I think it is more complex than the author thinks.
There are clearly defensible aspects for ai startups. Specifically I think these are:
a) in-context and collaborative features (since working alone with ai through a chat box is unlikely the only way we will interact)
b) gated knowledge/data (since commonly available technology can be leveraged with unique data)
c) edge computing and offline usecases won't be the center piece for many classical companies and therefore can be very well exploited.
Doesn't (a) fall into the bucket of UI, i.e., something that can be easily copied?
Agreed on (b) - I think this is anyone's best shot at a moat.
Curious to see how (c) evolves. It's unclear to me whether the future of these things are running locally or whether we'll all continue hitting remote APIs
Think of the difference of using a single-user application to e.g. make mockups for websites or a collaborative environment like figma, in which you happen to also be able to have AI collaborate with you. Very different usecases and solving collaboration workflows, etc. is non trivial.
I guess for (c) both things will exist.
Local will be done for 2 reasons:
- data sovereignty (e.g. companies wanting to have applications that are purely trained/fine tuned on their own data; but that improvement is not shared)
- privacy (anything from an AI having access to all your email and calendar up to having intimate "friendships" with AI)
I think various of those aspects you call out here, I do as well. The specificity of the application is fairly key, whether it comes through proprietary data or application-specific stuff or simply business-lock-in.
Interesting hill analogy—I do broadly agree with the areas.
However, to your last point:
You are saying that nuclear power plants then are insured to risk.
Can you point me in your sources to the place where it is shown how nuclear power
plant operators today then are insuring "to risk" the risk of nuclear disaster and all costs and risks of the disposal?
Not OP but I do think you have a false equivalence here.
Coal plants don't cover the costs and risk associated with the disposal of their _nuclear_ waste as well as their CO2 waste. They just vent it out into the atmosphere.
Not a hardware startup per-se, but we are ex-satellite engineers who out if frustration with available tooling have developed the engineering software every hardware engineer deserves: https://www.valispace.com
Oh sweet, hey Valispace. I can vouch for this software as a spacecraft integrator. I worked with it a few years back as part of a trial and the power of inputing real world hardware measurements of equipment back into a system and see the impact of the measurements system wide is very powerful.
While it is aimed at the spacecraft industry, I can see any industry with concurrent engineering taking advantage of this sort of system.
I have found this software to be very unintuitive with bad UI/UX, unfortunately. The utility of the software is good amd it fills a vital gap for concurrent engineering, but UI/UX needs to be improved badly.
We're taking a shot at hardware engineering: www.valispace.com/ai/
And I see similar things happening for major fields such as education, law, art, software development, management, etc.
Here I looked into a few examples of what is happening in these fields already: https://assistedeverything.substack.com/p/the-age-of-assiste...
For 6 years I had a long distance relationship between Peru and Germany.
When you are in different timezones it can actually be nice to fall asleep with the other person "close" to you; so we kept Skype running while one of us went to bed and the other person was working on the PC.
Unfortunately the internet connection would regularly drop, ending the Skype call. Now you did not want to wake the other person by calling them.
So I wrote a small script that would allow you to send a secret word in the chat and invoke the other persons' Skype to call you instead automatically.
Kept our relationship healthy. Now we've been married for nearly 10 years and are happily living together :)
Love this. This is exactly what coding is about for me; improving your life and the life of others by solving some problem, how miniscule it might seem.
MS Live Messenger used to have a feature where you could set certain contacts to auto-accept. When my SO and I were doing the long distance thing, that was our lifeline.
Did the same with Google Hangouts which wouldn't end the call if someone dropped, and when their internet came back up they would automatically be back in the call. I think that's how it worked.
- allowing engineers to develop their products much faster (especially doing good requirements engineering for now)
- allowing us to demo to/onboard users with data from their specific usecase (prepopulate their trial account)
Hardware engineering at first does not seem like an obvious choice for LLMs, but I think that it will be those vertical solutions that will still surprise us all the most.
I found it interesting that there start to be best-practices for professors and teachers on how to deal with ChatGPT in a class room [1].
One I really liked a lot:
>And if you don't trust the AI to output correct content, teach your students critical thinking by using Assisted Teaching to write history essays and let your students find mistakes in it.
Another way (if you want to go the more traditional way), is the Flipped Classroom [2] method, in which students self-teach at home and do the "homework" in the classroom.
I agree with others here that there is no way to ban these technologies; there are just better and worse ways to deal with it.
In my opinion what we are seeing is the age of "Assisted Everything" [1].
I am very certain that in 2 years' time no white-collar job will be performed anymore alone in front of a PC. You will always collaborate with an assistant.
Personally this makes me super excited. As a Satellite Engineer, I always wanted to have a Jarvis-like experience and was super disappointed, when I found that the hardware engineering world was only paper-pushing excel files.
Finally the future is here and engineers can focus on solving problems instead of on tasks that can be automated.
There are clearly defensible aspects for ai startups. Specifically I think these are: a) in-context and collaborative features (since working alone with ai through a chat box is unlikely the only way we will interact) b) gated knowledge/data (since commonly available technology can be leveraged with unique data) c) edge computing and offline usecases won't be the center piece for many classical companies and therefore can be very well exploited.
I wrote up a framework to assess LLM powered Startups/Ideas here: https://assistedeverything.substack.com/p/the-three-hills-mo...