We are Early in AI
Tech is very early in its AI journey. As a comparison, I remember when I worked at Chainalysis in 2017. Bitcoin had reached an all-time high of almost $20,000. The companies I worked with were notable, well known brands. I thought, “This might be the top!” And for some time, it was.
Then it quietly crept back up. It recently crossed $100,000 and no one batted an eye. Because bitcoin, similar to AI, and similar to other phenomenons before it, went from outside interest to mainstream technology. Companies like Meta are paying millions for AI talent. The reasoning is simple: the road ahead is long, and there’s still a lot of upside. Also like crypto there is a lot of noise in the signal. This means sticking to fundamentals is important. It’s no surprise that about half of the engineers that Meta poached worked at OpenAI.
There are a lot of unsolved problems. Most enterprises are just using AI as a chatbot solution. Most patient intake is still done manually. Most meetings now have AI notetaking, but not AI summarization. There are still a lot of areas where AI can be leveraged to reduce friction.
We also do not have the right skillsets yet. Many organizations are downsizing other parts of their business to focus on AI-skilled talent. This misses the opportunity to leverage SME knowledge and upskill workers on the potential of AI. There are a number of functions that, augmented with AI, can work much more efficiently.
Models are also getting larger. While this is great for more complex tasks, focused and specialized tasks that need narrower scope are not well supported by these models. This means that to support the full range of tasks and subtasks, we will need smaller and faster models to not just execute but also to guardrail these executors.
Fortunately, there is also the appetite to get these things done. While the current AI wave started as a consumer product with ChatGPT, it is now largely being driven by enterprises. Enterprises stand to benefit greatly from the improved efficiencies from AI, and therefore are spending a lot of money on it. This means that AI is now being heavily driven by these interests and therefore we should see a number of powerful innovations coming to continue to fascilitate growth in AI.