Gen-AI and the Attacker's Advantage in Real Estate Brokerage

The next office brokerage giant won't win by being cheaper. It will win by selling a product the incumbents can't yet imagine.

The cash register that shouldn’t have won

In the 1970s, National Cash Register made the best electro-mechanical cash register in the world. The brand was a century old. The product was excellent. The sales force was everywhere. And then a much smaller company called Data Terminal Systems built a computerized register, and inside a few years NCR’s position collapsed.

Here is the part that matters. DTS did not win by selling a cheaper version of the same machine. It won by selling a different machine, one that did things the electro-mechanical register fundamentally could not do, and in doing so it created demand that had not existed before. The customer did not walk in asking for a computer at the checkout counter. DTS showed them they wanted one.

This is the case McKinsey partner Richard Foster opens with in Innovation: The Attacker’s Advantage (1986), and even though the work is four decades old, it is the right place to start a conversation about generative AI and the brokerage business, because the lesson is almost always misread. Incumbents do not lose to a discount. They lose to a better product that serves a need the market had not yet learned to ask for.

I think that is exactly what is about to happen in commercial real estate brokerage. But to see why, you have to throw out the version of the AI story that everyone is telling.

The S-curve, read correctly

Foster’s framework is the S-curve. Plot cumulative investment in a technology against what it produces. Early on you spend a lot for almost nothing. Then the curve goes exponential: each incremental dollar buys a large incremental return. Finally you hit the plateau, where the technology approaches its limit and more investment buys less and less.

Foster’s thesis is that companies which stay on a maturing curve too long get displaced by an attacker who jumps to the successor curve, the one with a higher ceiling. By the time the incumbent recognizes the discontinuity, the attacker is already climbing the steep middle while the incumbent is grinding away at the flat top of an exhausted technology. The advantage is structural. It does not come from working harder.

And here is the line in Foster that everyone skips. He argues that the CEO’s primary job should shift from efficiency-seeking to competitiveness-seeking. The successor curve’s advantage is not that it does the old thing cheaper. It is that it does a new thing the old curve could never reach. The ceiling is higher because the product is different, not because the cost is lower.

Why efficiency is the wrong perspective in brokerage

Most of what gets written about AI in brokerage is a cost story. The pitch is that a small team with the right software stack can produce the same offering memorandum, the same broker’s opinion of value, the same market report, with a fraction of the analysts. Henry AI markets on collapsing twenty hours a week of deck production. VTS reports ninety-three percent time savings on proposals. Savills, pointing its data at an OpenAI partnership, claimed brokers hitting ten times their old output. The numbers are real and they are impressive.

They are also, by themselves, a competitive dead end.

Brokerage is not a business with a fat cost structure waiting to be arbitraged. “Back of house” labor is a relatively small share of the economics, and the part that matters most, the producing broker, is generally paid through commission. That means the largest cost is already variable and already matched to revenue: when a deal closes, the cost of the person who closed it scales with the fee. There is no heavy fixed-cost back office whose reduction hands you a significant competitive edge. Cut the analyst hours and you have saved something real, but you have saved it once.

Worse, you have saved something your competitor can save too. The majors are not standing still, and all have spun up some form of AI initiative, at least on paper. If the entire generative AI story in brokerage is “the same deliverable, produced cheaper,” then the incumbents win, because they have the brand, the balance sheet, the errors-and-omissions coverage, and the relationships, and they will absorb the tools and keep their seat. A cost advantage that the other side can copy is not a sustainable advantage.

So the right question is not how to produce the old product for less. It is what new product the technology now makes possible.

A product that looks like the client’s business

Think about what a brokerage actually sells the client today. Market surveys. Comparable transactions. A financial analysis of a lease or a sale. This is the stock-in-trade, and they have two important features. First, they reflect the broker’s view of the market, not the client’s view of its own business. Second, every major firm produces essentially the same thing. It is a commodity, and it sits squarely on the plateau of the old curve. CoStar can add dashboards for another decade and the deliverable does not change in kind. It gets marginally faster and marginally prettier and remains the same product.

