IS THERE A MARKET FOR THE TECHNOLOGY?
When I originally wrote “Is There a Potential Market for the Technology?”, market research was largely a process of finding information—often slowly and at considerable cost—and then using that information to decide whether an opportunity justified further investigation.
The fundamental question has not changed: is there a real market for what the technology enables?
What has changed dramatically is the speed at which technology, competitors and markets now move, and the extraordinary amount of information available to us. AI can compress days or weeks of preliminary research into hours, but it can also produce convincing answers from incomplete, outdated or incorrect information.
For this 2026 edition, I have therefore retained the original disciplines of market analysis, feasibility assessment and Snapshot Market Research, while shifting the emphasis from simply finding information to verifying evidence, testing assumptions with customers, exercising judgement and learning quickly. The central principle is simple: information has moved from scarcity toward abundance; verification, judgement and speed of learning have become the scarce resources.
TECHNOLOGY ANALYSIS: KEY TO MARKET SUCCESS
Businesses seek growth in several ways.
They strengthen their position in existing markets. They enter new markets. They develop new products.
And they use new technologies to create opportunities that previously did not exist.
What has changed dramatically is the speed at which technology, competitors and markets now move. AI has accelerated all three.
A technically impressive product is not necessarily a commercial opportunity.
A good technology is not necessarily a good business.
Technology creates value only when it solves a problem for somebody willing and able to pay for the solution.
Therefore, after asking: How good is the technology?
we must ask: Is there a market for what the technology enables?
And increasingly: Will that market still be attractive by the time we get there?
THE MARKET TEST
Market and feasibility studies can become complicated very quickly.
A useful decision framework should simplify complexity, not add to it.
For a technology opportunity, reduce the market question to five fundamentals: Problem. Customer. Economics. Defensibility. Timing.
Together, these form the Market Test.
PROBLEM
Start with the problem rather than the technology.
What problem does the technology solve? How important is it? How is it solved today? What does the present solution cost? What happens if the customer does nothing?
Our most important competitor is often not another technology. It is doing nothing.
A technology can be significantly better and still fail commercially if the customer does not consider the problem sufficiently important to justify changing.
The more painful, expensive, frequent or strategically important the problem, the stronger the potential market.
CUSTOMER
Who actually has the problem? Who uses the product? Who specifies it? Who recommends it? Who approves it? Who pays for it? And who can prevent the purchase?
In business markets, these may all be different people. Understanding the entire buying chain is critical.
Identifying the person who can say no may be as important as finding the person who wants to say yes.
Do not define customers simply by industry or company size.
Understand the people making the decision, what motivates them, what risks they perceive and what would cause them to change from their present solution.
MARKET STRUCTURE
Define the market around the problem being solved rather than around our technology.
If we manufacture a better mousetrap, our market is not necessarily the mousetrap industry.
Our competitors may include poison, pest-control services, electronic devices, building design, biological controls—or simply tolerating the mice.
The same applies to technology businesses.
A competitor may be another technology, a different process, a service, an employee performing the task manually, software already owned by the customer—or doing nothing.
Customers do not necessarily buy technologies. They buy solutions.
Understanding the market therefore means understanding every reasonable way the customer can solve—or avoid solving—the problem.
MARKET POTENTIAL
Entrepreneurs frequently describe opportunities by saying: “This is a $50 billion market. If we capture only one percent…”
One percent of a market is not a strategy.
The more useful question is: What portion of the market can we realistically reach and serve profitably?
Consider current demand, potential demand, market growth, customer concentration, geography, industry structure, regulation, distribution and adoption rates.
Then segment the market. Which customers have the strongest problem? Which can be reached most easily? Which are willing to adopt new technology? Which offer the best economics?
The theoretical total market may be enormous.
The market that matters initially is the market we can actually enter and serve profitably.
The objective is not necessarily to find the largest market.
It is to find a market we can enter, serve and defend profitably.
COMPETITION
Identify competitors before committing significant resources. Study their products, customers, technology, pricing, distribution, strengths, weaknesses, intellectual property, market position and likely future strategy. But do not limit the analysis to competitors already visible.
Ask: Who could enter if this market becomes attractive?
AI makes this question particularly important.
Capabilities that once required years of specialized development can sometimes be reproduced or substantially improved much more quickly.
Therefore, consider not only today’s competitive environment.
Consider the competitive environment created by our success.
If we demonstrate an attractive market, what will existing competitors do? What will new competitors do? What will large companies with greater resources do?
