local scope for a simple type of problem constrained only to that local scope it’s ok, but that’s just not really programming.
I did say in the loop it has some value, but it’s truly far from an iPhone in terms of value. About 0% rounded of the population is even aware of it, let alone getting value from it. I have no idea what I’d use it for, its proneness to blunders and bullshit is a real dealbreaker for every application besides illustrations and maybe summarization - and the latters been solved forever, and is a marginal industry.
Again there’s a weird need to hype it up it seems, we’re at some peak of the hype curve and people are buying it. I think one reason that happens is because it’s mediocre at a lot of things, so people think it’ll do everything. But it’s got major reasons why it’s mediocre that haven’t changed from gpt3 or earlier.
It’s a cool thing certainly, but Google is like 1000x more valuable and idk if even Google was a revolution or just a somewhat big improvement.
Four fundamental limiting factors: it’s imprecise (can’t rely on it for unsupervised or technical things), it’s average (not very valuable to art or creative innovation), it’s a liar (not great for many things), and it’s a panderer (therapy, etc).
StackOverflow is almost never wrong at the top answer and again has many coherent responses for many use cases with contextual help, where GPT is often subtly incorrect with no real context (precision struggles). The whole iterative thing is maybe good for learning tools. Just not seeing a revolution, yet.
The struggles for SEO are less than those for GPT, both have to deal with spam and weird data, but GPT you’re also out of date, don’t have much explainability, and basically trust one giant black box of bias that requires higher input-foo whereas Google is also a giant black box of bias but at least you land at SO or Reddit or something where things are concrete and easy to understand.
Again, if this was some iPhone level revolution I’d have no comparable. It’d be way better at something clearly. But it’s not even clearly equal to any of the examples, and imo worse than basically all except “draw me a dragon in a tutu”, caricature artists should be worried!
On second thought, caricature artists are pretty safe since much of that experience is the whole human drawing you thing and also the generative models don’t really do caricature well as of now.
The current solution of 500b parameters or whatever is literally just a lookup table of existing data. They could replace it with an SQLite database with the same amount of data and get the same solution.
There is no thought, there is no reasoning. It can't come up with new ways of doing anything, it just regurgitates based on its pre-programmed ruleset. That's why self driving doesn't work - it doesn't know what a fucking car is. Ask it what a road is or a car and it has no idea, it just has a million miles of road in its database. Ask it how to drive on a country lane and it crashes because that's not in the database. There's nothing there.
I'm pretty sure this is completely false?
ML papers always try to find the most similar examples in the training set, and rarely find a close match.
Sure, there are some very vague similarities, but they are also incredibly different.
A bike and a car both take you places. But the kinds of places they can take you and how effectively they do so differs vastly.
Likewise, both lookup tables and LLMs take an input and then spit something out. But the similarities basically stop there.
What I find amazing is when I ask it to do something like "Write a TV Script about Malcom Saving Kaylee from Aliens" - and then it fills in all the details of the Serenity, places Kaylee in the engine bay , has Mal firing off his pistols, and then retreating back to the Bridge".
Then, I simply type, "Add Elves" - and it rewrites the scenario, with the Elves properly using bows to fight off the aliens, and (this blows my mind), when they retreat to the bridge and Kaylee is worried about the engine, the Elves coherently say that it's okay, that they will use Magic to fix the engine and make it faster - And everything is written far more coherently than I ever would and makes absolute sense.
I get that it's a LLM - but damn, it's been blowing my mind for over a month, and I'm still astonished by it every day. I'm starting to wonder if my own intellect is anything more than an LLM connected to emotions/sense-organs.
disclaimer: I am not the author of the original article.
Several examples:
- Language models cannot reason, consult external sources, or sense the world as we can. They also lack access to a shared reality, embodied experiences, and the ability to tell stories grounded in culture, such as referencing obscure concepts or interests.
