The a16z Show: AI Just Gave You Superpowers — Now What?
Andreessen Horowitz | March 19, 2026

Episode Overview

This episode explores the profound changes artificial intelligence (AI) is bringing to work, startups, and society, driven by recent advances in AI agents. Host Robert is joined by Christian Catalini (co-founder of LightSpark and founder of MIT Crypto Economics Lab) and Eddie Lazarin (CTO, a16z) to discuss Christian’s influential new paper, Some Simple Economics of AGI. Together, they unpack key frameworks for understanding the economic implications of advanced AI—including shifting the nature of labor, the rise of hyper-leverage for individuals, the challenge and opportunity in verification, and the vital intersection between AI and crypto. The conversation is both practical and philosophical, ultimately offering optimism—and warnings—about how individuals and organizations can navigate a new world where “you’ve just been given superpowers.”

Key Discussion Points and Insights

1. AI as a Force Multiplier for Individuals

• “You’ve just been told you have superpowers. You’ve just been told you can have multiple employees for $200 a month. What do you do?”
  (Eddie Lazarin, 00:00)
• AI agents can now run complex, long-duration tasks, shifting the feeling from “using a tool” to “working with a coworker.”
• The meme of the “one-person billion-dollar startup” is becoming reality, as AI enables single individuals to perform work that once required entire teams.

⚡ Memorable Quote

• “The specialty of the human being is going to be looking at the whole thing and being able to zoom in and zoom out and zoom in and zoom out across an entire endeavor ... If I was a young person today ... I would try to convince my parents to give me some money to harness a huge swarm of computers and see, like, can I spend $5,000 of compute productively?”
  (Eddie Lazarin, 49:54)

2. The Economics of Automation and Verification

• Automation is rapidly reducing the cost of output (“anything that can be measured will be automated”), but “verification”—deciding whether the output is correct or valuable—remains challenging.
• Jobs are bundles of tasks. Coding is a present case study: the writing of code is being automated, but the critical skill is shifting to verifying, integrating, and judging that code.

⚡ Memorable Quotes

• “The balance of work for a great engineer is shifting quickly. The amount of attention paid to writing the code ... is smaller and vanishingly small ... And a huge part of the work is now verification.”
  (Eddie Lazarin, 09:02)
• “These tools ... are taking out the groundwork. But ... at the top thinking through, what is not known? Where can we push beyond the boundaries of what's been recorded, measured, digitized? ... Those decisions ... have much higher leverage than we had before.”
  (Christian Catalini, 03:46)

3. Defining Labor in the AI Economy: The Three Main Roles

Christian outlines a taxonomy for where human labor can thrive:

a. Verifiers

• The “top experts” who ensure system output aligns with intent, dealing with non-measurable, exceptional cases.
• This work is high-leverage but threatens its own future, as verifiers “label” the data that enables their eventual automation (“the codifier’s curse”).

b. Directors

• Individuals who drive the intent, vision, and ongoing course corrections in an AI-driven enterprise.
• Startups can be run by “directors” with swarms of agents as their workforce.

c. Meaning Makers

• Those who create and coordinate around societal trends, consensus, or art—often in domains less measurable or based on social constructs.
• Paradoxically, as automation increases, the label “human made” may become prized for its scarcity, not necessarily its intrinsic quality.

⚡ Memorable Quote

• “The apprenticeship might be dead, but the real work is beginning.”
  (Christian Catalini, 52:34)

4. Crypto as a Verification Tool in the Age of Probabilistic AI

• As AI blurs provenance and authorship, crypto (blockchains, cryptography) becomes the way to anchor trust, ensure identity, and certify the origin/data/provenance of digital assets.
• The declining cost of automation and the relatively slower decline in verification cost creates a “gap” that crypto is uniquely positioned to help close.

⚡ Notable Moment

• “In a land where trust is going to be increasingly scarce, yes, I do think crypto primitives will finally truly shine ... everything that makes verification easier is going to be a part of solving that gap.”
  (Christian Catalini & Eddie Lazarin, 25:31)

5. Risks: The “Trojan Horse” of Invisible Technical Debt & Systemic Liability

• As AI-generated work (software, content, decisions) proliferates, it becomes impossible for humans to verify all outputs. This raises the risk of undetected errors, security vulnerabilities, and accumulating systemic risk.
• The incentive structure means companies may “ship” unverified AI outputs, focusing on short-term productivity at the potential cost of future liabilities.
• New industries and products (like specialized insurance for AI agent outputs) will emerge to help manage this risk.

