The Claim and Why It Deserves Scrutiny

Keith Rabois, managing director at Khosla Ventures, made a pointed claim this week: the product manager role in tech "makes no sense" in the era of AI. His argument, stripped to its core, is that AI eliminates the need for intermediaries who translate between engineers and customers. This is a provocative claim from a credible source, and it deserves serious analysis rather than reflexive dismissal or uncritical celebration. What Rabois is describing is not simply a labor market shift. He is making a claim about the structural role of competence intermediaries inside organizations, and organizational theory gives us tools to evaluate whether that claim holds.

Intermediaries as Competence Brokers

The product manager role emerged as a solution to a specific coordination problem. Engineers and customers operate with different schema structures. Engineers reason about systems; customers reason about outcomes. The PM historically served as a translation layer, converting customer signal into technical specification. This is not a trivial function. Gentner's (1983) structure-mapping theory would frame this as cross-domain analogical reasoning, identifying structural correspondences between two representational systems that do not share surface-level vocabulary. The PM's core skill was not information access. It was schema translation.

Rabois appears to assume that generative AI can absorb this translation function automatically. That assumption deserves skepticism. Algorithmic systems are exceptionally good at pattern completion within a learned distribution. They are considerably weaker at identifying when the distribution itself has shifted, which is precisely the condition under which translation competence becomes most valuable. The awareness-capability gap documented in algorithmic literacy research is instructive here: knowing that an AI tool exists does not produce the schema-level understanding needed to use it effectively (Kellogg, Valentine, and Christin, 2020). The same gap is likely to appear at the organizational level. Knowing that AI can assist product development does not automatically redistribute the competence that PMs currently hold.

What Actually Gets Eliminated and What Does Not

Rabois is probably right about one specific subset of the PM role. The procedural, routine dimensions of product management - writing tickets, synthesizing user research summaries, maintaining roadmaps in project management software - are genuine candidates for AI substitution. These tasks follow predictable structures, operate on well-defined inputs, and produce outputs that can be evaluated against clear criteria. Hatano and Inagaki's (1986) distinction between routine and adaptive expertise is useful here. Routine expertise, which involves executing practiced procedures in stable contexts, is exactly what large language models replicate most reliably.

Adaptive expertise is a different matter. Adaptive expertise involves recognizing when existing procedures are insufficient and constructing new responses to novel problem structures. This is the competence that high-performing PMs actually deploy when navigating ambiguous market signals, managing cross-functional conflict under resource constraints, or determining which customer complaints represent genuine latent demand versus idiosyncratic noise. Polychroniou, Trivellas, and Baxevanis's work on conflict management across cross-functional relationships identifies precisely this kind of integrative judgment as central to organizational coordination. No current AI system credibly replaces that capacity.

The Organizational Risk Rabois Is Not Accounting For

There is a deeper organizational risk embedded in the Rabois position that receives almost no attention in his framing. If firms act on the prediction that AI eliminates the need for competence intermediaries and systematically eliminate PM roles, they are making a structural bet that will be difficult to reverse. The competence that experienced PMs hold is not stored in documentation. It is held in the person, developed through repeated exposure to edge cases, organizational politics, and cross-functional negotiation. This is exactly the endogenous competence problem I have been developing in my dissertation work: platform and product coordination competencies develop through participation, not prior to it. Once an organization strips out the role that housed that competence, reconstituting it later is not a matter of rehiring. The schema infrastructure that made those workers effective is gone.

Rahman's (2021) analysis of algorithmic control in platform work is relevant here. Rahman identifies how algorithmic intermediation restructures accountability in ways that are not immediately visible to the organizations deploying these systems. The substitution of AI for human coordination intermediaries is likely to produce a similar opacity problem. The failures will not be obvious at first. They will accumulate in the decisions that never got the right translation layer, the product directions that optimized local metrics while missing structural market shifts.

The Actual Prediction

My reading of organizational theory leads to a more conservative prediction than Rabois offers. AI will eliminate the procedural substrate of product management. It will not eliminate the adaptive, schema-translating core of the role. Organizations that conflate these two will face a competence gap that their AI tools will not diagnose and cannot fill. The firms that outperform in the next five years are more likely to be those that redesigned the PM role around adaptive expertise rather than those that eliminated it on the assumption that AI makes translation unnecessary.

References

Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155-170.

Hatano, G., and Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma, and K. Hakuta (Eds.), Child development and education in Japan (pp. 262-272). Freeman.

Kellogg, K. C., Valentine, M. A., and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410.

Polychroniou, P., Trivellas, P., and Baxevanis, A. (2016). Conflict management research and cross-functional relationships: An integrative review and synthesis. International Journal of Strategic Innovative Marketing, 3(2). https://doi.org/10.15556/ijsim.03.02.004

Rahman, H. A. (2021). The invisible cage: Workers' reactivity to opaque algorithmic evaluations. Administrative Science Quarterly, 66(4), 945-988.