Welcome to episode six of Forward Deployed. Noah sits down with Rohit Krishnan, author of Strange Loop Canon, to talk about what happens when we take the word “agent” seriously.
Rohit brings together AI, markets, economics, organizational theory, and simulation. The conversation moves from principal-agent problems to enterprise world models, data-first ontologies, MarketBench, model self-knowledge, and the harder question underneath all of this: how do we build agentic systems that can coordinate, learn, and act inside messy organizations?
Key Topics Covered
Agents as an economic problem: Why principal-agent theory, markets, and organizational design matter for AI agents
Rohit’s path: Startups, markets, McKinsey, investing, AI, and the long-running attempt to connect economics with technology
Enterprise world models: Why organizations may be a richer substrate for world models than the physical or visual domains alone
Data-first ontologies: How company histories, email, Slack, docs, GitHub, Jira, and other streams can produce a live model of how work actually happens
Small models and simulations: Why Rohit is experimenting with small JEPA-style models and daily retraining instead of treating frontier LLMs as the whole system
Counterfactuals and prediction: How world models can help answer questions like whether a customer will renew, how to respond to a contract, or which actions are likely to produce which outcomes
MarketBench: Why Rohit and Andrey Fradkin built a benchmark for asking whether AI agents can bid on their own capabilities and costs
Model self-knowledge: Why Gemini tends to overbid, GPT tends to underbid, Claude is directionally better, and none of them are well calibrated enough yet
Memory and onboarding: Why agents still feel like brilliant day-one employees, and why markdown files, project context, and memory systems are only partial answers
Specs, workflows, and co-evolution: Why real software work is not just satisfying a fixed spec, but helping the spec and the deliverable evolve together
Kids and AI: How to help children use AI as a tool for creation and discovery without outsourcing their own taste, voice, or imagination
Timestamps
Note: timestamps are approximate
00:00 - Introduction: Rohit Krishnan, Strange Loop Canon, and market mechanisms for agents
00:55 - Rohit’s background across AI, economics, startups, consulting, investing, and simulation
04:30 - Why “agents” are an economic and organizational problem
09:55 - Why technology wants answers while economics designs mechanisms
11:00 - Enterprise world models and the limits of traditional digital twins
14:00 - Building organizational world models with small JEPA-style systems
16:15 - Public datasets, Enron, startup data, and daily decision support
17:20 - Modeling companies through email, Slack, docs, GitHub, Jira, and Confluence
20:35 - Data-first ontologies and why static top-down ontologies break as companies change
24:10 - Counterfactual questions, model priors, and where LLMs regress toward the median
27:00 - Frontier models, unknown unknowns, and why expertise still shapes the questions worth asking
32:30 - MarketBench and using markets to coordinate AI agents
35:35 - How model auctions work: success probability, token cost, bids, and allocation
37:05 - Model calibration: Gemini overconfidence, GPT underconfidence, and Claude’s relative advantage
40:00 - Whether prior performance data can improve model self-knowledge
41:40 - Agents as brilliant day-one employees and the limits of current memory systems
44:35 - Onboarding, markdown files, context windows, and the need for better mechanisms
47:00 - Misalignment in coding agents and why tests do not solve the whole problem
48:50 - Software companies, software factories, and rediscovering The Mythical Man-Month
50:50 - Why requirements, specs, and deliverables have to co-evolve
51:30 - Using world models to discover workflows and skills inside an enterprise
53:00 - Kids, AI, creation vs. consumption, and preserving individual voice
Links & References
Core References
Rohit Krishnan - Guest on this episode and author of Strange Loop Canon
Strange Loop Canon - Rohit’s Substack
MarketBench: Evaluating AI Agents as Market Participants - The paper by Rohit Krishnan and Andrey Fradkin
Agent, Know Thyself! (and bid accordingly) - Rohit and Andrey’s writeup on MarketBench
Andrey Fradkin - Rohit’s MarketBench coauthor
Concepts & Frameworks
Principal-agent problem - The economic framing Noah and Rohit return to when talking about AI agents
MarketBench - Benchmarking whether agents can estimate their own success probability and task cost
SWE-bench - The software engineering benchmark used as one task source for MarketBench
JEPA / I-JEPA - Joint Embedding Predictive Architecture and world model context
Enron email dataset - Public organizational dataset Rohit discusses as a testbed for enterprise world models
The Mythical Man-Month - The classic software engineering reference Noah invokes near the end
Previous Episodes
About the Hosts
Noah Brier is co-founder of Alephic, an AI consulting company helping brands and enterprises build custom AI systems.
Rohit Krishnan is an independent researcher and writer whose work spans AI, economics, markets, organizational theory, and simulations. He writes Strange Loop Canon.
Connect with the Hosts
Rohit Krishnan: Strange Loop Canon | X/Twitter
Alephic: alephic.com
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