Simulation and the S-Curve
The product is not only the tool. It is the reality people begin behaving inside.
Every disruptive technology changes two things at once. It changes what can be done, and it changes what people believe the world is.
The first change is the familiar one. A technology crawls, then runs, then becomes background. This is the S-curve: early awkwardness, sudden compounding, then maturity. At the bottom of the curve the thing is expensive, unreliable, strange, or too narrow to matter. Then some constraint breaks. Cost falls. Performance rises. Infrastructure appears. Users stop needing an explanation. The curve steepens.
But there is another curve underneath it, less measurable and often more decisive. It is the curve by which the representation of the technology becomes more important than the technology itself. The thing enters imagination before it fully enters practice. A model of the future begins to organize behavior in the present.
That is where Baudrillard becomes useful.
The map starts giving orders
In Simulacra and Simulation, Baudrillard is not just saying that copies are fake. He is pointing at a stranger reversal: societies begin to live through signs, models, images, dashboards, forecasts, feeds, and simulations that no longer merely describe reality. They produce the reality people act inside.
The map stops pointing to the territory. The map starts giving orders to the territory.
That is not abstract French theory when you are building products. It is the demand side of disruption. A technology becomes disruptive when its simulation becomes socially operational: when people, companies, regulators, investors, children, schools, and competitors behave as though the new world has already arrived.
The S-curve tells you when capability is becoming possible. Baudrillard tells you when the simulation is becoming real enough to live in.
Memory as manufactured terrain
I keep coming back to memory production because it makes the overlap visible. Memory used to feel like an interior human faculty. Then cameras, storage, search, social feeds, recommendation engines, and now AI moved up their own S-curves. Memory became external, searchable, ranked, summarized, enhanced, and re-authored.
Photos do not simply preserve a moment. They teach us which moments are preservable. Feeds do not simply show the past. They decide which past returns. Search histories do not simply record desire. They become an index of the self. AI summaries do not simply help us remember. They compress experience into a narratable shape.
A memory product, then, is not just a tool for recall. It is a machine for producing the conditions under which recall will happen. It decides what can be found, what will be emotionally available, what will be forgotten because it was never surfaced, and what will become true because it was repeated in the right format.
This is the Baudrillardian part. The archive stops being a neutral shadow of life. It becomes one of the surfaces life is performed for.
The two curves
Product builders usually learn to watch the first curve: cost, latency, accuracy, storage, bandwidth, adoption, density, manufacturability, regulation, margins. This is necessary. If the capability curve is not steep enough, the product is theatre.
But the second curve matters just as much: expectation, status, fear, imitation, institutional response, new rituals, new taboos, new defaults. This is the simulation curve. If it is not steep enough, the product may work and still fail to matter.
The best products sit where the two curves cross. The capability has become just good enough, and the simulation has become vivid enough that users are ready to reorganize a behavior around it.
The raw technology asks: what is now possible? The simulation asks: what will people now behave as if true?
Disruption ripples from that second question.
What actually gets disrupted
The car did not only make transportation faster. It simulated freedom, suburbia, adolescence, logistics, distance, and status. It changed where people could live and what adulthood looked like.
The smartphone did not only combine a phone, a camera, and a computer. It simulated presence. Everyone became reachable, locatable, photographable, interruptible, and socially visible. The device was a slab of glass; the disruption was the new reality of permanent addressability.
AI does not only automate tasks. It simulates expertise, companionship, writing, judgment, memory, creativity, and agency. The disruption is not merely that a paragraph can be generated. It is that the presence of an apparently competent mind becomes cheap, ambient, and callable.
So the product question cannot stop at: what can this model do? It has to ask: what role does this model simulate, and what happens when that role becomes abundant?
How to build with both principles
For any disruptive technology, I would map two surfaces.
First, the capability surface. What is improving? What is getting cheaper? Which constraint is breaking? What failure mode still makes the product feel fake? What has to become boring before the product can become normal?
Second, the simulation surface. What story are users already telling about the technology? What status does it create? What anxiety does it create? Which institution does it quietly embarrass? Which job title does it turn into a costume? Which private behavior does it make public? Which memory does it manufacture?
The first surface tells you what to build. The second tells you where the ripples go.
A weak product chases the capability. A strong product understands the new simulation and gives people handles for living inside it.
The builder's discipline
This does not mean products should become cynical machines for manipulating perception. It means the opposite: builders need to take perception seriously enough to be responsible for it. When a product changes what people treat as real, the product has entered the architecture of memory, trust, and behavior.
The S-curve is the physics of the disruption. Simulation is its weather. One tells you where the energy is building. The other tells you where it will blow.
The companies that win a technological wave are not always the first to possess the capability. They are the ones that understand what the capability will make people expect, fear, remember, perform, and believe.
The machine becomes powerful enough on one curve. The world becomes ready to believe it on the other. Product strategy lives between those two moments.