Synthetic Users Are Useful. Synthetic Customers Are the Problem.
I was pitched the same idea three times in twelve months. The tool varied. The claim did not: before you commit real budget, test your idea in a safe space.
The offer came packaged differently each time. AI-generated personas. Synthetic customer panels. Digital twins of future users. Beneath the variations, one promise: make your assumptions visible before the spending starts.
I understand the appeal. Most teams run on weak assumptions disguised as analysis. Any system that forces assumption clarity before money gets locked in deserves attention.
So I am not against synthetic users. I am against treating them as customers. That distinction will cost you a round, a market entry, or twelve months you will not get back.
The Preference-Behavior Gap Predates This Technology
The core problem is not AI. It is the distance between what people say and what they do.
In 2001, economists List and Gallet published a meta-analysis of 29 experimental studies on stated versus actual willingness to pay. Participants in hypothetical settings overstated their preferences by a factor of roughly three on average. That finding predates generative AI by two decades. Every research method built on stated intent faces the same structural trap: responses are not behavior.
Language models add a second layer of distance. Horton, Filippas, and Manning at MIT and NBER describe LLMs as “homo silicus,” an implicit computational model of humans that can be given preferences and simulated in economic scenarios. Their 2023 NBER working paper shows that LLM experiments reproduce results qualitatively similar to classic behavioral economics studies.
The same paper notes what economists have long held: the economic content of mere statements is worth far less than the economic content of actual behavior. That critique applies directly to synthetic user outputs.
Bisbee and colleagues sharpened the constraint in 2024. Studying ChatGPT’s ability to replicate human survey data, they found that while LLMs recover average opinion scores with reasonable accuracy, the variance is compressed and the regression coefficients diverge from human benchmarks. For the kind of statistical inference a founder uses when deciding whether to fund, kill, or pivot a product, LLM-generated responses are not reliable substitutes for behavioral data.
The practical consequence: a synthetic user can produce a plausible answer to what a person like this might say. It cannot produce evidence of whether that person will pay, return, or recommend.
The Digital Twin Language Is Doing Real Damage
Every pitch I receive includes the phrase “digital twin.” I think it is the most expensive two words in market research right now.
A digital twin of a machine works because the machine has stable physical properties, measurable states, and causal behavior you can model. A person embedded in a buying situation has none of that. They have constraints, fears, habits, alternatives, social pressure, timing conflicts, and identity concerns that shift faster than any training data can track.
If the idea is new enough to matter, there is no behavioral evidence for the exact situation you want to predict. The model interpolates from adjacent data. It generates plausible reactions. It can surface contradictions. But plausibility is not evidence.
When teams call that output a digital twin, they convert uncertainty into interface design. The tool looks authoritative. The confidence is borrowed. Expensive decisions then move forward on borrowed confidence, and the reckoning arrives when it costs real money to find out.
B2B Breaks the Single-User Frame Entirely
The digital twin problem becomes structural in B2B. There is no single customer. There is a buying system.
Gartner’s research on B2B purchasing puts the average buying group at 6 to 10 decision-makers for complex solutions, each entering the process carrying 4 to 5 pieces of independent research they bring to the group. Forrester’s 2024 State of Business Buying report raises that average to 13 stakeholders, with 89% of purchasing decisions crossing multiple departments.
Buyers spend only 17% of their total purchasing time meeting with potential vendors. The rest happens in internal rooms and conversations a seller never enters.
The user has the problem. The manager owns the outcome. Procurement owns the process. IT owns the risk. Legal owns the contract. Finance owns the budget. An executive sponsor owns the political cover. A middle manager you never encounter may block the decision because the solution reduces their control.
The relevant question is not: would this synthetic user buy? It is: can this solution survive the buying system?
A B2B purchase moves through budget cycles, internal politics, procurement rules, security reviews, switching costs, vendor trust, risk ownership, and the capacity of a sponsor to spend internal capital. Gartner’s 2025 survey of 632 B2B buyers found that 74% of buyer teams experience unhealthy conflict during the decision process. Even when consensus forms, one new stakeholder joining late can dissolve it.
