Will we stop solving for the AI “problem?”

Can we all agree it’s not magical fairy dust?

I’ve written before about how the hype around AI seems to be cooling off, and we are entering into what I’ve dubbed AI fall. As a person who follows the seasons and tries to take some cues from nature, fall as a time to reap the benefits of your hard work of the summer before settling in for a winter of benefitting from the abundance that you have created for yourself.

The problem is when it comes to AI there are so many who are not going to reap or benefit. Even the biggest companies are operating at massive losses. Smaller companies are folding, or frantically trying to do additional raises. And we now know that 95% of AI initiatives in business have failed. What was assumed to be a massive year for profitability and productivity is now being seen as the bursting of another tech bubble.

Does this mean that AI has failed? I would argue no, and that there are a lot of places where there have been successes. I also think that the best of AI is yet to come.

There are both large and small companies out there taking their time to bring AI tools to market, and I believe that when they do, these will be smarter, have a better user experience, and be more effectively focused on solving real world problems. I’ve had the good fortune to work with the number of founders that I would place into this bucket, and I’m telling you right now, when they get out there, they will be making people’s lives better.

One of the things I hope we do as an industry is to learn from some of the things we’ve seen that didn’t work. From where I sit, one of the biggest issues was panic development of AI powered products across industries. Entrepreneurs and founders went out there thinking “how can I bring AI to this market?” The focus was on AI as a capability and how it was obviously going to bring value to everything.

That’s just not the case, and it’s not how great products are built. Great products are built by solving problems.

That’s this jumped the proverbial rails. If the problem you think you’re solving for is that AI is not in a specific market or industry, you haven’t validated whether or not people actually want or need to use it. You haven’t taken a look to see whether or not AI is actually the best solution for a problem, if you have even identified one. This is where the products that were slower to come to market seem to have done a better job. Companies took a look at the actual problems. Their users had, and determined that AI was the best way to either solve them, or improve the solution.

My LinkedIn feed was packed this year with smaller companies, touting AI and making bold claims that they were going to take down massive companies like Figma. Then at Config the Figma PMs marched out one by one announcing a suite of AI powered tools that we’re going to augment what they already offer. The bold claims in my feed changed from how Figma was going to be taken down, to how it had killed a number of smaller startups.

Now mind you, I understand that a company like Figma has the luxury of being able to invest in AI tools over time, as well as the competitive advantage of the fact that designers are already using their tools on a daily basis. I’m not saying that smaller companies shouldn’t try and compete with them. I imagine that the tools that Figma has released and are refining are going to be better because there are smaller companies nipping at their heels. I also don’t think they are all necessary, and some will probably be scrapped. What’s good here is we don’t want the 500 pound gorillas in the room to think no one is going to challenge where they sleep, and those smaller companies were doing just that.

But when you choose to go to market simply infusing AI into something that already exists, you’d better be sure that AI is a truly important differentiator. You need to make sure that AI is necessary for the tool you are trying to build. And you need to make sure that people want it. I don’t think a lot of founders, did either of these things. When you look at a public perception of AI, it is clear that not everybody is interested in having it in the things they use. When it comes to tech, there is a love-hate relationship with it. It presents tremendous potential, while also threatening people and their livelihoods. When you look at the quickly declining adoption rates for tools like Lovable or V0, it’s not hard to wonder if the critical differentiator isn’t AI but something else. Or even that there isn’t a critical differentiator at all.

I do wonder what would’ve happened if the founders that took the pathway of AI-industry-solution instead took a pathway of problem-solution-AI. What would’ve happened if they had honed in on the problems that users had with some of these existing products and experiences, thought through the solution, and determined that AI was the best thing for the job?

I think a lot of companies would have decided AI was not in fact the way to go.

This means that there would’ve been fewer products on the market. It would mean that there are fewer founders that got massive investments and valuations. I’m not going to pretend that those things don’t drive a lot of the market. It’s not necessarily about innovation, but about looking good and making a lot of money. The realist in me understand that those things are greater incentives than following effective process, and I know that we all have different definitions of what “effective” is in the first place.

However, I do think there are many things that would have been significantly better in tech if there had just been a pause and a determination prior to pushing straight into AI. You just need to take a look at the jobs market to see the damage. Again, referencing my LinkedIn feed, I see the pain and frustration of this daily. At this point, the layoffs aren’t just because people are being seen as expendable because AI products are going to fill the gap. It’s also because companies have gone in so hard on AI that they need to cut ranks as their investments do not return the proper dividends.

So what can we learn from this?

Companies large and small need to focus on user and problem-centric solutions. This may mean that AI is not a part of the equation. It also may mean that AI is part of the equation, and that using it as part of the solution is truly a differentiator. We’ve already seen this bringing us some measure of success, and I think that those who have been doing this are going to bring truly innovative and effective products market.

We can’t forget to go upstream before we hit the rapids. My hope is that as we enter AI winter that is a lesson we learn.