Neel Somani: Why Debuggability Is the Missing Foundation of Enterprise AI
Summary
Neel Somani argues that the real shortcoming in enterprise AI is not explainability alone but a lack of debuggability: the technical ability to locate failures, make surgical interventions, and certify that fixes work. He frames trust as an engineering capability built on three pillars — localisation, intervention and certification — and warns that compliance without these capabilities leaves organisations dependent on black boxes they cannot control.
Key Points
- Current AI explanations are often post-hoc rationalisations and can mislead organisations about causal mechanisms.
- Debuggability comprises three pillars: localisation (finding the mechanism), intervention (targeted fixes) and certification (formal guarantees over bounded domains).
- Most enterprises lack the technical capability to diagnose, repair and verify AI failures, making compliance performative in many cases.
- Formal methods, mechanistic interpretability and neural verification techniques offer concrete paths to build debuggable systems.
- Organisations should change hiring, procurement and deployment criteria to prioritise control over raw performance for high‑stakes applications.
Content Summary
Somani highlights that modern AI typically identifies statistical patterns and then generates coherent-sounding explanations that are not causally accurate. That creates dangerous operational blind spots: teams believe they understand system behaviour and then cannot fix it when it fails. He proposes reframing trust from a compliance task to an engineering discipline focused on debuggability. Practical steps include developing tooling and teams that can localise faults to specific mechanisms, perform surgical interventions that don’t cause regressions, and certify properties exhaustively within bounded domains. Somani points to existing technical foundations — sparse circuit extraction, neural verification, alternative attention mechanisms and symbolic circuit distillation — as evidence that provable, debuggable components are feasible and should be prioritised for consequential uses.
Context and Relevance
This piece matters for leaders responsible for AI strategy, procurement and risk. As enterprises scale AI into lending, hiring, content moderation and other high‑stakes domains, the cost of not being able to diagnose and fix failures compounds technical debt and organisational risk. Regulatory compliance (for example, the EU AI Act) reduces some exposure but does not substitute for the engineering capability to intervene precisely and verify outcomes. Firms that invest early in debuggability will have a competitive advantage as procurement and liability concerns shift the market toward verification-ready systems.
Why should I read this?
Short version: if your organisation uses AI for anything important, this is not optional. Read it because it tells you what actually stops AI being safe and controllable — and what to do about it. Somani isn’t selling a checklist; he explains a practical shift from chasing performance to engineering for control. It’ll save you from spending millions on flashy models that you can’t fix when they go wrong.