Hi everyone,
First off, a massive thank you to this community. Thanks to your support, Future Proof officially hit #1 Best Seller across multiple Amazon categories!
If you haven’t picked up your copy yet, here’s the Amazon link: amazon.com/dp/B0GPDFPWD6
I’m incredibly grateful that the message is resonating. I truly believe that the leaders who “paddle early” are the ones who will own the next decade. Speaking of paddling, I wanted to share a story about my friend Josh Blanchfield that reads like a live case study of the frameworks in my book.
Josh just launched his first ETF (ticker: AVOS): a high-conviction, active global equity portfolio. Normally, a launch like this is a “hiring trigger.” You’d expect to see a job board full of openings for junior analysts to dig through SEC filings and scrape pricing data. Josh had the budget and plan to do exactly that.
But instead of growing his headcount, he decided to “AI-ify” his entire research process. He just wrote a brilliant Substack piece about the research team he never hired. It’s a masterclass in the exact concepts we talk about in Future Proof:
- Positioning for the wave: Josh didn’t just jump at the first shiny tool. He kept testing AI capabilities until they were mature enough to handle complex research. (Chapter 7: Building Your AI Roadmap)
- The AI as a “thought partner”: Josh built an agentic system specifically designed to challenge his assumptions and debate his hypotheses. Humans are often too polite to tell the boss an idea sucks, but Josh’s AI is a tireless sparring partner that makes him sharper. (Chapter 4: Incremental AI)
- Human in the loop: Despite building an autonomous system, Josh used the “Trust but Verify” model, ensuring he remains the final “captain” in the cockpit. (Chapter 9: Ensuring Responsible AI Adoption)
- The copilot effect: By offloading the “drudge work,” Josh recaptured his time for high-value activities. (Chapter 10: Jobs, AI, and the Future of Work)
The net-net? He didn’t hire the team. He avoided the overhead and management headaches, allowing him to make better decisions at a velocity a human-only team simply couldn’t match.
…If you’re still looking to solve your scaling problems by just throwing more “junior human hours” at them, you’re playing an old game. Josh is playing the new one.
You can check out his full breakdown (also copied below): The Research Team We Never Hired.
Give him a like and feel free to comment! And if you want to see the output of this AI-powered process in action, keep an eye on AVOS.
Best,
Dr. Michael “House” Housman
The Research Team We Never Hired
By Josh Blanchfield
On Friday we launched our first ETF, ticker AVOS. It is the highest-conviction, long-only, active global equity portfolio we can build. More info at avosglobalequities.com.
Last week I made three claims about the conflict in Iran, prefaced by the disclaimer that “it could age like milk.” Claim #1 and #2 have already curdled. Luckily for me (and our investors), claim #3 was that almost every trader should sit this one out, since “in a scenario where I highly doubt that even the central participants know what they are going to do, it’s a little hard to imagine anyone having a real edge.” In our commodity fund we hedged away as much of our exposure to the war as was practical, and we are up a bit for the month so far.
So this week I decided to take a breather from being wrong about the war, and instead share our firm’s evolution on AI. I share it to pull back the curtain on what we are doing as fiduciaries, but also (hopefully) to be helpful to you. With the exception of James Wang, I suspect no one reading this is using AI nearly as effectively as they could be, in their business or in their life.
Last summer, we raised some GP capital from a few existing clients and showed them a buildout plan. Multiple research hires at different seniority levels, and all expensive. We planned to scale our investment team the conventional way.
We never made those hires.
By October, AI research capabilities were such that I went on stage at a conference and said I was struggling to see why we would hire any junior investment people. That month I wrote a piece where I went back through every Chinese five-year plan and examined whether they did what they said they were going to do. AI did the immense grunt work of parsing all the plans, separating out testable promises, and then checking what happened. That would have been a junior person’s job for a couple weeks; it took me a few hours.
The improvements continued to compound. Earlier this year we built our first AI-powered system designed to be a peer to me and our team in trading one of our most important markets. We fed it raw data and what we believed were our edges. It then did three things:
- It pressure-tested our assumptions. It came back and told us where our edges were real and where we were kidding ourselves. That kind of honest audit is nearly impossible to get from an employee trying not to offend their boss.
- It proposed new ideas. Ideas we had missed or were blind to because of our biases.
- It freed us up to do other things. It scans markets and news flow on a regular schedule, checking against our framework and flagging anomalies.
I want to be clear: I don’t trust the system enough to give it trading permissions, and I may never. Any output must be checked before using. But it’s getting hard to argue that any single human on our team has a better handle on that market than the system does.
We are now rolling this out across every market we trade. None of the researchers we thought we needed are getting hired. Some of the cost savings we’re keeping as a buffer against the inevitable AI price hikes. Some accrues to our shareholders. And the compounding improvement in our research accrues to our clients, including, as of Friday, anyone who buys the AVOS ETF.