Podcast Summary: “Entering the Trillion-Agent Economy”

Podcast: Azeem Azhar's Exponential View
Host: Azeem Azhar
Guest: Rohit Krishnan (Writer: Strange Loop Canon)
Date: February 19, 2026

Episode Overview

In this thought-provoking episode, Azeem Azhar and Rohit Krishnan delve into the rapidly approaching era of AI agents: autonomous, task-completing systems proliferating across digital life. Together, they examine the practical and philosophical shifts that come with a “trillion-agent economy”, covering personal workflows, future economic models, agent-to-agent transactions, and the stubborn limits of machine-generated writing. The conversation is rich with hands-on experiences, visionary projections, and candid peer-to-peer exchanges—a must-listen for those at the intersection of AI, business, and society.

Key Discussion Points & Insights

1. What Is an AI Agent?

• Defining Agentic Systems:
  • Azhar: Predicts “hundreds of millions and then billions... of agentic systems running around the Internet as automated infrastructure in a few years' time” [00:16].
  • Krishnan: Coins the term “Homo agenticus” to describe this new era where “agents are the ones that are interacting mostly with each other... doing transactions for us” [00:35].
• Agent Economy:
  • Agents will require new coordination frameworks and mechanisms to safely and reliably exchange value [00:54].

2. Practical Use of Agents: Personal Workflows Evolving

• Daily Agent Integration:
  • Krishnan describes using AI agents (notably via Openclaw) for:
    • To-dos and daily admin [07:43]
    • Email triage and cleanup
    • Interacting with code and running technical tasks
  • Azhar: “I have committed about 300,000 lines of code this year... my coding world has changed remarkably with Opus 4.5, now Opus 4.6, but also now with these Openclaw agents.” [04:09]
  • Azhar’s agent “Armini Arnold”: An orchestrator and personal assistant, capable of everything from market analysis to cyber-security fortresses.

Notable Example [19:25]:

Krishnan: “It’s amazing that... you have an analyst at your disposal... you can spin up a report about anything in the world in 20 minutes... an extremely well-researched 90th percentile report on any subject and topic that you want.”

Notable Quote [11:06]:

Azhar: “I want them to do the most useful things for me, not the trivial thing.”

3. Exponential Token Usage & Intelligence-on-Tap

• Token usage is exploding:
  • Azhar: “On Wednesday... I fell just shy of 100 million tokens personal usage per day... each one of those is generating value... it is unending.” [00:00, 21:49]
  • Krishnan: Built a Rust tool to track agent usage—his agent used 17 billion tokens in Q4 2025, and then 50 billion in January 2026 alone [23:15].
  • Agents are democratizing access to high-level research, analysis, and creativity.

4. Agent Capabilities: Research, Analysis, and Tool Use

• Agents as Analysts:

  • Both hosts describe using AI agents for deep dive research, taxes, portfolio management, or even paleontology projects [19:25].
  • Krishnan: “Anything that I want to analyze... is effectively at my fingertips. The only thing that remains is for me to ask.” [19:25]
  • Azhar’s agent generates better research reports than large LLMs with deep research capabilities [12:00].

• Tool Chains:

  • Openclaw: Agent orchestration framework with persistent memory and tool connectivity (Slack, Telegram, etc.) [04:56]
  • Codex & Claude Code/Cowork: Described as iterative, task-solving environments—not just for code but also comprehensive research and analytics [14:17, 16:02].
  • MCP (Model Context Protocol): Enables agents to interface more seamlessly with apps/browsers [16:28].

5. Agents, Economics, and the Trillion-Agent Future

• Coordination & Exchange:
  • Agents will require identity, means of communication, verifiability, and a unit of exchange (potential for crypto, micro-payments, etc.) [44:01].
  • Krishnan: “If you want them to do stuff together, they need some shared medium of exchange... the Hayekian price signal thing.” [44:01]
  • The agentic economy may reproduce some of the same “economic invariants” (currency, identity, contracts) as human economies [45:23].
• Resource Management:
  • Value will no longer be evenly distributed; lower costs will democratize complex analyses and automation [41:00].