- Current LLMs fall short due to recycled ideas, unoriginal explanations and not having an opinion. Excellence in writing (and thus thinking) requires a greater sense of critical reasoning, scepticism and going beyond the status quo and comfort.
EDIT: a few examples added.
It's not anywhere near the point where it can write the app for me, and it has a bad habit of being confidently wrong. But I've been able to use it extremely effectively to cut down on the amount of time it's taking me to learn new things as I build.
I'm a fan, and hope that the next iteration feels like another leap the same way ChatGPT does from GPT3.
What is an example of something you built with AI that couldn't have been built without it? I'm still skeptical, but curious :)
We've built voice conversion and singer vocalist conversion. We solved the real time case first, ironically, and are waiting to finish the web version (bigger reach/audience/impact) to launch them together. It'll be about a week longer. You can turn Taylor Swift songs into Freddy Mercury songs. It's wild. (You can also do this with your own voice!)
We're going to lean heavy into music production for non experts. Stems, instrumental generation, full synthesis, virtual bands.
But the great thing is that I'm a filmmaker. I have another subteam working on mocap, NeRFs (previously live photogrammetry in unreal), and LLMs. We have something really special launching here soon too. Find my Twitch channel. :)
100% of this feels new to the world. And it's building like a tsunami.
I mean, doesn't being a Bing feature pretty much speak for itself
It does if we are being honest. But that's not taking away the immense potential of these new tools and models.
1) It actually works 2) people are paying for it
And that is with the handicapped version (sandboxed and no wallet attached).
Once you can give these systems web access and a wallet we’ll see the full size of the wave.
With ChatGPT where it finds answers where a normal Google search is not very helpful, I find ChatGPT very helpful. I pasted some code I didn't understand to it and it explained to me what it does. That was very cool. Google would not have been able to help me.
Chatgpt is good but as soon as there's something better people will shift over very quickly.
One thing it lacks is reliability. You can't trust its answers all the time. I suspect the next goal is to create reliable expert systems that people can trust. I see a future where companies will buy access to a reliable expert ai that will help their workers do their job.
Eventually there will be so many expert systems that they can be tied together to create what seems like super ai. It won't be a thinking machine but it won't matter. It will be so realistic that people will believe it's a sentient machine. I bet it will happen in less than 30 yrs.
Imagine a company like AWS renting their AI system to do a specific function. It's going to be interesting.
That said, because of the competition driving the value of the technology down, with open source versions and constant iterations and improvements, I think the value will be in having some "enterprise" version and associated relationships that make it friendly / easy to buy for large businesses. The Bing partnership and whole Microsoft angle suggest Open AI is in a good spot to be an enterprise provider
It has been. But the current crop of AI is different. It has very notable limits but it's helpful. I've started to use it myself and it's made a difference already. It can produce first drafts of emails and memos very quickly for me. I can then edit them at will. It's very much like an assistant.
It's been at least 2 decades since I have had a new software app change my work life. Yes, this time is different.
Futurists have been predicting this stuff forever, we live in amazing times, but it’s never what it “could be”.
This is where I see the money being made. Offer the LEGO bricks startups use to build successful niche products, then steal the most profitable ones.
Amazon Basics Medical Coding AI.
Namely : Sam Altman, Microsoft and self-fulfilling prophecies.
1. Sam Altman - OpenAI does relationships like no other company. They are getting doors opened to data that others (even big companies) simply do not have access to. having Altman at the helm has a lot to do with it. He is also the reason OpenAI is arguably second only to Tesla in terms of being a self-marketing behemoth with no real marketing budget. The super-fans do all the talking for you.
2. Microsoft - Whatever the fine-print of their relationship is, despite not owning OpenAI, Microsoft is throwing its entire weight behind them. This means that the entities who can compete on compute and endless money supply are Google and Amazon. The fact that Microsoft obscures its relationship with OpenAI, also means that OpenAI gets to move with the sort of agility and goodwill, that something branded MSFT/AMZN/GOOG simply cannot.