Competitors do not remain stationary while we execute our business plan.
MARKET ENTRY
What must we possess before we can compete?
Technology? Capital? People? Manufacturing? Data? Distribution? Customer relationships?
Certifications? Regulatory approvals? Intellectual property? Cybersecurity? Brand? Trust?
Some requirements can be purchased quickly. Others take years to develop. This distinction matters.
Money can buy computing power quickly. It cannot necessarily buy ten years of customer trust.
Identify the barriers before entering the market.
Then determine whether those barriers work for us or against us.
ECONOMICS
A market is attractive only if we can serve it economically.
Understand the value of the problem to the customer. What does the existing problem cost? How much money does our solution save? How much revenue can it create? How much time does it save? What risk does it reduce? What alternatives exist?
Then ask how much of that value we can capture while leaving the customer with a compelling reason to buy.
Price is ultimately a division of value between seller and customer.
Cost remains important. But price should not simply be production cost plus a predetermined markup.
If our product creates $100,000 of value for a customer, its manufacturing or computing cost alone does not determine what it is worth.
Conversely, an expensive technology that creates little customer value may have no viable price at all.
If we cannot explain the value, we will eventually compete on price.
DEFENSIBILITY
Suppose the product works. Suppose customers want it. Suppose the economics are attractive.
Then ask: What happens when everybody else notices?
How do we retain enough of the market to justify the investment required to create it?
Protection may come from patents, trade secrets, proprietary data, specialized know-how, software, regulatory approvals, distribution, customer relationships, brand, network effects, switching costs or speed.
Usually, the strongest businesses combine several.
AI creates an additional test: What remains unique when competitors have access to the same technology?
If the answer is nothing, the opportunity may still produce revenue.
But its competitive life may be short.
TIMING
Timing has always mattered in technology. In 2026 it matters even more.
Enter too early and the market may not be ready. Customers may not understand the problem.
Infrastructure may be missing. Costs may be too high. Regulations may be unresolved.
Enter too late and competitors may already control the customers, distribution and standards.
Therefore ask: Why now?
What has changed that makes this opportunity possible today?
Technology? Cost? Customer behaviour? Regulation? Infrastructure? Demographics? Competitive weakness? AI?
And how long is the window likely to remain open?
The objective is not simply to identify a good opportunity.
It is to identify a good opportunity at the right time.
A QUESTION OF DEGREE
Market analysis does not have to begin with an expensive consulting study.
Start with the questions capable of killing the opportunity.
Is the market large enough? Is the problem important enough? Can we reach the customer?
Can we make money? Can we overcome regulatory requirements? Can we defend the position?
If the answer to one of these questions is clearly no, STOP.
Do not spend $100,000 answering a question that $5,000 could have killed. And do not spend $5,000 if five telephone calls can expose the fatal assumption.
If the opportunity survives the first screen, investigate further.
Technical feasibility. Market feasibility. Intellectual-property position. Customer evidence.
Regulatory requirements. Financial feasibility. Increase the investment in information as confidence in the opportunity increases.
This progressive approach preserves capital and optionality.
The objective is not to eliminate uncertainty. That is impossible.
The objective is to identify and reduce the most important uncertainty sufficiently to make an intelligent decision.
SNAPSHOT MARKET RESEARCH
When I originally developed the concept of Snapshot Market Research, the objective was to gather enough technical and commercial intelligence in a short period of time to decide whether an opportunity warranted further investigation.
The principle is even more relevant today. What has changed are the tools.
Online databases. Patent searches. Company websites. Scientific publications. Regulatory records.
Online marketplaces. Customer reviews. Industry forums. Social media. And now AI.
A preliminary market map that once took days or weeks can sometimes be assembled in hours.
But speed introduces a new danger.
Faster information is not necessarily better information.
AI can summarize incorrect information beautifully.
It can repeat outdated claims. It can confuse companies and technologies. It can produce plausible conclusions from weak evidence.
It can even provide references that do not support the conclusion.
Therefore: Use AI to accelerate research. Do not outsource judgement to it.
THE 2026 SNAPSHOT
A rapid market assessment should answer five questions.
1. IS THE PROBLEM REAL?
Who has the problem?
How serious is it?
How is it solved today?
What does the existing solution cost?
What happens if the customer does nothing?
