America Forgot About IBM Watson. Is ChatGPT Next?
theatlantic.com
theatlantic.com
IBM's marketing department, at least at one time, understood that public recognition drove sales in the boardroom. Stripping the company of any and every product that was consumer or consumer adjacent hurt their recognition and reputation. Of course, this is just one of many reasons IBM is less of a company today.
Pretty easy to look back and see their amazing "innovation" every 5-10 years, which never results in any lasting product.
In the in-between time, they farm employee IP via contracts taking all past and future rights, and profit on that IP ownership for no ones benefit but the IBM management. Disgusting company. Pour one out for Redhat.
I do think maintaining some brand lustre in consumer-facing products for lines of business where it at all makes sense is a potent recipe for excitement in enterprise etc.
I did help a consulting customer, a long time ago, try to use the Watson NLP APIs, but I was not impressed.
I am 90% retired and in my 70s but I still do a lot of coding and writing for my own enjoyment. Even in my retired lifestyle, the OpenAI APIs (and Hugging Face models) are so useful and easy to use that they have changed the way I work. I feel like most of the busy work is now stripped away and I can be more creative. Bing+ChatGPT search has also been transformative and I look forward to seeing what Google builds Bard into.
It started off rosy, the CEO of the company we were partnered with had fully bought onto the hype train. Every meeting we had he couldn't help but mention Watson (by name). I tried to gently warn him that maybe, just maybe, he had fallen for the marketing hype and the idea wasn't going to be a blockbuster like he thought. It fell on deaf ears.
Over the course of the next year or so, they pumped big $$$ into the project. Eventually, the project was quite delayed and over budget and the CEO stopped bringing it up. I asked about the status of that Watson thing and the look on his face said everything. Not long after the entire leadership team was shaken up.
I love how acknowledgment of misinvestments are always brushed off, thrown down into the abysses of managerial due to incompetence losses, with little to no written traces of what happened. that look in their eye is also a warning to not bring this up again.
At least Watson was an unknown. We got mostly annoyed at other things where we could say "You already tried that and it didn't work, which is why you're talking to us". Even though they kept living in hope that something would turn big masses of noise into clean data with no effort.
A decade later, they might actually be getting that. I'm skeptical, but at least it's a real thing.
In 5 years it'll be the stable diffusions and alpaca's, and other open source models ruling and closed source will be mostly for vertical specific custom use cases. Generative AI on the hand --- it's never going back to the way it was before, not unless we have a Carrington event, that is.
This is not a bad thing, but it's a very predictable thing. We're making inanimate objects simulate talking to you, and we're going to see consciousness when it's not there.
They were good at that, but then the tech didn't meet the lofty expectations that had been created.
ChatGPT is entirely different - people understand instantly how useful it is and don't need marketing and sales people to peddle it.
They convinced executives to do a pilot. It was a shit-show. Quick and dirty API endpoints running on bluemix and spewing 500 internal errors at every gust of wind. Nothing remotely intelligent.
I simply don’t recall anything else where most people’s opinions spiked then fell so rapidly before.
That said, it would be insane to use it at work to generate content in a domain you're not an expert in. It is an amazing productivity tool, but it does not change the reality that faking expertise at work is always going to bite you.
"The dirty secret of artificial intelligence" - https://news.ycombinator.com/item?id=35832168
It raised over $11B so far.
If we follow the money, the valuation, on either low or high end is absurd. It's starting to seem like weWork at a larger scale.
Revenue for 2022? $3M
No trace of financials for 2023, other than some expected 100M or 1B depending on the wind for this year. we can be sure of one thing, when numbers can be used to boost interest and claim a success, we get numbers release faster than accounting even settled.
My guess is that they are burning at least hundreds of millions by poaching the best AI talent from Google. Infrastructure wise, about the same. And no, it doesn't help the bottom line to be owned by a massive cloud provider, it only distracts since there is a senior VP somewhere in there now who's non negotiable OKR is complete migration to MS Azure. But openAI will still be charged like any other client.
Welcome to vampirizing VC at its best. Put billion in to inflat the bubble, if it wins great, if it doesn't then you would have sold plenty as a prefered must have no choice parter for services, and by then insider info would have given you a head start to sell of a good chunk of your bags anyway.
OpenAI may win the prize for the most hyped business and fastest skyrocketing company in the entire history, it risks to not even explode but worse: to disappear in the deep sky with backers pulling their hair off or each others when they finally get to receive the in and out reports. Everyone else will have moved on, subscribing to a plethora of dedicated services built by tiny companies improving on moderate size models and building catered solutions.
It will be a great happy ending after all the spams we've seen everywhere over the internet about this gpt genius tech.
If ChatGPT was all that, you'd imagine at least 10x with OpenAIs founders keeping control being the baseline
[0] https://garymarcus.substack.com/p/is-microsoft-about-to-get-...
This is going to just become a ubiquitous tool and part of the computing toolbox. I am increasingly thinking the winner take all dynamics of social media and search may not apply. There’s going to be many cloud hosted AIs and lots you can run yourself if you feel like spending a few thousand dollars on hardware. That cost will fall as more special purpose NPU hardware enters the market and acceleration even becomes a standard part of CPUs.
As a concrete example, assume that embedding vectors are just two dimensional (in reality OpenAI's are 1536D). "cat" might map to [0.1, 0.9] in OpenAI while the same term maps to [0.7,0.3] in another company's engine. The mapping is completely non linear so matrix multiplication or other basic tools cannot be use to find a mapping. The "Rosetta stone" in this case is another massive LLM which would be exceedingly expensive to create. I'd posit that this will create a moat and OpenAI/MSFT are in a good position in this regard.
Completions have such a simple API I can see this becoming almost as ubiquitous as s3. It will be trivial for companies to switch this aspect.
