The Scarcity That Moves

The Business of Advice: Navigating the New Scarcity of Meaning

The Shift in Knowledge Economy

The business of advice is undergoing a profound transformation due to technology. We are moving from an era characterized by a scarcity of knowledge—the library catalogue era—to a scarcity of assembly in the search engine era. Now, with the rise of AI, we are facing a scarcity of meaning. This shift will lead to the end of the hourly billing model, the career pyramid evolving into an obelisk, the rise of gig work and side hustles, and the emergence of multicollarism.

In 2019, I entered the library at the Palais des Nations in Geneva as a lowly Researcher. The grand shelves were filled with books older than my grandmother. Antique globes and maps displayed borders redrawn by wars and treaties. It was a room that made me feel the weight of accumulated human knowledge. Yet, it also made me feel both relevant and irrelevant.

As I sat down at a desk in that magnificent room, I opened my laptop and spent the day searching legal databases to validate my hypothesis. Scattered across those shelves were extraordinary volumes on diplomatic immunity, the law of the seas, and nuclear non-proliferation. I flipped through a few, dense and brilliant. I thought about the scholars who had argued international law from those pages in the 1970s, drafting conventions and fighting cases. For them, the question which-book-has-the-answer was a form of expertise. Knowing where knowledge lived was the skill. I wondered then, is knowledge quietly becoming obsolete, one search at a time?

Today, in 2026, I operate with a stack of AI tools. A note-taker distills my calls into insights, an agent manages my calendar, and an LLM aids my research. I still wonder the same thing. But, the more things change, the more they remain the same.

Don’t get me wrong. AI is revolutionary. But as technology shifts, the bottlenecks shift too. What remains constant is the underlying human layer beneath the technology. Let me explain.

From Scarcity of Information to Scarcity of Assembly

Google did not kill the library; it relocated the bottleneck. We transitioned from an era of “scarcity of information” to “scarcity of assembly.” The question stopped being “where is the answer?” and became “how do I put the answer together?” Information became abundant, but processing it did not. The functional skill that mattered shifted from retrieval to assembly.

During this transition, notably, the idea of knowledge transformed. What you searched for was still driven by your curiosity, your intellectual formation, and your sense of what mattered. Judgment remained essential. This transition reshaped entire professions—law firms, consultancies, journalism, and academia. It took nearly twenty years for most of them to fully absorb it. Some are still absorbing it.

With cutting-edge AI tools, the bottleneck has moved again, from “scarcity of assembly” to “scarcity of meaning.”

From Scarcity of Assembly to Scarcity of Meaning

With AI tools’ superior power of cognition, the scarcity that remains—arguably the only scarcity that remains—is meaning. The functional skill that matters is shifting from retrieval to assembly to generating meaning. We are transitioning from “where is the answer?” to “how do I put the answer together?” to, at a fundamental level, “what is the answer?”

But what does meaning mean? What is what? To me, meaning is a culmination of human knowledge, judgment, foresight, relationships, and leadership.

When you ask an AI tool how energy is shaping geopolitics today versus when you ask what lessons Japan's response to the sekiyu shokku—the oil shock of 1973—holds for nations navigating a dual energy and data infrastructure crisis in 2026, you receive something entirely different. Asking the right questions and correlating the past, present, and future—that is knowledge. This is not just glorified prompt engineering.

Knowing what this portends for a client, an industry, or a regulatory regime—energy-intensive crypto mining operations and GPU-heavy data centres—industries whose economics are built on the assumption of cheap, abundant power—that is judgment.

Understanding that a government investing in hyperscale AI infrastructure this year is positioning for quantum sovereignty next—that is foresight.

Making tough, subjective decisions, backed by data but driven by values and instinct—that is leadership. A model can generate ten options and rank them by the probability of success. It cannot absorb the consequences of being wrong.

And then there is the aspect no model can replicate—relationships. The conversations, the energy of the room where solutions are born. Not the deck that follows, but the moments just before and after it. The coffee that runs long, the off-the-record questions that don’t make it to training datasets, the trust extended before it was earned. This trust is the hardest infrastructure to build and the most expensive to lose.

Knowledge, judgment, foresight, leadership, and relationships were not rendered obsolete when online search arrived. Their manifestation at work evolved. Similarly, now they are the only bottleneck between information and meaning.

The Economics of the New Scarcity

How does this new scarcity of meaning impact the business of advice—consulting, law firms, policy shops, anyone who has historically sold thinking by the hour?