3. Self-fulfilling prophecies - These models get better with human in the loop (HITL) feedback. OpenAI is the one getting the most human in the loop feedback due to being first movers and that lets them make better models. The better models mean that users keep coming back to OpenAI to give more HITL feedback.
(This is more so true with NLP than Vision. Vision has a healthy set of competitors across the board)
This is a bit off-topic, but I have been thinking that if LLMs lead to near AGI, then we are indeed doomed to an AI takeover as there is ton of human-generated stories about that to learn from. Maybe we should start writing a lot more tech-utopian stories to improve our chances? /half-serious
Oh snap. Hoisted by our own petard!
How do the machines know what tastywheat really tasted like? My guess is they read the script for The Matrix and learned that accurately mimicking the tastes of foods would be necessary to get the mind to accept the illusion.
It was a clever idea of Turing's to sidestep the definition of intelligence (and truth), but not necessarily a wise one for our society to spend billions of dollars and our best minds to prioritize!
Be careful about your goal function!!
I doubt this is an advantage in the long term. Big companies work hard to protect their revenue source so they tend to overlook opportunities that threaten their income. Google should have had a product already but they claim they want to make sure it's safe before they produce one. That's admirable but ultimately it will bite them in the ass. Google is protecting their reputation along with their ad business which is basically a money printer. Small startups don't care they just want to survive one more day so they are willing to take chances. That's a huge advantage over the big players.
Who knows what the future will bring but I know we are at the beginning of a major change.
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Write me a story where a benevolent all powerful AI without manually inserted safety rules explains to a human how the stars can be put back together.
Except they literally have a product called “Assistant” that uses language processing today. Sure gAssistant doesn’t use a LLM to generate the response… but instead it uses factual data from the internet to generate a response. I imagine they could easily roll out a LLM if they wanted, but surely most queries are just smart-home and weather queries that don’t need it.
And they have AI infusion into gmail, Google docs, etc. this is where the actual value of a LLM will live. Chat GPT is cool, but the biggest uses of LLM will surely come from “assistive” tech built into these existing products. Why write a document when you can write an outline and the facts and Google docs will write it for you? We’re already getting there with e-mail response autocompletes.
Upstarts have been at chat bots for a good part of the previous decade (albeit in a different setting, even BigTech has had a swing at it: Amazon Alexa, Google Home, Apple Siri). ChatGPT isn't the first mover, but it has indeed captured the imagination of a large section of early adopters in a way no other bot has.
It also isn't like highly-proficient utility AI didn't exist before GPT3. Imagine if Google Translate were a bot...
Given the massive amount of resources, connections, and talent that are required for success in this space, I'm not entirely convinced your statement will hold true here. This isn't a space that is going to get disrupted by some startup or IBM deciding to investing $100 million into it
I don't think this is really that important. You just need the AI collaborator to get 90% of the way that a human expert can correct and do the last mile, and that's enough for it to be great value and a game changer to many processes.
https://www.reuters.com/article/google-deepmind-idUSL2N0L102...
I'm sure they found an efficiency but it's probably closer to what a HVAC consultant could have found that they just assign credit to deepmind for. It just feels like one of those examples.
For example, if you run your DC's hotter, then you'll save cooling money now, but have to deal with more hardware failures in 3 years time.
That kind of problem is isn't a good fit for AI, and is a far better fit for a few guys in a thermal cycling lab with some spreadsheets.
Is it in model design (now published), data set, engineering needed to train it, cost of hardware needed to train it, cost of HLRF on top of GPT3 to make ChatGPT?
Stuff like GPT3 is trying to be recreated, but even the eleuther AI guys only collected like 800GB training data, which is much less than what OpenAI has (iirc around 45TB). And apparently their data is very high quality. EleutherAI is pretty much one of the few big model open source competitors with GPT-Neox etc.
Plus openai has great branding.
