LinkedIn cofounder Reid Hoffman is challenging how major corporations approach AI transformation, arguing that companies are focusing on the wrong areas. While businesses rush to hire chief AI officers and establish specialized tiger teams for agentic AI products, Hoffman believes they’re missing where automation truly delivers value—in the “unglamorous layer” of everyday work.
Speaking on his “Possible” podcast with AI engineer Parth Patil, Hoffman emphasized that successful AI transformation requires employees to openly discuss and collectively learn about AI tools. The problem, he warns, is that when workers fear punishment or judgment for using AI, they become what Wharton professor Ethan Mollick calls “secret cyborgs"—individuals who quietly use AI to accelerate their work while their organizations gain no institutional knowledge.
The stakes are high as companies dramatically increase AI investments. Goldman Sachs spent approximately $6 billion on technology in the previous year, with CEO David Solomon expressing he wished the figure was even higher. An RBC Capital CIO survey from December revealed that 90% of organizations plan to increase AI spending in 2026, demonstrating the industry-wide commitment to AI adoption.
However, Hoffman argues that the common strategy of running pilot schemes with small specialist groups and expecting “transformation to magically spread” is fundamentally flawed. “AI lives at the workflow level, and the people closest to the work know where the friction actually is,” he wrote on LinkedIn.
Instead, Hoffman advocates for automation to begin at the coordination layer—encompassing meetings, note-taking, and knowledge management tools. This bottom-up approach focuses on the mundane but essential tasks that consume significant time across organizations.
“The winners will be companies that build the muscle of day-to-day use early enough for the gains to compound,” Hoffman stated on X (formerly Twitter), adding a stark warning: “Start learning now, or watch the advantage slip away.” His message is clear: companies must democratize AI adoption across their workforce rather than confining it to elite teams if they want to realize meaningful transformation.
Key Quotes
If people feel they’ll get punished or judged for using AI, they become what Ethan Mollick calls ‘secret cyborgs,’ who quietly speed up their own work while the organization learns nothing
Reid Hoffman wrote this in a LinkedIn post, highlighting the critical importance of creating psychologically safe environments for AI experimentation. This quote captures the core problem with restrictive AI policies that prevent organizational learning.
AI lives at the workflow level, and the people closest to the work know where the friction actually is
Hoffman made this statement to emphasize why top-down, specialist-driven AI implementations often fail. It underscores his argument that frontline workers, not executive tiger teams, hold the key to identifying valuable automation opportunities.
The winners will be companies that build the muscle of day-to-day use early enough for the gains to compound
Posted on X, this quote encapsulates Hoffman’s central thesis that AI advantage comes from widespread, habitual use rather than isolated pilot projects. It frames AI adoption as a competitive race where early movers gain compounding advantages.
Our Take
Hoffman’s critique exposes a fundamental disconnect between how executives perceive AI transformation and how it actually occurs. The obsession with hiring chief AI officers and creating specialized teams reflects traditional change management thinking that doesn’t apply to AI’s distributed nature. Unlike previous technology waves, AI tools are increasingly accessible to individual contributors, making grassroots adoption more powerful than executive mandates.
The “secret cyborg” phenomenon is particularly revealing—it suggests many organizations already have AI-powered workers, but institutional barriers prevent knowledge sharing. Companies that recognize this and shift toward permissionless innovation cultures will accelerate learning curves dramatically. Hoffman’s focus on the coordination layer is strategically brilliant because it targets universally applicable use cases rather than department-specific applications, creating faster organizational buy-in and measurable ROI.
Why This Matters
Hoffman’s perspective represents a critical inflection point in how businesses approach AI adoption strategy. As companies collectively invest billions in AI infrastructure, the risk of misallocated resources and failed transformations grows exponentially. His argument challenges the top-down, specialist-driven approach that dominates corporate AI strategy today.
The concept of “secret cyborgs” highlights a dangerous organizational dynamic where AI’s benefits remain siloed with individual workers rather than scaling across enterprises. This creates competitive disadvantages for companies that fail to foster open AI experimentation cultures. With 90% of organizations planning increased AI spending in 2026, those that get implementation strategy wrong will waste substantial capital while falling behind competitors who successfully democratize AI tools.
Hoffman’s emphasis on the “coordination layer” is particularly significant because it targets high-frequency, low-value tasks that collectively consume enormous organizational resources. By automating meetings, note-taking, and knowledge management, companies can achieve immediate productivity gains while building workforce AI literacy. This bottom-up approach creates sustainable transformation rather than isolated pilot successes that never scale.