How AI Is Changing Investing— with David Trainer
Episode
52 min
Read time
2 min
Topics
Relationships, Investing, Startups
AI-Generated Summary
Key Takeaways
- ✓Walled Garden AI Architecture: General-purpose AI tools like Claude or Gemini produce unreliable stock picks because they draw from unverified internet data. Domain-specific agents that restrict inputs to audited, validated datasets produce deterministic, trustworthy outputs. Investors evaluating AI tools should ask explicitly whether the underlying data is sourced directly from filings or scraped broadly from the web.
- ✓Data Integrity Standard: A dataset that is 99% accurate but lacks identification of which 1% is flawed is functionally unreliable for financial decisions. New Constructs validates every data point against original SEC filings with full audit trails — a standard confirmed when Harvard Business School reviewed 350 companies and found zero errors across all adjustments.
- ✓Core Earnings Edge Signal: New Constructs calculates a "core earnings edge" metric — core earnings minus net income, divided by total assets — to rank stocks by earnings quality. A Bloomberg index built on this signal returned 27% in the most recent year versus 18% for the S&P 500, and a "very attractive stocks" index has outperformed the S&P by 30–40 percentage points over five years.
- ✓Reverse DCF for Valuation: New Constructs uses reverse discounted cash flow modeling to determine what future profit growth a stock's current price implies. When NVIDIA's price implied a permanent 50% profit decline, the firm flagged it as a long idea. Investors can apply this framework by asking: what does the current stock price require the business to do, and is that realistic?
- ✓Agentic AI Workflow: Reliable AI at scale requires stacking multiple narrow, domain-verified agents rather than relying on one general model. Each agent must be built on a 100% auditable dataset for its specific domain. New Constructs is scaling its US fundamental dataset globally in 2025, aiming to extend the same earnings-quality and valuation signals to international stocks.
What It Covers
David Trainer, CEO of New Constructs, explains how his firm built a verified fundamental investing dataset over 20 years, partnered with Google Cloud to create a domain-specific AI agent called FinSite, and why reliable data inputs — not general large language models — determine whether AI produces trustworthy stock analysis.
Key Questions Answered
- •Walled Garden AI Architecture: General-purpose AI tools like Claude or Gemini produce unreliable stock picks because they draw from unverified internet data. Domain-specific agents that restrict inputs to audited, validated datasets produce deterministic, trustworthy outputs. Investors evaluating AI tools should ask explicitly whether the underlying data is sourced directly from filings or scraped broadly from the web.
- •Data Integrity Standard: A dataset that is 99% accurate but lacks identification of which 1% is flawed is functionally unreliable for financial decisions. New Constructs validates every data point against original SEC filings with full audit trails — a standard confirmed when Harvard Business School reviewed 350 companies and found zero errors across all adjustments.
- •Core Earnings Edge Signal: New Constructs calculates a "core earnings edge" metric — core earnings minus net income, divided by total assets — to rank stocks by earnings quality. A Bloomberg index built on this signal returned 27% in the most recent year versus 18% for the S&P 500, and a "very attractive stocks" index has outperformed the S&P by 30–40 percentage points over five years.
- •Reverse DCF for Valuation: New Constructs uses reverse discounted cash flow modeling to determine what future profit growth a stock's current price implies. When NVIDIA's price implied a permanent 50% profit decline, the firm flagged it as a long idea. Investors can apply this framework by asking: what does the current stock price require the business to do, and is that realistic?
- •Agentic AI Workflow: Reliable AI at scale requires stacking multiple narrow, domain-verified agents rather than relying on one general model. Each agent must be built on a 100% auditable dataset for its specific domain. New Constructs is scaling its US fundamental dataset globally in 2025, aiming to extend the same earnings-quality and valuation signals to international stocks.
Notable Moment
Trainer described how Google Cloud built a working prototype of the FinSite AI agent within weeks of their first conversation — faster than any financial services firm he had worked with over decades. The speed stemmed from Google's need for a proven, reliable dataset to demonstrate real-world AI capability.
Episode Transcript
You know, there's a problem with AI. Like, you can ask Claude and and regular Gemini and all these things, all kinds of questions. Like, hey. What are the best stocks in the sector? It's like, you know, they're giving me the most popular stocks, you know, or or you get hallucinations. And and where Google Cloud and new constructs really fit together was this belief that we we can't have a generative AI that enters everything. The idea that you can just pour the Internet into a large language model and all the answers will emerge. You're tuned in you're tuned in to the investing for beginners podcast investing for beginners podcast, the show for the long term investor. We cut through the noise to focus on what works, Compounding, discipline, discipline, and the conviction to buy wonderful businesses and stick with them. Your path to financial freedom start now. Welcome to the investing for beginners podcast. Got a special guest for you today, two time guest, David Trainer, CEO of New Constructs. New Constructs is a independent research firm that helps investors with an AI tool, and we're gonna talk all about AI. David was gracious enough to do this interview a little bit early. So by the time you guys listen to this, there might have been some some things that have changed in AI. But, thank you, David, for for joining and and being flexible around my schedule with with my baby coming and everything. My pleasure, Andrew. Glad to do it. And these are the most exciting times that free baby time. Your world's about to get rocked, so, enjoy. Yeah. The nesting just started, like, last weekend. And then, like, my wife and I are are Google Calendar people. So next weekend has a Google Calendar event that has to do with further nesting. So it is an exciting time, and I'm just I'm trying to get as much sleep as I can. Like, I fell asleep watching the Lakers last night and have been sleeping ever since because I know I'm gonna I'm gonna have quite the deficit pretty soon. Yeah. It's, you know, it's funny. You will you will find having been through it three times myself, you will find that you have, resources, energy, that you didn't expect. It'll come it'll kinda come in droves, and it's part of the magical well, nature of it all is that it's it's you can do it. It's been done along for many, many years, so you'll you'll find, you know, you'll find it's it's it's pretty magical, and things will things will work out really well. I appreciate that. Well, speaking of things that have been around for many, many years, analyzing financial statements and doing the right kind of fundamental analysis has been a key strategy for investors who are picking stocks and doing well over a long time period. With all that in mind, I would love if you can give …
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by New Constructs
“partnered with Google Cloud to create a domain-specific AI agent called FinSite”
by Bloomberg
“A Bloomberg index built on this signal returned 27% in the most recent year versus 18% for the S&P 500”
company
“David Trainer, CEO of New Constructs, explains how his firm built a verified fundamental investing dataset over 20 years”
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