Cognition Labs Seeks $2B Valuation
wsj.com
wsj.com
Mirror that I should've posted instead https://www.msn.com/en-us/money/companies/a-peter-thiel-back...
Yes. The key is 'if' and that is the hidden risk when training a model which can overfit or under-perform.
That cost is in the tens of millions and they (Cognition Labs) better make some revenue to cancel all of this out.
It sounds a bit like Cognition labs can afford to take a few losing bets costing tens of millions (based on $2bn valuation) but investors seem to be speculating that they'll find something which works in the end.
“In a presentation earlier this month, the venture-capital firm Sequoia estimated that the AI industry spent $50 billion on the Nvidia chips used to train advanced AI models last year, but brought in only $3 billion in revenue.”
In seriousness, the upside with code assistants is increasingly clear. And may be the bet that these would continue to improve quadratically is worth taking if you're a VC. In fact, PG argues that buying into hype cycles is the default: https://paulgraham.com/herd.html
So far, the utility on existing codebases is less than zero.
> continue to improve quadratically
Using what metric?
I'm curious what definition of "Utility" you are going by here - at the most basic level of "smarter, more context-aware autocomplete", does it have zero or negative value to a developer? Would you disagree that even an experienced developer could save some amount of time, at the current state of the technology, even if only at the stages where you're just writing code you already have a pretty clear concept of in your head?
Unfortunately that's not how things work under capitalism. To the extent that coding assistants make a difference in productivity (in my experience quite limited gains), that will just mean that you get more work done for the same time and salary.
Once we get free models that are as good as GPT4, while there will still be a market for a “GPT5”, the current applications will experience serious price competition.
If I can fulfill my objectives with a commodity model with GPT-4 capabilities then I’m not going to pay any $$$ for GPT-5 because my needs are already met. So OpenAI will lose customers at the same time as gaining them.
The price at the top end will go up, but the price at the bottom end (which is currently the top end) will fall.
No one is going to pay $100 for “better than Google search” chatGPT, even if its AGI tier
But paying $100 for a “junior dev who takes over all the grunt work” is still a value buy
Not sure this is the phrase you meant as no one is accusing Microsoft of laundering money via OpenAI.
My understanding is that the APIs are profitable, and the best alternative (spin up mixtral on cloud provider) isn't that expensive. So there's some downward pressure there.
I expect ChatGPT is priced right where anthropic and OpenAI want. It's generating cash, and if you're a heavy user they can rate limit or offer a higher tier
Is that higher tier what you're expecting?
Even if we forecast this, Devin is using GPT-4. That is going to be very expensive for Cognition Labs for the number of API calls / credits they are doing and it will be a while for them to roll their own model to surpass GPT-4 for Devin to 'replace' developers.
Otherwise, this tells us that we are at the very peak of another VC fuelled AI bubble which will end with lots of losers being unable to generate meaningful revenue and the VCs will push and run this startup into the ground.
Looked this up and yeah, that’s insane. Their share price per user is above $1,000 while Reddit is $150, X is $80. Crazy stuff!
But AGI
https://cc.bingj.com/cache.aspx?d=230782937255&w=NYG4LSGzXMn...
In the past 40 years we went from:
1. PCs (real productivity gain) as now all every home and office could afford a computer running important things like spreadsheets and word processors.
To
2. The world wide web. Huge economic and productivity gain from making information acquisition and communication cheaper and more readily available. The apogee of the world wide web was probably in the early 2000s. Google search worked. You could find what you were looking for. eBay and Amazon worked.
To accelerating bullshit based on advertising.
3. Facebook, Google SEO spam, Twitter Reddit. Increasing programing frameworks all of negligibly marginal productivity and economic value.
4. Crypto currency. For some governments world wide turned a blind eye to the creation of private tokens whose only real use cases are speculation (gambling, distributed ponzi), money laundering and or other black and grey market activities of negligible or negative real economic value.
To now 5. "AI" trained on Reddit posts, Wiki articles, stack posts, and Google SEO blog spam. "AI" is basically a glorified, hallucinating chat bot at this stage. Nothing like real intelligence or capable of producing trustworthy reliable output at par with a human expert or a Google search from 20 years ago, before the degradation.
The sigmoid curve strikes again.
Silicon Valley in bringing the world microprocessors, PCs, the commerical WWW. Since then the ratio of bullshit to real innovation has increased at an accelerating rate. Eventually there will be a profound economic.restructuring of the area more in line with reality.
The only reason why it hasn't happened yet since 2009 to present when bullshit proliferated was low interest rates and too much money chasing gains with delusional business propositions.
But as things are right now, the only AI companies with any serious impact are the very companies that train and release the huge and extremely expensive models.
Everyone else seem to be layers on top of those products.
I'm getting immediate flashbacks to the era of "Google killed my startup/product", where google could simply either acquire companies, or implement their own solutions which essentially killed small competition.
I head over to their website: https://www.cognition-labs.com/
It's just a landing page. Some information about Devin, their main (only?) product:
https://www.cognition-labs.com/introducing-devin
Have they trained their own network? Is it a fine-tuned version of something else? Is it just some interface against another LLM?
Is it just some startup that's waiting to get acquired, based on hype and valuation alone?
So many questions.
EDIT: The article is paywalled, so can't read it.
Whipping up some MVP product/functionality and interface where the LLMs do all the heavy lifting is a weekend project.
The only gatekeeper seems to be the cost of eventual pre-training, and the LLM queries / API calls themselves.
But seriously, most of that stuff is completely useless for anything but machine learning tasks, which you'll find some hobbyists for, but for all that stuff that is floating around right now? As a hobbyist you need one GPU.
https://apnews.com/article/openai-voice-engine-aigenerated-c...
I expect this to set a floor on the crash.
We will continue to get better and better predictions at more affordable prices.
1. It took about 50 minutes before a link to a non-paywalled version of the story was posted so many early commentators were going just on the title, which normally works pretty well but can fail badly when the submitter does a poor job of picking a title.
2. The submitted title was "WSJ: AI industry spent 17x more on Nvidia chips than it brought in in revenue".
In the corporate world we had CIOs and Operations leaders naively but legitimately making decisions based on their belief Watson was going to replace service workers. Now it’s OpenAI, Copilot or Generative AI broadly.
Yet what the corporate world has to show for all the hype is the same administrative burden and many failed experiments, POCs and strategic projects. Oh and a new generation of believers and snake oil sales teams.