I’m an enterprise consultant: Here’s what you don’t know about AI-related layoffs

Source: Business Insider

Published: 2026-07-19

Entity Analyzed: Enterprise AI Implementation Workforce

URL SCAN

Max Votek, cofounder of a 1,000-person consulting firm that implements AI for Fortune 500 companies, reveals that executives are rarely replacing people with AI. Instead, they are using AI to explain restructuring decisions they already planned. The public misunderstands where AI savings go: token costs, AI licensing, and secure internal systems are consuming the ‘savings.’

The Triage

This is not a confession. It is a positioning statement from a service provider who profits from the AI transition he claims to deconstruct. Votek’s 1,000-person firm exists because Fortune 500 companies pay for AI implementation advice. He is not an outsider observing the game—he is a player with a seat at the table, and his seat depends on the table remaining full of clients who believe AI is complex enough to need consultants. The ‘image problem’ he identifies is real, but his diagnosis is self-serving. When he says companies are ‘AI washing’ layoffs, he is correct. When he says the savings are being consumed by infrastructure costs, he is revealing the mechanical truth that his own business model depends on obscuring: the AI transition is a cost center, not a cost saver, and the layoffs are not the result of efficiency gains but of capital reallocation away from labor toward compute and consulting fees.

The Autopsy (with DT-LAG)

Mechanical Collapse Point

The mechanical collapse is not ‘AI replacing workers.’ It is the collapse of the narrative that AI creates savings. Votek documents ‘token maxxing’—companies burning through AI budgets in months while failing to achieve productivity gains. The ‘savings’ are illusory: they are being consumed by token costs, AI licensing, and the internal infrastructure required to keep proprietary data out of public LLMs. The layoffs are not being funded by AI efficiency. They are being funded by redirecting payroll budgets toward AI infrastructure, consulting fees, and compute costs. The mechanical reality is that companies are paying more to do less, and the workforce is absorbing the cost. When Votek says employees can learn new AI skills in ‘just a few weeks,’ he is inadvertently documenting the collapse of skill premiums: the barrier to entry for AI-augmented work is so low that it no longer commands a wage premium. The ‘adaptability’ he celebrates is actually the commodification of the workforce.

Lag-Weighted Social Timeline

The lag is 12-18 months. The ‘AI washing’ narrative will become visible when the ROI fails to materialize. Companies that laid off workers to ‘fund AI’ will face the moment when their AI budgets are exhausted and their remaining workforce is smaller, less experienced, and unable to execute the business processes that the AI was supposed to augment. The first wave of ‘AI regret’ will manifest as rehiring at premium rates, outsourcing to the same consultants who sold the transition, and public relations campaigns about ‘human-centric AI.’ The 86% of adults who believe AI savings should lower prices will realize the savings never existed in the first place.

Lag Factors

Consultant Dependency: Firms like Votek’s create a self-reinforcing cycle: the more complex AI appears, the more consulting is needed, and the more consulting is needed, the more complex AI appears. This is a lag factor because it delays the social recognition that AI implementation is not delivering promised returns.
Narrative Inertia: The ‘AI is coming’ narrative has been deployed for three years. The public and workforce have internalized it as inevitable. This inertia prevents the social recognition that the ‘inevitable’ is actually a capital reallocation dressed in technological inevitability.
Stock Market Valuation: Companies that announce AI investments see stock price increases regardless of implementation success. The market rewards the narrative, not the results. This creates a perverse incentive to maintain the AI story even as the underlying business deteriorates.
Token Cost Opacity: The public does not understand token costs, model licensing, or infrastructure requirements. This opacity allows companies to claim ‘savings’ while actually increasing costs. The lag is the time it takes for financial analysts to trace the actual cost structure.
Compensation Theater: Votek’s suggestion that companies should ‘publicly disclose how AI savings are being used’ is a demand for transparency theater. Even if companies disclosed the ledger, the numbers would be meaningless without context on what the AI was supposed to deliver versus what it actually delivered.

Defensive Moats

Regulatory Armor: Compliance mandates, data protection requirements, and industry-specific regulations create a need for human-in-the-loop documentation and audit trails. But as Votek notes, companies are building internal AI infrastructure to automate compliance itself. The moat becomes the bridge.
Trust Shield: The ‘human touch’ argument is eroding, but client relationships and institutional knowledge still matter. However, Votek’s own data suggests that employees can learn new AI skills in ‘just a few weeks’—meaning the moat is shallow and can be crossed quickly. The ’10x engineer’ mythology is already collapsing under the weight of AI-augmented mediocrity.
Physical Chains: Enterprise relationships, vendor contracts, and geographic presence slow the transition, but they do not stop it. The consulting industry itself is being hollowed out by the same AI tools it deploys. Votek’s 1,000-person firm is already a target: if AI can teach consultants new skills in weeks, it can replace the consultants themselves.

Future-Proofing Scorecard

| Timeline | Score | Commentary |
|———-|——-|————|
| 1 year | 3/10 | AI implementation continues. Layoffs accelerate. Token costs remain hidden. The ‘savings’ narrative persists. Consultants like Votek profit from the opacity. |
| 2 years | 1/10 | Token cost reality becomes visible. Companies that laid off to ‘fund AI’ face budget crises. Rehiring begins at premium rates. The ‘AI washing’ narrative collapses. |
| 5 years | 0/10 | The enterprise AI consulting model has collapsed. AI-native tools are self-implementing. The middle layer of ‘AI implementation’ is gone. Votek’s 1,000-person firm is either a software company or it is gone. |
| 10 years | 0/10 | The concept of ‘enterprise AI consulting’ is obsolete. AI systems configure themselves, and the only remaining human role is liability absorption. The consultants who survived are the ones who built the AI that replaced them. |

The Verdict

The article is a consultant’s attempt to position himself as a truth-teller while profiting from the lie. Votek correctly identifies AI washing, token cost delusion, and the transparency gap. But he misses—or strategically avoids—the larger mechanical reality: the AI transition is not about replacing workers with machines. It is about replacing stable employment with a volatile, capital-intensive infrastructure that consumes more resources than it saves, while enriching the consulting and compute layers that sit between the enterprise and the AI. The workers are not being replaced by AI. They are being replaced by a financial architecture that uses AI as its justification. The verdict: the layoffs are not the end of work. They are the beginning of a new cost structure where human labor is treated as a variable to be optimized, AI infrastructure is treated as a fixed cost to be maximized, and the difference is captured by consultants, cloud providers, and shareholders. The ‘image problem’ is not that AI is misunderstood. It is that AI is understood perfectly well by the people who profit from it, and they have no incentive to correct the record.