Andreas De Neve

Andreas De Neve's central argument is that job titles have become a broken instrument for understanding work. An accountant today carries the same title an accountant carried a century ago, yet the actual content of the job has transformed completely, and De Neve insists this gap between static titles and dynamic tasks is exactly why companies misjudge what AI is doing to their workforce. His fix, embedded in how TechWolf operates, is to break every job into its constituent tasks and classify each one as intrinsically human, augmentable, or fully automatable. The conclusion he draws from this exercise is consistent across his public statements: "Er zijn heel weinig cases waar AI gewoon een job één op één zal vervangen" ³. AI reshapes the inside of jobs far more often than it deletes them outright.

That framing leads directly to his skepticism about the corporate AI-layoffs narrative. De Neve is blunt that a large share of what gets announced as AI-driven restructuring is theater for investors rather than a true reflection of technology replacing labor: "Bij heel grote bedrijven is dat heel veel AI washing" ²³. He backs this with a concrete observation from working with large clients, noting that some Global 2000 firms' latest approved LLM is over six months old, undercutting the idea that these organizations have deeply operationalized AI. He extends the point with an infrastructure analogy, pointing out that roughly 70 to 30 percent of running software still isn't cloud-based today, which he uses to argue the AI transition will unfold over "10-20 years" rather than overnight ²³.

De Neve's own company is his proof case for how automation actually plays out in practice. He describes a striking internal shift at TechWolf: "Bijna niets van code wordt nog door mensen geschreven, maar dat wil gewoon zeggen dat er heel veel meer code is om door mensen te laten reviewen" ². Rather than shedding engineers, the company kept its team of 40 and redirected their effort toward review and coordination as output multiplied. He reaches for a rural metaphor to generalize the lesson: "Maar de stieren hebben de boer ook niet vervangen hè, Tim" ²³. Machines amplify capacity; they don't automatically eliminate the people directing them.

On talent and seniority, De Neve is willing to overturn conventional hierarchy. He has watched TechWolf's own hiring process produce counterintuitive results: "Soms hebben wij schoolverlaters die betere cases maken dan mensen met 15 jaar ervaring" ²³. He generalizes this into a broader claim that someone with three years of experience paired with strong AI skills can outproduce a twenty-year veteran who doesn't use the tools. This is where his "world belongs to the adaptable" thesis crystallizes most sharply: "De wereld is aan de mensen die zich kunnen aanpassen. Dat is puur Darwin. De regels van het spel zijn herschreven en er gaan winnaars en verliezers zijn" ²³. Notably, his reading of this dynamic isn't static doom for junior workers. He argues the first, obvious conclusion, that AI does entry-level work so companies should hire fewer juniors, may flip once firms realize that junior talent plus AI can perform mid-level work, pointing to IBM's plan to triple graduate hiring in 2026 as an early signal of that reversal ²³.

De Neve is also careful to separate volume of work from quality of experience. He argues AI is inflating cognitive load rather than compressing hours, so people accomplish more but finish the day mentally spent, a state he simply calls "brain fry" ²³. This nuance matters to how he frames the ultimate stakes of the AI transition. Rather than worrying about aggregate job losses, he reframes the real question: "Ik ben niet bezig met gaat er nog werk genoeg zijn voor iedereen? Ik denk dat er nog werk gaat genoeg zijn voor iedereen. De vraag is gaan we er allemaal beter van worden?" ³. For him, the historical base rate on labor markets absorbing technological shocks is reassuring, but whether that absorption improves people's daily experience of work is genuinely open.

Underneath all of this sits a business narrative he's candid about. De Neve credits macro disruption, COVID, supply chain crises, talent shortages, and now the LLM wave, as the actual engine of demand for TechWolf's skills data, saying plainly that without change in the world there's no transformation to sell into ²³. He also tracks the company's growth as evidence the market is validating this thesis at scale: "In de laatste 18 maand zijn we eigenlijk van minder dan een miljoen omzet in de US naar meer dan 10 miljoen gegroeid" ²³, and reflects on how the scale of deals has changed over time, "nu vijf jaar later zitten wij continu in dezelfde situatie, maar gaat over 100 of honderden miljoen dollar" ²³. Despite frequent acquisition interest, which he attributes to being an API-first AI platform other companies want to bolt onto their own weaker capabilities, his stated position on independence is unambiguous: "Elk jaar hebben wij aanbieding gekregen om het bedrijf te verkopen. Maar dat is niet waarom dat een bedrijf start" ².