2. IS THE TECHNOLOGY MEANINGFULLY BETTER?
Better may mean lower cost, higher performance, greater speed, more convenience, higher reliability, improved safety, lower energy consumption or reduced labour.
If the difference cannot be measured or explained clearly, customers may not care.
3. WILL SOMEBODY PAY?
Identify actual potential customers.
Talk to them.
Distinguish: “That’s interesting.”
from: “When can we buy it?”
They are not the same thing.
4. CAN WE REACH THE CUSTOMER?
What is the sales channel?
How expensive is customer acquisition?
How long is the sales cycle?
Who controls access to the customer?
A superior technology without a route to the customer may simply be an interesting invention.
5. CAN WE DEFEND THE POSITION LONG ENOUGH TO MAKE MONEY?
Consider patents, trade secrets, data, know-how, distribution, brand, regulatory position, customer relationships, network effects and speed.
Then apply the AI test: What happens if today’s advanced capability becomes cheap and generally available?
FROM INFORMATION TO EVIDENCE
Perhaps the biggest change in market research is not AI.
It is our ability to move quickly from information to evidence.
Desk research can tell us who the competitors are, what products exist, what companies claim, what patents have been filed, what customers say publicly, what regulations apply and how the industry appears to be structured.
But desk research cannot prove that a customer will buy our product. Eventually we must confront the market.
A useful sequence is: Research → Hypothesis → Customer → Evidence → Decision
Use research and AI to form the hypothesis.
Use customers and industry participants to test it.
Then update the hypothesis.
Repeat.
This is the modern learning loop.
THE DANGER OF PLAUSIBLE INFORMATION
Businesses have always made bad decisions because of mistaken assumptions, incomplete evidence and confirmation bias.
AI adds another danger. It can make a weak assumption look professional.
A beautifully written market report can still be wrong.
Therefore separate: Facts. Assumptions. Estimates. Opinions. AI-generated conclusions.
For important decisions ask: What is the evidence? How current is it? Where did it come from?
Can we verify it independently? What evidence would prove us wrong?
The purpose of research is not to confirm what we already believe.
It is to discover whether we should believe it.
TALK TO THE MARKET
Technology entrepreneurs naturally spend time talking about technology.
Spend equal time talking to customers.
Ask: What frustrates them? What takes too long? What costs too much? What do they repeatedly work around?
What risks concern them? What would cause them to change supplier?
What would prevent them changing? Observe behaviour as well as listening to answers.
Customers may say they want innovation while continuing to buy the familiar solution.
A workaround is often evidence of an unmet need.
A purchase is stronger evidence than an opinion.
SPEED OF LEARNING
In the 1990s, access to information itself could create competitive advantage.
In 2026, information is abundant.
Reliable information is not.
AI can search, summarize, compare and generate information at extraordinary speed.
The competitive question has therefore shifted.
It is no longer simply: Can we find the information?
It is: Can we determine what is true, what matters and what we should do about it?
Information has moved from scarcity toward abundance.
Verification, judgement and learning speed have become scarce.
The old sequence was: Information → Knowledge → Strategy → Creation
The 2026 sequence is: Information → Verification → Knowledge → Judgement → Action → Learning
Information begins the process.
Verification separates evidence from noise.
Knowledge places the evidence in context.
Judgement determines what matters.
Action tests the judgement.
Learning improves the next decision.
The real competitive advantage is speed of learning.
TWO TESTS—ONE DECISION
Chapter 2 asked whether we had an attractive technology opportunity.
Its test was: People. Protection. Price. Pace.
This chapter asks whether there is an attractive market.
Its test is: Problem. Customer. Economics. Defensibility. Timing.
Together, the two frameworks provide a practical bridge from technology to commercial opportunity.
A wonderful technology without a market remains a technology.
A wonderful market without the capability to exploit it remains somebody else’s opportunity.
The objective is to bring the two together.
KNOW WHEN TO PLAY
We do not need perfect information before making a decision.
We need enough reliable information to understand the important risks.
Ask: What do we need to know next?
Then: What is the cheapest and fastest credible way to learn it?
Sometimes the answer is a comprehensive market study.
Sometimes a patent search.
Sometimes an AI-assisted analysis.
Sometimes five conversations with potential customers.
Do not commission research simply because research feels like progress.
Research should change a decision.
Find the information. Verify it. Understand it. Decide. Test it. Learn.
Then do it again—faster.
Information is abundant. Judgement and learning speed are not.
And sometimes, as I wrote in the original edition, we do not need to know everything.