Generative AI makes a genuinely different product possible, and the difference is not speed. It is integration across disciplines. The new deliverable stitches together things that used to live in separate silos and separate professions: the client’s financials, its hiring and headcount signals, its operational footprint, its capital plans, its portfolio, and the market around it, into a single piece of analysis that reflects the client’s own business back to it more clearly than the client’s internal teams can see it themselves. It does not wait for the client to articulate a need. It surfaces the need.

These are capabilities that live only on the successor curve. A traditional stack parses structured fields. The new tools read two hundred leases in free text and extract terms no parser was built to anticipate. They synthesize a company’s earnings calls, its filings, the news, and the market data into one narrative. They answer counterfactuals the old systems cannot touch: what does our occupancy cost look like if we consolidate three offices, what happens to our portfolio if we exit downtown, where does our own hiring plan say we will be short of space in eighteen months. No amount of additional investment in the legacy survey-and-financial-analysis product gets you there, because the problem requires a kind of reasoning the old curve does not possess.

That last point answers the obvious objection. You might assert that the legacy stack only *looks* exhausted because the industry stopped innovating and investing in it a long time ago. Then you could argue that these firms could still be in the exponential part of the curve, but have chosen not to invest further.

But the way you settle the argument is to name capabilities the old product cannot reach at any reasonable level of spend, and the cross-disciplinary, client-specific reasoning above is exactly that. This is structural displacement, not a self-fulfilling forecast. The new product does something the old one cannot, full stop.

Why the incumbents won’t build it

If this product is so obviously better, why won’t JLL or CBRE simply build it first? This is the question Clayton Christensen spent a career answering in The Innovator’s Dilemma (1997), and his answer is uncomfortable: they will not build it because they are well managed, not because they are badly managed.

A large firm allocates resources toward the articulated demand of its largest, most profitable clients. That is good management, and it works beautifully for what Christensen calls “sustaining” innovation. It is also precisely what blinds the firm to a latent need in a segment it does not value. The new product I described is, at first, most valuable to clients the majors find marginally profitable to serve, in deals below the economic floor where a full-service firm can justify a custom, cross-disciplinary effort. The rational manager inside the incumbent cannot build a case for it. The market looks too small, the margin too thin, the need too undefined. So the firm cedes the ground, every time, for reasons that look correct on the spreadsheet.

Christensen’s deeper point is that markets like this cannot be analyzed in advance because they do not yet exist. They have to be discovered through fast, cheap, and flexible attempts. The incumbent is built to predict and execute. The attacker is built to discover. Those are different organizations with different reflexes, and you cannot easily turn one into the other.

You can watch the dilemma operate in real time. JLL Spark, the firm’s venture arm, looks for companies and products that will be useful to JLL’s existing client base. That sounds prudent, and it is exactly the trap Christensen describes. It is an engine pointed at what current clients already want, which is the one place disruption never comes from. The disruptive opportunity sits in a different value network, serving a need today’s clients have not voiced, and the incumbent’s own logic steers it away from precisely that.

The modern DTS

So who is the attacker? Not a single firm, and not, despite the funding headlines, the proptech platforms themselves, CoStar, Henry AI, Dealpath, Sytes, qbiq, Crexi, CompStak: these are the picks and shovels. They are necessary, and they are available to everyone, which means owning a subscription is not a strategy.

The attacker is the experienced-broker startup, and the reason is a variable that both Foster and Christensen leave out, because it does not show up in their case studies. In brokerage, the client relationship moves with the senior broker, not the firm. Morgan Stanley is the client of a particular broker, not of the institution on the broker’s business card. When five or ten senior people leave a top-three firm together, the relationships leave with them.

That is the modern DTS. A small group of senior brokers who own their books, organized from day one around the AI stack, building the new cross-disciplinary product and aiming it at the clients and the deal sizes the majors will not economically serve. They establish density in that underserved segment, they refine the product where the stakes are lower, and then they climb up-market as the advantage compounds, which is Christensen’s new-market disruption run by the book. Foster would add that the cleanest path often runs through a hybrid: keep the traditional sourcing and relationship work that already works, and run the new product underneath it, the way ships carried both sail and steam before they trusted steam alone.

