Multiple Stability AI researchers are departing, CEO says
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2. Identify a demand in the market.
3. Quit the lab/startup, launch your own startup.
4. Get acquired by some established AI company or FAANG tech megacorp.
5. Retire.
It's a gold rush, get in now.
That’s not totally true, but we’re all here trying to outsmart one another in order to have a Sure Thing. Well I’m here to tell you that there are no Sure Things in life except for death and taxes, so take a risk, jump on a random plot of land, and start digging.
(What am I doing to do when AI isn't just at the level of "intern"?)
It acts on the same dynamics companies act on. People build reputations by doing this with far less effort than it would take otherwise.
The problem is that it’s definitely a lot easier to just cruise along, punch 40 hours, and leave work at work. Startup founder life is rough. I don’t blame anyone for not wanting to extract that worth from themselves.
Not always true but it only needs to happen once to you for your 40 hour cruising to end. At that point, you will question why you spent so much time slaving away.
My daughter broke her leg just over a month ago. I have decent insurance and my out of pocket expenses were still close to $2000 alone with in network coverage. I think a lot of HNers are somewhat wealthy middle class+ with cash in the bank, or not from the US so they have no clue how out of control these kinds of expenses are.
2. Retire
Probably similar odds.
#5 will almost certainly happen even if you keep doing #1, earning a huge salary.
The numbers and risks just don’t make sense anymore.
...because there's a glut of investor money chasing "AI", raising money for your new AI startup is not particularly hard if you are linked with any of the big names in AI.
Even if the startup "fail", the founder could have been paying themselves a market-rate salary with the added possibility of an FU-money exit. The downsides are limited.
I've seen several startup founders pay themselves below market for years because they have all that equity. Only to find out, a couple down rounds later, it wasn't worth it. They tried though.
For me, it's the first step. It seems once you get started, everything else tends to fall into place more smoothly.
I'm eager to know others' perspectives.
So like in the case of Inflection.ai, Microsoft just did an acquihire of the entire team and “licensed” the technology/IP from what now is a shell of a company.
I think it's very valuable for the industry, and as long as the teams who are picking up these employees are able to utilize them effectively, will result in broader innovation.
Largely, because of salary caps created fairly explicitly to break the winning -> popularity -> money -> monopolizing talent -> winning positive feedback loop.
Success feedback loops aren't quite so constrained in most other industries.
Were there instances where a sports club actually didn't want to win some title to prevent exactly this from happening? How would the sports association react if the club decided to throw the match for this reason?
https://en.m.wikipedia.org/wiki/Tanking_(sports)
And of course when kids don’t want to play during the summer /s
It also goes against the idea that programmers are replaceable cogs that management can change out as and when they want to.
Which isn't what 'asset' means, but it is the central meaning; using it on people always felt squicky to me.
I guess one could argue it's a management issue that Sam Altman figured out a way to structure his company to make that possible.
While true for existing IP for industries like pharma, for industries like AI, your IP isn’t worth anything unless it keeps evolving. If you lose your key researchers, you’re done.
Personally, I assume stability has hit a scaling/money issue on image generation models, which is why they might be pivoting to other domains where you can still make headlines spending <$50M on training runs, like their 3D models.
(I'd further argue that the number of actual "AI researchers" that actually contribute enough to justify their mega-million comp packages is small indeed... but that's a separate topic)
If you are hearing about it, and you aren't already in, it might be too late.
That having been said, it's a prime time to have project that you can bootstrap into a startup. You dont need to be google, you need to be google, small teams can clear a few mill a year with the right product and not get much larger.
[0] https://www.forbes.com/sites/iainmartin/2024/03/20/key-stabl...
Some more discussion on Forbes article here:
I'm grateful for it, and I think they should have government funding if necessary -- the upsides are huge, just little of it goes to Stability -- but it's easy to see why investors would be wary.
I personally think they're missing some low hanging fruit, though I suppose it might be in the name of "safety." I believe Stability (maybe Clipdrop?) at least did have some sort of paid Lora or other training, but I tried it and it was awful. Considering they made the models surely they would have the absolute best insight into full fine tuning it and could roll out a service to do so.
I think they talked about doing it B2B at a presumably much higher cost, but a consumer facing, easy to use way of doing it would at least pull in some money. Of course, the second someone uses that to train child porn or whatever they'll be in hot water.
0. https://websd.mlc.ai/#text-to-image-generation-demo
It's readily apparent that any sufficiently useful AI/CV/ML should be deployed to solve specific, immediate, niche problems of individual people that they're willing to pay small amounts of money or assign referral commissions for:
- Product personal shopper-recommender goes out and crawls reviews and pricing to find what and where someone should buy something based on a prompt.
- Where to live.
- Where to go out.
- What to have for dinner.
- Find a plumber, carpenter, etc. and do open source due-diligence on them.
- How to optimally invest money based on circumstances, assumptions, and speculative outcome distribution.
- How to redecorate a room.
In a business context, there are many classes of problems AI can semi-automate including:
- Decision support
- Feature prioritization based on support data and social media sentiment
- Supply (inventory) and demand forecasting
- Pricing optimization
This is the second time this has been referenced on the front page and I still don't know who they're talking about or what the details are (and I'm not about to give money to forbes, of all people.)
The CEO of Stability seems to be rather controversial person at the helm of a rather controversial company. I'm honestly surprised more people haven't left the company by now, unless they had some absurd incentives to stick around.