ai + startup causal loops: hardware, solution to specific problems, automation

[hyunjimoon]

From comment below on startups & ai, 🎯 Layer 1 (Hardware) corresponds to "method capa", 2(Solutions to Specific Problems) corresponds to "candidate" stock as a combination of "solution capa" and "need capa". I wonder how Layer 3 (Growth in Process Automation ) can be modeled. what's missing is large companies' role in establishing infrastructure (e.g. google, nvidia, tesla) as potential win-win game between startup and big tech was alluded from Murli and Toni's comment.

animation version:

Murli Ravi's comment and Tony's comment from linkedin on the subject line
Major tech players are already leading the charge with billions in resources. How can startups weigh in? Some thoughts:

AI has many layers.
Understanding the dynamics of these layers and focusing is how startups can find an edge.
🎯 Layer 1: Hardware
This is the all-important layer: the expertise of large long-established specialists.
On top of this run core models processing language, images, or large statistical data.
Most of these models need lots of data and computing power, which are unimaginably expensive because of chip shortages. This naturally means that large companies are favoured, like NVIDIA.

🎯 Layer 2: Solutions to Specific Problems
Here is where startups are much better positioned: using these models to solve niche issues.
A "solution" is not merely a technical idea.
For instance, a hospital would never buy an LLM chatbot to help with diagnostics;
Rather, they need a full-featured solution that covers specific technical and non-technical needs, such as medical data, regulatory compliance, service for time-starved hospital staff, and more.

🎯 Layer 3: Growth in Process Automation
The focus so far has been on AI that produces answers to questions.
It is then up to a human to take its output and make use of it.
Increasingly, it will not be enough for an AI to produce an answer; it will need to actually execute tasks independently, under human supervision.
This is already prevalent in some domains, such as automated trading in financial markets, and I expect it to proliferate into other areas.

One last thing: Startups that are focused and nimble can make headway into these new areas more easily.
Meanwhile, the giants will benefit from startups utilising their core infrastructure.
Be creative in value creation if you lack the resources.

Toni's followup comment
Startups are much better positioned to get product market fit in a niche, customise tech from large companies to fit a narrow user market.
The more narrow the market, the harder it is for generalised solutions large companies have to deliver on.
Education and onboarding for non tech savvy demographics is another huge market opportunity.
The best part for startups is there is barely any upper limit to niching down. At its limit, AI can be customised down to 1 person.
Then finding more people to scale to is the next step, after winning in that narrow niche. Build for 1, but on things that scale.