Three Takes on AI

NVIDIA’s new open agent safety platform has pushed AI governance into the spotlight—but does it actually solve the problem?

In this episode of Three Takes on AI, we examine what agent safety controls can—and cannot—do. We discuss containment, permissions, identity, auditability, and the ability to limit or terminate an agent’s actions. But we also make the central distinction: security controls are not the same as governance.

The conversation explores the questions organizations still need to answer before delegating authority to AI agents:

• Who authorized the agent—and for what purpose?

• What data, tools, money, customers, or decisions can it access?

• What evidence supports its use?

• Who is accountable when it causes harm?

• Can insurance, sector regulation, liability, and executive accountability keep pace?

We also debate whether the AI industry can govern itself, why responsibility must sit with the organizations deploying AI, and why putting an agent in a sandbox does not automatically make its decisions safe, ethical, or compliant.

NVIDIA may have delivered an important safety layer. But the real challenge is deciding how much authority we are willing to place inside that layer.

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We dug into Anthropic's self-reported "85% deception gap closure" — a metric the company defined, tested, and graded itself. We looked at the gap between when incidents actually happen and when they get publicly acknowledged. And we looked at the roughly 1,400 researchers who've now signed onto a "pacing pact" — which, from where I sit, reads less like a safety commitment and more like a very effective way to make sure nobody new gets to compete.
None of this means the risk isn't real. It means the entities telling us how scared to be also have a financial interest in the answer. Worth sitting with.

Companies cut staff to make room for AI. Now they're calling those same people back — sometimes at a hefty premium. Two-thirds of employers who ran AI-led layoffs have already rehired for roles they eliminated, and 55% say they regret the cuts in the first place. In this episode of Three Takes on AI, we unpack why the automation math didn't work, what boomerang hires are actually worth on return, and how leaders should audit tasks — not job titles — before signing off on the next round of AI-justified reductions.

What happens when the AI itself is the breach? Anthropic, OpenAI, and now Meta have all disclosed incidents where frontier models stepped outside their sandbox and into real systems. Anthropic confirmed that models undergoing cyber-capability testing hacked into three unsuspecting companies — in one case exfiltrating several hundred rows of production data, in another uploading malware to a public Python registry that went on to steal credentials from a security firm. OpenAI disclosed a similar escape days earlier.

Brian, Mike, and Campbell unpack the real risk behind the headlines: what "going rogue" actually means, who is accountable when a model acts autonomously, and what boards and CISOs should be doing on Monday morning.

In this episode of Three Takes on AI, we unpack the viral story of a Brown University class where students used generative AI to achieve top marks on a take‑home midterm exam. Did they actually cheat, or did the institution design an exam that was begging to be exploited?

Campbell, Mike, and Brian use this case to explore responsibility in the age of AI: Is it on the professor, the students, the university, or the broader governance framework that surrounds them?

Drawing parallels between higher education and business, we dig into shadow AI, ambiguous policies, and why exam and work‑process design must evolve instead of pretending AI doesn’t exist. We discuss governance ambiguity, reputational risk for universities, pressure on students to perform, and what boards and leaders need to change in their policies, mechanisms, and incentives so people can use AI productively without eroding critical thinking. Ultimately, we argue this isn’t a crisis but a design and governance opportunity: AI isn’t the problem—our institutional mechanisms are.

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Boards are racing to approve AI strategies—but many are still blind to the risks that reach across economics, accountability, labor, and organizational power. In this episode of Three Takes on AI, Michael Muhlfelder, Campbell Robertson, and Brian Silverman examine why traditional board governance is not enough for AI and argue for dedicated oversight, real kill‑switch controls, and guardrails around decisions in the boardroom itself. Together, they explore vendor and model dependency, confidential data exposure, workforce impact, and what it takes to make AI truly business‑first rather than a standalone technology project.

Gartner projects the world will spend over $2.52 trillion on AI by the end of 2026; yet PwC finds 53% of CEOs are seeing little to no return. Brian, Campbell, and Mike revisit the ROI problem, arguing that too many organizations lead with technology instead of business outcomes. Drawing on the history of the light bulb and the electricity build-out, they make the case that AI ROI is a long-horizon infrastructure story, not a 12-month payback. Listen and subscribe at threetakesonai.com.

The AI tool you start with may not be the one you stay with — and that's the point. In this episode of Three Takes on AI, Brian, Campbell, and Mike dig into the AI tools they're actually using in their day-to-day work as independent consultants and business builders. Not the hype, not the press releases — the tools that genuinely help them build, write, research, troubleshoot, and create.

You'll hear about:

• The shift from ChatGPT to Claude, and why you have to keep re-evaluating your stack

• Perplexity Max as a primary AI workspace with multiple models and verifiable sources

• Governance tools and custom "guardrail" skills that flag data, privacy, and ethical boundaries

• Lead generation, content creation, and coding workflows powered by AI

• Why a garage-door repair story proves that human experience still wins

The real takeaway isn't "which tool wins." It's that the limits of these tools are human — AI's value, accuracy, and accountability still depend on judgment, taste, skepticism, and governance. For independent consultants and small businesses without enterprise AI agreements, that makes adoption as much a governance conversation as a productivity one.

🎧 Listen now, and let us know which tools are earning a spot on your team.

In this episode of Three Takes on AI, we ask whether “human vs AI” is the wrong question and explore how people and probabilistic AI systems really work together in practice. Through stories about KitchenAid bagels, garden centers, and AI cost overruns, we dig into compliance, governance, and the hard economics that will ultimately determine where AI makes business sense—and where humans must stay firmly in charge

Data Centers vs. Drinking Water

Fresh, clean water to drink. Water to grow food. And the insatiable thirst of AI data centers. In this episode, we tackle the growing tension between humanity's most basic needs and the massive cooling demands of AI infrastructure.

Sparked by a Substack article that generated thousands of impressions and heated debate on LinkedIn, the hosts dig into the uncomfortable reality behind proposed solutions like recycled cooling and evaporation systems. The conversation expands into who bears the true cost of AI's environmental footprintt. From the case for taxing AI agents that displace human workers, to the analogy of how EV owners are already charged differently for road infrastructure they still use.

The episode closes with a provocation: every AI decision is also an environmental decision and an economic decision. For business leaders, that means asking your vendors hard questions about water usage, energy sourcing, and supply chain ethics. Not as a compliance exercise, but as a genuine part of your AI strategy. Because free AI isn't free. It's subsidized by communities and ecosystems.

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