⚡ Memorable Quotes

• “There's a good chance that it may carry some technical debt of different types ... if you scale it up to the entire society, that means that we’re probably accumulating some degree of systemic risk as we accelerate through.”
  (Christian Catalini, 35:56)

6. Two Future Scenarios: “Hollow Economy” vs. “Augmented Economy”

• Hollow Economy:
  • AI eliminates entry/junior jobs, reducing paths for human training and verification; “codifier’s curse” leads to ongoing self-displacement; misaligned incentives risk systemic failures.
• Augmented Economy:
  • AI accelerates mastery, helps individuals discover and specialize in their aptitudes, and enables people to operate at higher leverage as “directors” or “meaning makers.”
  • Investments needed in verification tools, crypto primitives, robust feedback systems, and education for this transition.

⚡ Memorable Quotes

• “We’re not training our future class of verifiers, the juniors ... our top verifiers are progressively becoming ... slimmer and slimmer. That class is shrinking in size, and we’re creating all these potential risk that can lead to what we call hollow [economy].”
  (Christian Catalini, 44:53)

7. Practical Advice for Individuals and Founders

• Young professionals: Don’t lament the end of traditional pathways (“the fantasy summer coding project is now a hobby”), but instead “learn to harness a huge swarm of machines.”
• Leverage AI to simulate environments, accelerate learning, and set more ambitious goals.
• Entrepreneurs: Build around verification, proprietary data, and create new forms of value; less labor means ultra-lean organizations and faster product cycles are not only possible—they’re happening.

8. Open Source, Diversity, and Antibodies for Side Effects

• Open source models, despite some risks, are critical for surfacing new types of AI preferences and vulnerabilities, enabling society to “build the antibodies” for mass deployment side effects.
• The AI-crypto nexus is presented as essential in defending against systemic failures and maintaining trust in networked, highly automated economies.

Important Timestamps & Sections

• 00:00 The metaphor of AI superpowers and democratized leverage
• 05:30 How the job of the engineer and developer is shifting to verification
• 10:35 Automation vs. verification as core economic concepts
• 17:43 The “AI Sandwich” and taxonomy of future work—verifiers, directors, meaning makers
• 23:48 Crypto’s role in the AI-native economy (identity/provenance/verification)
• 29:42 What is (still) unmeasurable? The limits of AI and the areas for human advantage
• 35:56 Risks: technical debt, systemic liability, and the need for insurance models
• 44:53 “Hollow economy” vs. “augmented economy” scenarios
• 49:54 Concrete advice for young people—how to start now and what skills to prioritize
• 56:54 Trojan horse risks, open source, and the need for robust verification infrastructure
• 61:44 How the hosts are personally adopting these findings
• 64:34 Optimism, complementary technologies, and closing thoughts

Notable Quotes

• “Now you’ve just been told you have superpowers ... you can have multiple employees for $200 a month. What do you do?”
  • Eddie Lazarin (00:00)
• “Look, the apprenticeship might be dead, but the real work is beginning.”
  • Christian Catalini (52:34)
• “Crypto primitives ... in a land where trust is going to be increasingly scarce ... will finally truly shine across a number of applications.”
  • Christian Catalini (25:31)
• “Is this not how that happens? The one-person billion-dollar startup ... The skill to control a huge class of machines ... that is itself a skill set that has never been developed because that’s never made sense to do.”
  • Eddie Lazarin (51:23)
• “Even Nick Bostrom has changed his tune ... Instead, Bostrom now frames it as a patient who is terminally ill, going to die, but we can choose to perform a life risky, life saving surgery ... So why not, why not take the shot?”
  • Eddie Lazarin (54:58)

Conclusion & Key Takeaways

The AI revolution is transformative—a new “surplus” for individuals and organizations to exploit, but it demands new mindsets and skill sets around verification, agency, and rapid adaptation. Crypto and DeFi intersect as the trust infrastructure for an increasingly automated and fragmented world. The future depends on our ability to adapt quickly, build robust verification/collaboration tools, and embrace new, higher-order forms of contribution. The message to listeners is clear:
The real work is only beginning—learn to wield your (AI) superpowers.

For further study:
Check out Christian Catalini’s paper, Some Simple Economics of AGI (linked via a16z), and continue exploring the a16z Show for more on the intersection of AI, crypto, and the evolving future of work.