6. Limits of Machine-Generated Writing

• Both hosts have experimented with getting AIs to write better—for example:
  • Krishnan’s “Horus” Project: Analyzing and improving “literary cadence” in AI-generated writing [24:50]
  • Azhar’s “Brocker” Tool: Attempts to pass stylistic “fingerprints” to the LLM, with modest success [26:11]
• Persistent Problem:
  • AIs push writing toward the median, losing the human edge of “taste” and personal voice [31:49, 32:36].
  • Azhar: “AI writing is distinctly mid... It doesn’t just delve.” [26:11]
• Insight:
  • Breaking writing (and coding) tasks into discrete, fractalized components improves results, but reflective, quality writing remains uniquely human [37:28, 34:04].

7. Getting Started & Security Considerations

• Practical Onboarding Advice:
  • Begin with tools like Claude Code/Cowork to get used to agentic workflows before attempting local installation of openclaw [46:33, 49:59].
  • If privacy is a concern, run agents on a VPS (virtual server) for isolation [46:33].
• Security Mindset:
  • Krishnan admits to a “YOLO” approach but advises caution: “LLMs... will not know what is poison versus what is not very easily... I would say: connect it to something where you are okay with... it going off into the world and screwing up something.” [47:44]
  • Gradual, incremental integration is recommended; start small and expand agent permissions as you gain trust/comfort.

8. Behavioral Shifts and Social Impact

• Krishnan: The major behavioral hurdle is simply learning to “just talk to it as your analyst,” letting the agent handle ambiguity and context [51:21].

Notable Quotes & Memorable Moments

• On the proliferation of agents:
  • Azhar (00:16): “...it’s not unreasonable to think that we will have hundreds of millions and then billions and then tens of billions and hundreds of billions of agentic systems running around the Internet...”
• On the shift to an agentic economy:
  • Krishnan (00:35): “I called it Homo agenticus. Suddenly the world becomes one where agents are the ones that are interacting mostly with each other...”
• On AI as ‘intelligence on tap’:
  • Krishnan (00:06): “You know, it is intelligence on a tap. Computer used to be a job that people did. There used to be like room full of people who were computers. And now it’s machine. The next step is analyst.”
• On the challenge of agent personality:
  • Krishnan (34:04): “...when I’m reading an author that I love, there is a sense of who the author is in the book. LLMs are still kind of flat in the amount of personality they are able to emit...”
• On human vs agentic writing:
  • Azhar (32:36): “But most people don’t write well enough... it is a testament... that when we write we are combining our assessment of empirical space... and some measure of interiority that is not captured elsewhere. Is that what’s going on?”
• On personal breakthroughs:
  • Azhar (12:00): “I have no idea what’s going on under the hood in openclaw but it was remarkable. It has kind of changed my way of working.”

Timestamps for Key Segments

| Timestamp | Topic | |------------|------------------------------------------------------------| | 00:00 | Massive token usage; AI as ‘intelligence on tap’ | | 04:09 | Azhar & Krishnan’s agent-powered workflows | | 07:43 | How agents have replaced traditional productivity tools | | 13:37 | How Openclaw & Codex enable richer workflows | | 19:25 | Example of reports and personal research with agents | | 23:15 | Quantifying the explosion of token usage | | 26:11 | The struggle for literary quality in AI-generated text | | 34:04 | The challenge of interiority and real personality in writing| | 37:28 | Fractal, compositional approach to writing with AI | | 39:26 | Defining an agent in the upcoming economy | | 44:01 | Why agents need economic coordination and money | | 46:33 | How to get started with agents, security advice | | 49:59 | Using local/cloud and learning to trust AI agents | | 51:21 | Helping non-technical users adapt to the new paradigm |

Key Takeaways

• We’re at the tipping point of ubiquitous AI agents—not as a prediction, but as an evolving daily reality for early adopters.
• Agent-centric workflows are replacing old paradigms, with major impacts on personal productivity, research, business process, and even domestic life.
• The “trillion-agent economy” will require new forms of identity, value exchange, security, and institutional frameworks—echoing, but not mirroring, traditional economic models.
• Agentic writing is the last bastion of human uniqueness: AI’s can mimic, but lack voice, taste, and depth—at least for now.
• Getting started is easier than ever, but security and thoughtful integration are key—adopt incrementally, experiment with sandboxed environments, and don’t hand over critical access too lightly.

For listeners eager to dive deeper:

• Krishnan’s Substack “Strange Loop Canon” & Azhar’s “Exponential View” are both recommended for ongoing commentary on the AI and agentic revolution.
• Experiment hands-on with Claude Code/Cowork, Codex, or Openclaw to get a sense of the future today.

[End of Summary]