Depending on which viral AI essay you agreed with on X this week, it’s already so over or we’re all playing a very expensive game of Farmville.
As with most things, the real answer is a lot more unsatisfying. It’s all still messy and probably lies somewhere in the middle.
While it does feel like things are shifting extremely quickly in a way that’s hard to dismiss, the hype is escalating every day. It’s impossible to tell where exactly we are on the curve.
What has been more interesting to me than the debate over which camp folks are in is just how much traction these pieces are getting. I think we’re all kind of desperate to make sense of how uneven things are, and are all caught up in this moment of trying to process the whiplash.
You’ll be scrolling through crazy OpenClaw workflows and mishaps that truly feel like scenes out of the future, but then skepticism starts to creep in about what’s true and what’s overengineered.
And then anytime you step outside the tech bubble you can get an instant reality check on how much wood there still is to chop to get to widespread adoption. (Even if it’s just seeing how differently the ChatGPT vs. Anthropic vs. Google Super Bowl AI ads landed for tech folks vs. normies.)
The gap between what people are posting about and what’s actually making a difference has never felt harder to figure out.
The main thing I’ve found to be helpful as a counterweight is trying to stay diligent on spending my time as an investor where I think it matters most vs. getting caught up in the wild swings. For the past few years now, that’s meant meeting with builders who are focused on solving hard, real-world problems.
To me that means taking on the biggest, most technically and operationally challenging issues in the physical world (aerospace, defense, robotics), or figuring out how to usefully inject AI into the painful, manual workflows in industries that actually make and move things.
Not only because this is what personally gets me more fired up but also because it feels like this is the general direction our world is headed in. More and more of the founders I’m meeting are reorienting their approach to problem hunting and building in this way.
@DideroAI is a great example here and is top of mind because they just shared their $30M Series A news today from
@chemistry and
@HeadlineVC.
They found a problem space most of us know virtually nothing about. Procurement teams at manufacturers and distributors are managing thousands of supplier interactions across email, spreadsheets and ERPs. The bulk of this stuff is still done manually, even though it’s only getting more and more complicated as companies try to diversify their supply chains across more countries.
When I led their seed in 2024,
@tspencer15,
@tcpetit, and
@LPallhuber immediately felt like a team that was well-configured to the shape of the challenge here. They’ve been those people stuck in the mud of these workflows. (Between the 3 of them they’ve built companies, run procurement operations across countries, and dealt with the pain of trying to coordinate 100s of suppliers.)
Spaces like these, where the team has the deep domain context and the agents are correctly applied, are where AI can and already is making a near-term, not-theoretical difference.
In took less than a couple years, and Didero’s agents are now doing things like following up on 2K past due order line items that were sitting neglected, automating PO creation, and pulling info off physical bills of lading and getting it into the ERP so production doesn’t stall out waiting on someone to manually type it in.
None of those are paradigm-shifting on their own, but stack up these small, boring, concrete wins week after week, company to company (and assume they continue to improve in capability at an explosive rate), and pretty soon we’ll all be surprised by just how much wood has been chopped.