To stress-test B2B demand properly, you would need a synthetic buying committee, a synthetic procurement path, a synthetic political map, and a synthetic risk model. That is not what most tools offer. Even if you built all of it, the output would still be a rehearsal, not evidence.
B2G Adds Another Layer
Public sector makes the synthetic-customer idea even more fragile.
In B2G, public need does not equal institutional ability to purchase. A department may have a genuine problem, a clearly superior product in front of them, and an obvious economic case. None of that moves procurement if the buying path is constrained by public law, tender requirements, multi-year budget cycles, political accountability, data protection rules, and vendor neutrality obligations.
The best product may lose because the tender rewards the wrong specification. The most urgent need may wait because the budget was allocated two years earlier. A successful pilot may never scale because no one can justify the next step inside the formal process.
The question in B2G is never: does the user want this? It is: can the institution buy this without creating legal, political, operational, or reputational exposure? A synthetic user cannot answer that.
A synthetic procurement simulation might help you rehearse the obstacles. That is not evidence of demand. What it does is show you where your go-to-market belief is naive. Useful. Stop calling it validation.
Where Synthetic Users Actually Help
The value is real. The use case is narrower than most pitches suggest.
Synthetic users can force teams to surface assumptions before money is committed. They can generate objections the team has not considered. They can expose where messaging fails to hold. They can simulate how different segments might interpret the same offer and stress-test pricing logic before a real experiment costs real money.
Most teams skip this work entirely. They move from idea to prototype to launch while quietly assuming the user has urgency, the buyer has budget, procurement has no friction, switching is easy, and the competition stays passive. A synthetic-user system can make those assumptions explicit and testable. That is worth something.
The problem starts when synthetic users substitute for real discovery: actual willingness-to-pay tests, real procurement conversations, behavioral evidence from real buyers. At that point the tool stops reducing uncertainty. It makes uncertainty look sophisticated.
Assumption Stress Tests, Not Digital Twins
I would not frame this output as customer validation. I would call it an assumption stress test.
That is less exciting. It is also accurate.
The output should not be confidence. It should be a sharper test plan.
After running synthetic users, a team should know which assumption needs real-world evidence first, which objection must be tested with actual buyers, which stakeholder can kill the deal, which channel assumption is optimistic, and which part of the business model is still belief rather than evidence. That framing changes what the tool is for. It is not an oracle. It is a diagnostic.
The Core Distinction
The mistake is not using synthetic users. The mistake is promoting them to synthetic customers.
In B2C, the risk is mistaking preference for behavior. In B2B, the risk is mistaking user pain for buying power. In B2G, the risk is mistaking public need for institutional ability to act.
Start with the decision system. Who must act? Who must pay? Who must approve? Who must change what they do? Who can block? Who benefits most if nothing changes?
Only after that map is visible does synthetic simulation have a useful job. The real question is not whether an AI-generated persona likes your idea. It is whether the market has a path from interest to action.
That path is where most ideas die. And no synthetic user saying yes has ever built it.
Sources
List, J.A. & Gallet, C.A. (2001). “What Experimental Protocol Influence Disparities Between Actual and Hypothetical Stated Values?” Environmental and Resource Economics, 20(3):241–254.
Argyle, L.P., Busby, E.C., Fulda, N., Gubler, J.R., Rytting, C. & Wingate, D. (2023). “Out of One, Many: Using Language Models to Simulate Human Samples.” Political Analysis, 31(3):337–351.
Bisbee, J., Clinton, J.D., Dorff, C., Kenkel, B. & Larson, J.M. (2024). “Synthetic Replacements for Human Survey Data? The Perils of Large Language Models.” Political Analysis, 32(4):401–416.
Forrester Research (2024). The State of Business Buying, 2024. Forrester Research, Inc.