On top of that, given Metcalfe’s law, communication and coordination cost of a team grows exponentially with the size of the team, which means small teams have a huge advantage over big teams
OpenAI/Microsoft don't need superior performance to have a moat (though they do have it, at least for now). They could just use the existing moats and make them unassailable. Or add new moats via API or hardware.
That being said I still see a much shallower moat here than there was in say Internet search. I can download a model in a few minutes. One could not download an entire web crawl index in any reasonable amount of time, nor could one update the index in real time continuously without enormous amounts of bandwidth and compute. DIY self hosted Google was to my knowledge barely even attempted due to the intrinsic difficulties.
This stuff is also not about communication or sharing, areas where there are very strong network effect moats. It’s not chat or social media or collaborative office software.
Generative AI is more like a stand alone application software. It can be hosted in the cloud but it’s easy to stand up competitors and it can be self hosted if you are willing to spring for the hardware.
I’m not saying you won’t have big dominant players, just that I see more opportunity for competition and diversity here than for lots of other things.
An example business need: Take all my documents and create an LLM acting as an internal knowledgebase.
Likely eventual Microsoft/Google solution: Press this button to take all your documents from your Office 365/GSuite account into our LLM. The LLM provides answers and links to the original document. We have automatic retraining, but you can remove/add data and retrain with a few more clicks. We have set up authentication and filtering so that unauthorized users can't get at your data.
Likely eventual OSS 'solution': Find your API key, download all your documents, and train them manually on the NVidia GPU cards you bought. Setting up a virtual python environment with CUDA is easy-peasy! Now, host the documents on your curlftpfs host so that our LLM could link to them.... (I could continue but this is too depressing).
I’m not saying there won't be small players, but the Google Research spin of 'no point in investing anything, Open Source will eat all!' was silly in the extreme. There'll be other products, and a smart enough OpenAI has good chances to create its own moats.
Similar things could be said for example about SpaceX with reusable rockets, but with a capital intensive industry like that it might take a decade or more for others to catch up.
Software iteration time is very fast, so if OpenAI slows down others could catch up in a year or two.
There are some newer models out there I have not tried yet like the open llama, GPT4all, etc. so I’m not sure how good they are. I get the sense they are still GPT-3 level but are achieving that with less RAM.
There’s a race on both for raw capability and optimization via pruning and quantization. The latter is important to make these things runnable locally without gigantic hardware. Lots of people have stuff with GPUs, fast CPUs, and 32-64G RAM. Few have huge workstations with hundreds of gigs of RAM.
Unless progress stagnates I can see something approaching GPT-4 that can run on under $5k worth of hardware in a year or so.
Open model progress seems to be lagging only 1-2 years behind big cloud hosted models.
Probably the fastest way to get started is to look into [0] - this only requires a beta chromium browser with WebGPU. For a more integrated setup, I am under the impression [1] is the main tool used.
If you want to take a look at the quality possible before getting started, [2] is an online service by Hugging Face that hosts one of the best of the current generation of open models (OpenAssistant w/ 30B LLaMa)
[0]: https://mlc.ai/web-llm/ [1]: https://github.com/oobabooga/text-generation-webui [2]: https://huggingface.co/chat
Not sure about how this related to the founders, other than Sam Altamn apparently having zero financial stake. I'm guessing the other founders may have put money in and have capped-profit deals ?
The “Open Source” ecosystem will drive the innovation and keep the technology in the news cycle and public mindshare. Many businesses will not or cannot pull technology out of the chaos of that world and will look to Microsoft.
Google and Meta can incorporate this stuff into their other products but it is going to be harder for them to sell it directly.
People are perfectly able to install and run their own PostgreSQL databases, or Kafka brokers. The software is free and doesn't need much resources.
Still, MSFT is selling its PostgreSQL-as-a-service and Not-quite-Kafka-but-similar-enough-as-a-service thing to a shit-ton of corporations which maybe once had the capability to host this for themselves, but have lost it while they migrated all of their stuff to the cloud. And they do so at a hefty premium.
The same thing will happen to LLMs. Anyone will be able to run an own, custom-trained variant of sufficiently capable models on their own hardware. But most corporations will not do it. Just like they don't operate their own Kafka broker. They will instead pay a lot for MSFTs cloud service offering, which of course comes with the crucial promise that their data is safe and secured and handled in a way that is compliant with all privacy laws. Which of course isn't true, but that doesn't matter, the promise is what matters. And currently businesses largely believe that promise, which is why the MSFT/Azure connection is indeed the actual "moat" of OpenAI.
In what way is this not true? Obviously there is no perfection here, only degrees of risk. But this is literally why people pick MSFT over others. They have by far the strongest culture around maintaining trust in the enterprise space.
Increasingly robust local LLMs as models get more efficient and capable and run on weaker hardware, or hardware catches up? Here to stay.
Connectionist, maybe gradient trained, AI seems here to stay, but no doubt future systems will be more brain-like in terms of capability, and not at all obvious how much of this simplistic pre-trained transformer approach will be retained.
The winner might not be something called ChatGPT though.
The AutoGPT stuff is pretty ridiculous...
- ask GPT for the steps to perform a task
- then for each task ask GPT, hey you're good at coding, can you code this? or if not can you break it down into more detailed steps? and add those to the queue
as GPT gets smarter this type of agent workflow seems like it might work quite well for a lot of high-level tasks.
some businesses might incorporate chatGPT and some new businesses might form. as the technology (chatGPT) advances we might find ways around it's ability to "confidently lie" in subtle ways.
i've had a bad experience with it generating code and config, it's wrong of inefficient in everything it does.
it's kind of like a glorified search engine, it gets you only the information you need. It can generate examples too.
Chatgpt has many uses.