The Business Model Decides What the Technology Becomes
This morning I read a piece on SRF about a team at ETH Zürich that has spent nearly a decade building a chip to fight deepfakes. The chip sits directly on a camera sensor and generates a cryptographic signature the moment an image is captured. Anyone who alters the image after the fact invalidates the signature. Felix Franke, the professor leading the project, says he saw the problem coming ten years ago and decided to work on it rather than wait for someone else. The chip is still a prototype. The problem it addresses is not.
That piece landed differently than most I have read on the subject. Not because the technology surprised me. I have watched deepfakes improve steadily for years, and the trajectory is not subtle. What struck me was the framing: authenticity now needs to be verifiable at the hardware level, because human eyes passed their useful detection threshold some time ago.
My spam folder has been telling me the same story. What used to arrive as obviously fraudulent, misspelled names, impossible offers, transparent pretexts, has gradually become more careful. Some messages now use language calibrated to sound like a colleague, a trusted institution, or a service I actually use. The writing has gotten better. The targeting has improved. The cost of producing a convincing fake has dropped, and the volume has gone up. That combination is not an accident. It is the signature of a business model that found a technology it could use.
The question that tends to get avoided
Whenever deepfakes come up in public discussion, the conversation lands quickly on the technology: how realistic it is, how fast it is improving, whether detection can keep pace, and what regulators might eventually do. Those are reasonable questions. But there is a prior one that gets less attention.
Who owns a person’s face, voice, image, and identity? And who gave anyone else the right to use them for advertising, content, entertainment, fraud, or profit?
The law is genuinely unclear in some places. Enforcement is weak in others. Platforms move slowly, and by the time a policy is updated, the next version of the tool has already changed shape. But the basic question is not that complicated. It is about consent and about who carries the cost when something goes wrong. Creating fake celebrity investment pitches is not a gray area. Cloning someone’s voice to run a financial scam is not a gray area. Generating non-consensual images of real people is not a gray area. The law may be slow. The moral position on those cases was never unclear.
Neutral at the level of physics. Not at the level of the market.
We reach for “technology is neutral” because it feels true and because it lets us avoid harder questions. A camera does not decide whether it documents a family trip or records someone who never agreed to be filmed. A microphone does not decide whether it captures a podcast or a private conversation. An AI model does not decide whether it helps a student understand a concept or helps a fraudster replicate a trusted voice.
At the level of physics, yes. Neutral.
But once someone builds a business model around a technology, the neutrality ends. A business model decides who is served, who pays, who benefits, and who has no say in the matter. It translates capability into incentives. And incentives decide what gets built next, what gets repeated, what gets funded, and what gets scaled. The technology is the capability layer. The business model is the behavior layer. That distinction is what makes the deepfake conversation actually useful.
The productive version of this pattern
We know this mechanism from disruption, and its constructive form is not hard to describe. A new technology lowers the cost of something. A business model forms around that lower cost. The initial offer is simpler, less polished, and less profitable than what established players sell, so they ignore it. The margins are thin. The customers look marginal. The use case seems narrow. The new entrant serves those customers anyway, learns faster than anyone expected, and gradually improves until it reaches a market incumbents actually care about.
Christensen’s work on disruption describes exactly this: a simpler, cheaper, more accessible offer that reaches people who were overserved, underserved, or excluded entirely. The technology alone does not disrupt. The model around it does.
At its best, this serves society in a straightforward way. Lower cost enables a different offer. The different offer reaches people who were previously locked out by price, geography, or the complexity of the incumbent product. They do not need the full package. They need the job done well enough at a cost they can actually afford. That is not a compromise. That is progress.
