Deep tech decisions are timing decisions.
TechNext measures how fast defined technology domains are improving and forecasts when an emerging technology will cross the cost or capability threshold that changes your decision. For the investors backing it and the R&D and mission teams that will have to adopt it.
Start with the technology area you are exploring, or the decision you need to make.
Measured, published, and patented.
Questions we help you answer.
The roadmap has more promising options than the budget can fund, and the case for each one rests on a claim about how fast it will improve.
- Which part of our system should we improve next?
- Where else is our technology worth applying?
- Do we build this, invest, buy, partner or stop?
There is no revenue to test the thesis against, so the whole bet rests on whether the underlying technology keeps improving on schedule.
- Is the technology under this company improving fast enough?
- Does the technical claim under this commitment hold up?
- What would change our mind, and when should we look again?
Programs are committed years ahead of the technologies they depend on, and the capability picture can shift mid-program.
- Which technologies does this mission actually depend on?
- Where will a capability or supply-chain crossover land?
- Which programs are betting on a slope that has flattened?
The slope changes first. The market notices later.
Every board, investment committee and R&D team has more emerging technologies to judge than judgment can cover. Analyst reports, press attention, founder stories and raw patent counts tell you how much noise a technology is making. None of them tells you how fast it is improving.
TechNext uses patented technology developed at MIT to precisely define technology domains and estimate the annual improvement rate of each one.
Most technology intelligence measures attention. We measure progress.
We say no as readily as yes.
Three public calls in opposite directions, each logged before the outcome. Dozens of other areas and calls available on demand.
Analysts had the cost stuck well above competitiveness. We recorded a forecast of a cost crossover ahead of that consensus, and logged the later observation against it.
Developers were publishing net-energy targets. Our estimated rates for tokamaks and HTS magnets put self-sustaining fusion later than any of them. Announced dates have since slipped toward our window.
From a year-2000 model, the method projected the trajectory of solar becoming cheaper than onshore wind per MWh, matching empirical reality roughly two decades ahead of time (Benson & Magee, 2018).
A forecast with an expiry date on it.
Every answer says what evidence would change it, and we watch for that evidence. When a trajectory moves, you hear from us.
Recent work, taken domain by domain.
Recent engagements. Each asks which of the competing approaches inside it are improving fast enough to matter.
We add a rate to the process your team already runs.
Thirty minutes with the people who build the model.
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