Is there a way for legacy brokerages to counter this threat? The answer is yes and it’s contained inside The Innovator’s Dilemma. Spin off teams within the firm with deep client relationships, engineering support, and the freedom to go after the smaller, early-adopter clients. This group will not meet margin goals, initially. The cost of developing a new product in an entrenched industry, for smaller more sophisticated clients, will make the exercise seem unattractive to current management. But that experiment will help find the future of brokerage.

Another reason that legacy management will be slow to make this change is a misunderstanding of the S-curve, and it is reinforced by the way the business press keeps score. The early data coming out of places like McKinsey and MIT shows that adoption of AI is already exponential, with surveys putting it north of ninety percent of businesses, while measured productivity is still nearly flat. Federal Reserve research in early 2026 put the economy-wide lift around 1.3 percent. The Penn Wharton Budget Model does not see the steep part of the curve until the early 2030s.

MIT’s Project NANDA turned that same gap into a headline: ninety-five percent of enterprise generative AI pilots, it reported, deliver no measurable impact on profit and loss. Interpreted in terms of Foster and Christensen, that is not a failure rate. It is a measurement mirage. The report judges a successor technology by a six-month-to-two-year return standard, the same standard that would have branded electricity a failure when it first entered factories, where productivity barely moved until managers reorganized the work around the electric motor. The return was always going to lag the adoption, because the return is the steep middle, and we are still on the early part of the curve.

Two things hiding inside that same gap are easy to miss and matter a lot. The first is the pile of small wins: a finance team that closes the month in days instead of weeks, an analyst who drafts from something instead of from nothing. None of it shows up on an earnings slide, and all of it compounds. The second is the shadow workforce. More than ninety percent of employees already use AI at work even though fewer than half of their employers pay for it. That is not a governance problem to be stamped out. It is the market telling you where the unarticulated demand already lives, which is exactly the signal Christensen says the incumbent’s formal processes are built to filter out and the attacker is built to find.

So the gap between exponential adoption and flat output is not evidence against the thesis. It supports it. The steep middle waits on the complementary work: redesigning the practice, building the new product, reorganizing around it. The incumbent will find it hard to prioritize that work, because it means dismantling the very deliverable it sells today, and because its own scorecard keeps insisting the technology has not paid off. The startup does it on day one, because it has nothing to dismantle and no scorecard telling it to stop.

This is the third leaf of a pattern worth naming. The end of software displaces the software vendors. The great data reckoning displaces the dashboard and BI vendors. And this displaces the full-service broker, by the same mechanism each time: a successor curve replaces a commodity product with a genuinely better one, and the incumbent’s own economics keep it from following.

NCR had the better brand and the better balance sheet, and none of it mattered, because DTS was not selling a cheaper register. It was selling a different one. The brokerage version of that machine is being built right now, and the window, by the Penn Wharton timing, is roughly five to seven years. It is open today.

A few things I am confident about, and one I am not:

1. The cost story is a trap. If your whole AI plan is to do the old work with fewer people, the majors will out-absorb you.

2. The product story is the opening. Integrate data across disciplines into something that reflects the client’s business better than the client can, and you are selling on the new curve.

3. The attacker is people, not platforms. The senior broker who owns the relationship and commits to the new product is the structural threat. The software is just the engine.

4. What I cannot yet tell you is the date. Nobody can. The honest position, straight out of Christensen, is to plan for learning rather than prediction, conserve enough runway to be wrong twice, and start before the curve makes it obvious.

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Bibliography

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Christensen, Clayton M. “Exploring the Limits of the Technology S-Curve. Part I: Component Technologies.” Production and Operations Management 1, no. 4 (1992): 334–357.

Christensen, Clayton M. *The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail*. Boston: Harvard Business School Press, 1997.

Federal Reserve. Research on generative AI and labor productivity, April 2026.

Foster, Richard N. Innovation: The Attacker’s Advantage. New York: Summit Books, 1986.

McKinsey & Company. “Where AI Creates Value (and Where It Doesn’t).” 2026.

MIT Project NANDA. *The GenAI Divide: State of AI in Business 2025*. Massachusetts Institute of Technology, July 2025.

Penn Wharton Budget Model. Projections on AI’s contribution to labor productivity growth. University of Pennsylvania, 2026.

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