In boardrooms across Silicon Valley and Wall Street, executives are grappling with an uncomfortable truth: the AI systems powering their most critical decisions are fundamentally unauditable. While vendors promise “explainable AI” and consultants tout governance frameworks, the reality is that most organizations are running on faith and deploying systems they cannot meaningfully inspect, verify, or fix when things go wrong.
Neel Somani argues that the path forward isn’t about providing better explanations or establishing more oversight committees. Instead, it requires what he calls “debuggability”—the ability to surgically intervene in AI systems with the same precision engineers expect from traditional software.
His recent work, “The Endgame for Mechanistic Interpretability,” challenges the field’s current trajectory and offers a roadmap that should matter to every executive deploying AI at a large scale.
The Governance Illusion
Most AI governance today operates on what Somani identifies as a fundamental mismatch. Organizations treat AI systems like they treat human employees—with policies, reviews, and accountability structures. But AI systems aren’t employees. They’re software, and software that operates through mechanisms even their creators struggle to explain.
“Mechanistic interpretability is currently pulled between competing visions,” Somani writes in his analysis. “The disagreement persists because mechanistic interpretability lacks an agreed-upon end goal. What, exactly, would count as success?”
This isn’t an academic question. When an AI system denies a loan application, flags a security threat, or recommends a medical diagnosis, leadership needs more than a plausible-sounding explanation. They need the ability to identify why the decision was made, fix any errors without breaking other functionality, and verify that the fix actually works.
Current approaches fail this test. The “explanations” provided by most AI systems are retrofitted stories, not causal mechanisms. Change one variable and the entire explanation often shifts, revealing that the system never operated according to the logic it claimed.
From Legibility to Debuggability
The field of AI interpretability has traditionally focused on making systems “legible”, producing explanations that humans can understand. But as Somani points out, legibility alone is insufficient for organizational control.
“Explanations that fail under counterfactual intervention may sound plausible, yet provide no reliable handle for control,” he notes. “Even advocates of curiosity-driven interpretability typically hope their findings will eventually support some downstream use, and legibility alone does not guarantee this.”
Instead, Somani advocates for debuggability: the ability to localize failures to specific mechanisms, intervene predictably, and certify that the intervention preserves desired behavior on bounded domains. This shift from understanding to control has profound implications for how organizations should evaluate and deploy AI systems.
Consider a financial institution using AI for fraud detection. Under the legibility paradigm, the system might explain that a transaction was flagged due to “unusual spending patterns.” Under debuggability, the institution could identify the specific neural circuit responsible for the decision, modify it to correct false positives, and mathematically prove that the modification won’t create new vulnerabilities in other transaction types.
The Three Pillars of Trustworthy AI
Somani outlines three requirements for achieving true debuggability in AI systems, each placing progressively stronger demands on our technical capabilities:
Localization involves identifying which internal mechanisms drive specific behaviors. This goes above pointing to a layer or component—requiring distinguishing mechanisms that actually cause the behavior from those that merely correlate with it. As Somani explains, this includes “being able to determine whether the behavior can occur without the mechanism being active, or whether the mechanism can be active without producing the behavior.”
Intervention requires the ability to modify identified mechanisms with surgical precision. The modification must remove undesired behavior without causing collateral damage elsewhere in the system. This is where many current approaches fail—fixes that seem reasonable in isolation often create cascading effects that make the system less reliable overall.
Certification represents the highest level of assurance: making exhaustive, falsifiable claims about model behavior on bounded domains. These aren’t probabilistic guarantees that something is unlikely to happen. They’re mathematical proofs that certain behaviors cannot occur within specified contexts.
Why Current Approaches Fall Short
The implications for organizational leadership are stark. Most AI systems deployed today fail all three tests of debuggability. They operate as black boxes with post-hoc explanations, making genuine accountability impossible.
This isn’t just a technical limitation—it’s a governance crisis. When an AI system makes a consequential error, organizations face an impossible choice: accept the flaw, attempt risky modifications that might create new problems, or abandon the system entirely.
The problem is compounded by the pace of AI deployment. As Somani notes, the gap between what organizations are deploying and what they can control is widening. Traditional risk management frameworks, designed for systems with clear audit trails and accountable decision-makers, simply don’t apply to opaque neural networks.
Taking a Practical Path Forward
Despite these challenges, Somani sees a viable path toward debuggable AI. Recent advances in sparse circuit extraction, neural verification frameworks, and symbolic distillation suggest that meaningful control is achievable—if organizations are willing to invest in the necessary capabilities.
“Debuggability is about constructing a family of verified, compositional abstractions that faithfully reproduce model behavior on bounded domains and support predictable intervention,” Somani writes. “These abstractions are partial, local, and plural, but they are exact where they apply.”
This vision doesn’t require that entire AI systems be fully verifiable end-to-end. Instead, it focuses on building domain-bounded assurances for critical components. As Somani explains, “Meaningful debuggability consists in guarantees such as: This subcircuit cannot activate a forbidden feature on domain D. This intervention removes a failure mode while preserving all other behaviors in scope.”
For executives, this means rethinking how AI systems are procured, deployed, and managed. Instead of asking only about accuracy and speed, organizations should demand evidence of debuggability. Can the vendor demonstrate localization capabilities? Have they proven interventions preserve desired behaviors? Are there formal certifications for critical safety properties?
The Executive Imperative
Building AI systems that leaders can actually trust isn’t about adding more oversight or requiring better documentation. It’s about the fundamental technical capabilities that most organizations currently lack. As Somani argues, this represents a shift from treating AI as a mysterious oracle to treating it as engineered software that can be inspected, modified, and verified.
For CEOs and boards, the message is clear: the organizations that master debuggability will be the ones that can deploy AI confidently at scale. Those that don’t will remain vulnerable to failures they can neither predict nor fix. In an era where AI drives competitive advantage, that’s a risk no leader can afford to ignore.
The question isn’t whether to deploy AI—that ship has sailed. The question is whether your organization will have the technical infrastructure to govern it when it matters most and build systems from the ground up.
The stakes for organizational leadership couldn’t be higher. In a world where AI drives strategic decisions, the difference between companies that can debug their systems and those that can’t will determine who leads and who follows.