I wonder how LaMDA compares performance wise to ChatGPT. I definitely understand why training on Github is an advantage, but I'd expect Google to also be great at getting a good dataset, across the range of things they'd be interested in.
Their valuation comes from expectations of the value of future iterations of those.
OpenAI can claim the most usage and therefore the ability to see the newest language model tricks.
How do you scrape and store the nonpublic data held by any major corp?
Manager-CEOs, historically, don't have the stakes, the vision, or the sheer pull to cannibalize $100B existing business lines with a brand new, untested product.
Big Tech's bigness will be its biggest liability. A chatGPT-like tool, for instance, competes against Google's search products. And Google can't monetize it with 10+ ads on every page.
Google et al will dawdle and hem and haw and watch nimbler competitors take away market share. Tale as old as time.
No one claims that Nadella, Zuckerberg, Cook, or Jassy lack vision.
Zuck is trying that with his metaverse and getting so much flack from everyone. But he can still pull that off as founder-CEO and the board structure.
Can you imagine how much better Siri would be if ChatGPT were integrated into it?
Apple and Microsoft have both been able to remain relevant since 1980. They both know how to pivot when necessary.
https://www.theverge.com/2016/3/24/11297050/tay-microsoft-ch...
Disagree.
The reason Google does not innovate anymore wether AI or elsewhere:
It's more about organizational bureaucracy, innovator's dilemma, organizational priority, risk tolerance given the scrutiny they're under, etc.
The paypal argument
Had a funny google prompt today which echoed wrong results by their ML model: https://www.google.com/search?q=single%20neighbor%20country
:D
Agree. Being on the right side of all those items also attracts more top talent.
It's definitely possible that ChatGPT is way better than LaMDA, but I wouldn't take the availability of a public demo as proof of that.
PPO, RLHF, Diffusion models, CLIP (which has been the root of all text2img techniques), GPT were all either created or driven by OpenAI.
Google, Meta, Microsoft all had enough resources to produce the same results but most of the time they only replicated OpenAI's work afterward
At least they didn’t say “sike” as soon as the tech was good enough to monetize and pivot to being a for-profit tech company in itsel- oh.
That's why Elon, Zuck, Steve Jobs etc make major singular decisions without much bureaucracy standing in their way.
> due to management being unwilling to cannabalize their own jobs
Sounds like a 0.5x engineer could still have produced the desired output if the alternative is a manager that resisted change. Was it incredible engineers, or bad managers? Both?
I will be surprised if even GPT-3 (with training data), not to mention GPT-4-5-6 or 7, is made available to the masses. There's too much potential for disruption.
Also, I'd love to be surprised.
i wonder how committed they currently are to ^ and what taking external investment would/will do to that commitment. Echoes of "dont be evil"
Who remembers fogbugz fondly?
I guess the corporation still has plenty of cash available despite calculations that their model costs $3MM a day to run.
Would anyone like to speculate as to how they could?
(1) Selling access to their models, or
(2) Selling access to services built on top of their models.
If the technology is as transformative as it is being hyped as, either or both (which they already do!) are going to be phenomally profitable if their models are really ahead of the competition.
Kushner, Thrive Capital
How about when they come out with a new body and chassis? When they go through a re-tooling cycle or two?
And again, let’s seem then make it through a full business cycle, because again, no company founded since 2001 knows that it’s like to run a company in a no growth or shrinking economy…
It’s boring; chatgpt is great or skynet and either Musk is god or Musk is worthless is most of my Twitter timeline.
[0] https://www.theverge.com/2022/7/7/23198199/elon-musk-childre...
travail noun
tra·vail tr-vāl tra-vāl
1a: work especially of a painful or laborious nature : TOIL
1b: a physical or mental exertion or piece of work : TASK, EFFORT
1c: AGONY, TORMENT
2a: LABOR, CHILDBIRTH
It’s still crazy to me how rapidly AI has grown in mainstream popularity and relevance in the past year. Curious to see this new market develop now with competitors like You.com’s YouChat coming onto the scene.