Phrases qui font monter mon niveau d’intérêt pendant des présentations de startups :
- on a testé ...
- on a compris que ...
- on a découvert que ...
- contrairement à ce qu’on croyait ...
Au contraire de :
- on est convaincu que ...
- on a la certitude de ...
- on sait que ...
Les coupes tech d'avril ont un profil précis : le middle manager. Pas l'ingénieur. Pas le commercial. Celui dont le métier consiste à forwarder de l'information entre équipes, consolider des rapports, et servir de courroie de transmission entre la direction et l'exécution.
@StephaneMallard l'écrit depuis des années : le manager disparaît quand l'information circule librement.
L'IA vient de rendre cette fonction réelle pour vingt dollars par mois.
Good Products are Opinionated.
“Every great founder I’ve seen up close, or even from afar, is highly opinionated and they’re almost dictatorial in how they run things.
Also, early-stage teams are opinionated. And the products they build are opinionated. Opinionated means they have a strong vision for what it should and should not do.
If you don’t have a strong vision of what it should and should not do, then you end up with a giant mess of competing features.
@Jack Dorsey has a great phrase: “Limit the number of details and make every detail perfect.” And that’s especially important in consumer products. You have to be extremely opinionated. All the best products in consumer-land get there through simplicity.
You could argue the recent success of ChatGPT and similar AI chatbots is because they’re even simpler than Google.
Google looked like the simplest product you could possibly build. It was just a box. But even that box had limitations in what you could do.
You were trained not to talk to it conversationally. You would enter keywords and you had to be careful with those keywords. You couldn’t just ask a question outright and get a sensible answer. It wouldn’t do proper synonym matching, and then it would spit you back a whole bunch of results. That was complicated. You’d have to sift through and figure out which ones were ads, which ones were real, were they sorted correctly, and then you’d have to click through and read it.
ChatGPT and the chatbot simplified that even further. You just talk to it like a human—use your voice or you type and it gives you back a straight answer.
It might not always be right, but it’s good enough, and it gives you back a straight answer in text or voice or images or whatever you prefer.
So it simplifies what we looked at as the simplest product on the Internet, which was formerly Google, and makes it even simpler. And you just cannot make a product that’s simple enough.
To be simple, you have to be extremely opinionated. You have to remove everything that doesn’t match your opinion of what the product should be doing. You have to meticulously remove every single click, every single extra button, every single setting.
In fact, things in the settings menu are an indication that you’ve abdicated your responsibility to the user. Choices for the user are an abdication of your responsibility. Maybe for legal or important reasons, you can have a few of these, but you should struggle and resist against every single choice the user has to make.
In the age of TikTok and ChatGPT, that’s more obvious than ever. People don’t want to make choices. They don’t want the cognitive load. They want you to figure out what the right defaults are and what they should be doing and looking at, and they want you to present it to them.”
My AI investment thesis is that every AI application startup is likely to be crushed by rapid expansion of the foundational model providers.
App functionality will be added to the foundational models' offerings, because the big players aren't slow incumbents (it is wrong to apply the analogy of "fast startup, slow incumbent" here), they are just big. Far more so than with any other prior new technology, there is a massive and fast-moving wave that obsoletes every new app almost as fast as it can be invented. There is almost no time to build a company and scale it.
There are two ways AI application startup founders can make money:
- Make a flash-in-the-pan app that generates a ton of cash and bank the cash (my estimate is that you have about 12-18 months cashflow generation)
- Make a good enough app that you get acquired by one of the big players for sufficient equity
The situation is highly unstable - we don't know if it's going to crash or go to the moon but both scenarios make it very unlikely that any AI application startup will independently become a generational supercompany (baseline odds are low to begin with).
The best odds are finding an application niche in a highly specialized field with extremely unique and specific data barriers, ideally ones relating to real atoms (hardware or world-related) data and not software/finance.