One won't be prescient in saying that AI has enhanced efficiency across the board. That is common knowledge. In the legal industry alone, 65% of professionals report saving between one and five hours a week using AI tools, with 12% saving six to ten hours. In consulting, a BCG experiment involving roughly 750 participants found 30–40% efficiency gains for junior analysts and 20–30% for experienced staff.

Two things become important. First, on the client side, the hourly billing model is going the way of the library catalogue. The entire advice industry—consulting, policy, regulatory work—was built on the premise that thinking, and the time it takes to put that thinking onto a memo or a report, are the product. Input was the best proxy for result. AI has now disaggregated those two things.

Now, the deliverable is the thinking alone, “meaning,” the “what.” Thinking remains valuable. While the memo or the report are here to stay, the time it takes to generate them is no longer a reliable proxy for the thinking.

However, before the new equilibrium is reached, the market will experience tension. Drastically reduced timelines will mean more pressure on resources. This could even result in declining internal and external appreciation for creativity in standard outputs—the memos and reports. The incentive to think could diminish. It will take some getting used to, to tell human creativity from AI creativity.

Second, as efficiency is enhanced, the harder questions arise: in the short term, how are these organizations and professionals upskilling their teams so that as AI frees up their time, they are equipped to deliver outcomes that actually have meaning?

Long term, the problem is more concerning. Traditional organizations were built like pyramids—judgment at the top, grunt work at the bottom. That grunt work is now being automated. A major 2025 legal market report found firms have reduced the pace of associate hiring and cut the size of summer associate programs.

This matters because the bottom was never just about productivity and grunt work. It was the training ground. It is where knowledge, judgment, foresight, leadership, and relationships were developed.

So, as the bottom of the pyramid thins, what will the path from Associate to Partner look like? How will Associates learn to deliver meaning? And what will promotions really mean—how will the job of a Junior Associate fundamentally differ from that of a Principal Associate?

Rethinking Organization Structures and Professional Careers

Are organization structures and career pathways in for a fundamental redesign? They are certainly being reimagined with AI agents for team members. Brian Armstrong recently restructured Coinbase around what he calls AI-native pods—small teams, sometimes a single person, folding engineering, design, and product into one role. No pure managers. Five layers maximum. Player-coaches who both lead and build.

Some would argue the pyramid is being hollowed again, or becoming more like an obelisk. Others would say it is becoming more amoeba-like, amorphous, and context-dependent.

And what of the employment contract itself?

The pyramid isn’t the only structure being redesigned. The exclusive, full-time, single-employer model of linear, white-collar work may be the next thing to shift. Policymaking globally is recognizing this.

First, erstwhile employees will likely have many non-exclusive part-time jobs. Japan offers an early signal. Facing wage stagnation and an aging population, the Japanese government actively encouraged side hustles from 2018, dismantling the legal barriers that once prevented salaried workers from moonlighting. Today, portfolio careers are normalized. Singapore and the EU are also enforcing rules on gig workers, providing them with pension and injury coverage, decoupling the nature of the contract from the nature of protections.

Second, multicollar work will emerge. With AI making cognition abundant and hardware prestigious, a new category of technical work will take shape. The distinction between conventionally understood white-collar and blue-collar work will dissolve. This new "multi-collar" worker will lie neither in physical labor nor knowledge work alone, but in combining both. In these roles, the collars are not stacked—they are fused.

Third, professionals who emerge in the era of these trends—hollowing pyramids and portfolio careers—will likely fracture into two categories. One will go deeper. These are the true specialists—the embedded professionals whose knowledge, judgment, foresight, leadership, and relationships remain irreplaceable not despite the system, but within it. They are the human-in-the-loop: the ones who power the AI. These will likely stay in the “pyramid” (or obelisk). The other will go wider, in addition to going deeper. With T-shaped careers, these genero-specialists will move fluidly between domains, synthesize across disciplines, and ask the right questions. They will carry their knowledge, judgment, foresight, leadership, and relationships and orbit the pyramids—carrying "meaning" from one room to the next.

Combined, multi-collarism, non-linearism, and non-exclusivism will look like a data privacy lawyer who has spent two years managing power procurement inside a data centre and now advises a government, a climate tech fund, and a law firm—simultaneously, non-exclusively—on keeping their digital infrastructure sovereign. None of them could justify hiring her full-time. All three need her to orbit their pyramids.

The library in Geneva will still stand. The globes will still sit on their pedestals, borders still wrong in ways that matter. Albeit with machines that have all the answers, still waiting on the right question. Waiting for meaning.