AI & The Future of Expert Work
A grounded, forward-looking point of view on how AI is reshaping specialist consulting — and where the real opportunities lie.
What Is Actually Changing
AI is genuinely, measurably displacing routine cognitive work. This is not hype. Tasks that require assembling known information, generating standard artifacts, or pattern-matching against large corpora are being compressed — in time and cost — by an order of magnitude. Pretending otherwise would be both intellectually dishonest and strategically dangerous.
I work with junior devs and observe on a daily basis how programming is becoming a question of how to formulate things (aka vibe coding) and dispatching tasks, rather than a learned skill or API expertise. Especially UI development is dramatically affected, which includes graph and data visualization. The reason customers contacted me is quickly fading away and replaced by a chat with Claude. I hear innovators and entrepreneurs spending sometimes thousands a month on AI rather than on consulting.
For myself, I experience AI with awe and dispeair in equal measures. The excitement to see AI doing things in a few minutes is also the reason a customer does not need my (UI) skills anymore.
The Ikea effect is immense, coding and advice is becoming a commodity and there is an uneasy sentiment in the consulting market.
The market for assembling known facts is contracting. The market for structuring unknown problems is expanding.
The critical distinction is between execution work — doing things we already know how to do — and sense-making work — figuring out what needs to be done, why, and whether the result is trustworthy. AI is collapsing the cost of the former while raising the premium on the latter. There is a polarization.
What AI Systematically Cannot Replace
AI systems are powerful interpolators. They synthesize within their training distribution extremely well. But several categories of expert value remain structurally outside that reach:
| Capability | Why It Endures |
|---|---|
| ✤ Ill-posed Problem Framing | Clients rarely arrive with clean problem definitions. Recognizing what the real problem is — beneath the stated one — is a deeply human, contextual skill. |
| ✤ Judgment Under Ambiguity | When data is sparse, stakes are high, and the answer is “it depends” — clients need a trusted human to take a stance and own it. |
| ✤ Cross-Domain Synthesis | Connecting physics, software architecture, and business strategy in a single coherent insight is where deep specialist generalists thrive. |
| ✤ Relational Trust | Complex decisions are rarely signed off on without a person behind the recommendation. Authority and accountability remain human properties. |
| ✤ Domain Taste & Standards | Knowing what “good” looks like in graph modeling or knowledge architecture requires years of calibrated exposure — not retrieval. |
| ✤ Verification & Validation | As AI-generated outputs proliferate, someone must audit them. The demand for expert review is growing, not shrinking. |
AI as a Force Multiplier for Specialists
The more productive frame is not “AI vs. experts” but AI-augmented experts vs. unaugmented experts. The productivity differential between those who can direct AI precisely and those who cannot is widening rapidly.
The risk is not being replaced by AI. The risk is being replaced by another expert who uses AI better than you do.
Four concrete moves follow from this framing:
Compress routine deliverables. Reports, summaries, code scaffolding, documentation — tasks that once took days now take hours. This frees capacity for higher-value engagement.
Scale proprietary knowledge. Domain expertise becomes a durable moat only when embedded in systems, workflows, and products — not just in a person’s head. AI makes this extraction tractable.
Shift from time-billing to outcome-pricing. When execution time falls, the old hourly model erodes. The new model prices the value of the outcome — insight, decision quality, risk reduction — not the hours spent.
Build AI-native products around expertise. The highest-leverage play: encode specialist knowledge into a product or platform that scales independently of billable time. This is the transition from practitioner to product company.
Where Graph Expertise Is Uniquely Defensible
The graph domain sits at a particularly interesting intersection. As AI systems grow more capable, their own internal reasoning is increasingly structured as graphs — knowledge graphs, semantic networks, entity stores. The infrastructure to build, validate, visualize, and query these structures requires expertise that is sparse and deeply technical.
| Capability Area | AI Exposure | Expert Premium | Trajectory |
|---|---|---|---|
| Graph schema / ontology design | Medium | High | ↑ Growing |
| Graph architecture | Low | Very High | ↑ Growing |
| Document → knowledge graph pipelines | High | High | ↑ Growing |
| Graph ML / analytics | Medium | High | → Stable |
| Generic report writing | Very High | Low | ↓ Declining |
| Standard code generation | Very High | Low | ↓ Declining |
As a person or as a company, if you invest considerable amounts of time and money in something or if the strategic position of your product needs consideration you will want to talk to a human being. If you have a terminal disease, you want to talk to a person and not AI. Trust needs eye contact and a hand-shake. People are complex beings and the things that matter are always a combination of ratio, emotions and intuition. Many challenges don’t have a clear formulation or answer and dealing with the undefined aims (and the things between the lines) is what makes consulting and human contact as important as ever.
Personally, I think business is about people. Life is about people. Having a coffee or a beer with AI is not around the corner and the covid years have proven we are more than information processors.
Three Moves (Still) That Matter
Productize
Convert expertise into repeatable, scalable products. Every engagement that requires the same deep knowledge is a product waiting to be built. The goal is revenue that does not require proportional time investment.
Specialize Deeper
Generalism is more exposed. The more specific and verifiable your domain mastery, the less substitutable you are. Breadth is available via AI; depth is not. Go narrow and go deep.
Adopt Aggressively
Use AI across every workflow. The goal is to work at a pace and scope that was previously impossible for a one-person operation. The one-person firm that builds AI-augmented products is more competitive today than a five-person firm that doesn’t.
The small consulting firm that builds AI-augmented products is more competitive today than a five-person firm that doesn’t. This is a structural inversion worth taking seriously.
A Considered, Positive Stance
The framing of “AI replacing skills” is both true and misleading. True, in that a specific set of execution-oriented tasks are now cheaper and faster to produce. Misleading, in that it obscures what is growing in value: judgment, synthesis, accountability, and the ability to direct AI toward problems that actually matter. Talking matters, trust comes with communication and time.
For deep specialists — particularly those who can reason across technical and business domains — this is one of the more favorable environments in a generation. The barriers to building products have dropped. The demand for structured, trustworthy knowledge systems is rising. The combination of deep expertise and AI leverage is genuinely rare. The threshold to build amazing things has lowered. If you are creative and/or innovative these are golden times to go where no one has bone before.
The professional attitude for this moment is neither dismissal nor alarm. It is clear-eyed adaptation: understanding precisely which parts of your work are being commoditized, doubling down on what is not, and using the tools available to operate at a scale that was previously inaccessible.
Paths are made by walking.