Скачать или смотреть AI, Trust, and the Future of Financial Transparency with David Trainer of New Constructs

How do you trust the numbers? In this episode, David Trainer breaks down how New Constructs combines machine learning, NLP, and deep accounting expertise to reverse accounting distortions and surface the true economics of a business. We dig into their RoboAnalyst system, why ontology + taxonomy matter more than flashy models, and how a narrow, expert-defined data domain—what David calls a “small language model”—can power auditable, repeatable alpha. We also talk transparency (no black boxes), an ETF and indices that have outperformed the S&P 500, and New Constructs’ Google Cloud partnership to build an AI investing agent.

What you’ll learn:

● Why “garbage in, garbage out” still rules—and why data quality and footnotes are the edge
● How New Constructs structures filings (MD&A, footnotes, corporate actions) into an auditable knowledge graph
● The human-in-the-loop design: rules engines + auto-parsing for the easy stuff, experts on the exceptions
● Why transparency and auditability (not model secrecy) build institutional trust
● The case for domain-specific small language models vs “internet-sized” LLMs
● Thoughts on ESG (start with G—governance/ROIC), regulatory change, and error-proofing at scale
● Where this goes next: AI agents for fundamentals, technicals, sentiment, and potential expansions to global coverage and even political/healthcare integrity scoring

Chapters:

0:00 Intro & origin story (the “apples-to-apples” accounting problem)
2:40 Mission: improving the integrity of capital markets
5:14 Reversing accounting distortions (economics vs. accounting)
7:30 Inside RoboAnalyst: rules, NLP, and human validation
11:07 What differentiates New Constructs (methodology, transparency, alpha)
12:24 Why data plumbing beats model hype
13:32 “Small language models” for trusted finance AI
15:40 Training signals from MD&A & footnotes; seeding the rules
19:52 Data checks, model sanity, and exception handling
21:13 Automation vs. analysts: serving humans only the hard problems
25:18 Trade-offs (scale, capital) and the Google Cloud partnership
29:31 Building trust: 100% auditability, no black boxes
33:49 Guardrails against errors & overconfidence
36:41 Adoption challenges & Wall Street incentives (IPOs, “adjusted EBITDA”)
41:17 Accounting rule changes & disclosure shifts
42:47 Roadmap: agents, broader data, making markets boring (and better) again
47:05 What could threaten the edge; why the source dataset is the crown jewel
49:22 ESG, governance, and ROIC as the signal that matters
54:04 Moonshots & where this goes in 3–5 years
56:05 Wrap-up