10.25.23-Digital-TechNext

ANURAAG SINGH: Thank you, Ariadna, for the introduction. I am Anuraag Singh, CTO and co-founder of TechNext Inc. This is our small team along with my co-founder Professor Christopher Magee, who is an MIT professor. We started out of MIT three years ago to solve a simple problem that we both encountered in our own jobs before we came to MIT.

Both of us worked in R&D in technology strategy, at Ford and Honda respectively, and we were both faced with this problem of having to make technology decisions and having to pick technologies without actually having any facts or quantitative information. It was all gut feeling. And you probably have the same problem in your own working days.

And this is important because R&D is one of the largest expenses. In most corporations it is getting larger and larger, and it's impossible to cut. So this is an important problem, and we need to be more efficient at R&D.

We started with a simple idea, that companies and organizations of all kinds should be able to make technology decisions based on facts, not hype. And in doing so, we can help them identify promising new technologies early, guard against disruptive technologies, and also be able to take advantage of important acquisitions years before their competitors.

This is one of our tools-- this is a screen grab-- where a company might be able to model two different technologies. When might technology A overtake technology two? And what will be the most optimum time to invest in this technology?

I'm sorry for a lot of graphs, and this is just the first or the second of many graphs that are going to come at you fast. So if there are any questions, please come find me in the booth.

The simple idea that we started with actually took almost 20 years of research at MIT, started by Professor Chris Magee, and then continuing at TechNext for the last three years. We have one of the largest data sets in the world on performance improvement for any technologies over decades. This took years to prepare.

We have the latest artificial intelligence techniques, algorithms, and taxonomies to figure out which technology you're interested in and getting you the right results. And we have a catalog of almost 150,000 technologies with their metric to define their technology potential so that you get almost real-time results.

With any model, the question is, does it actually work? And this is one of the many backcasting studies we did. The TechNext forecasts are in color where we hid all future data from the system. So you can see the TechNext forecasts converge pretty quickly to what actually happened-- that's in black-- starting around the year 2000. That's about 20 years of foresight.

Our research is published in some of the top journals in our field. We have gotten a lot of media coverage. And we are working with the US Air Force, think tanks, VCs, MNCs, government agencies to help them improve technology forecasting in their organizations.

So we have worked with them to integrate our tools in-house, or they can work with us on studies in focused technological areas, from quantum computing to nuclear fusion to batteries to lidars. Here's another way you can use the rates that we think about. So if you--

Again, using only the data in the year 2000, if you could see that internet video streaming was going so much faster than the traditional method of video delivery, you could have potentially been able to invest much more quicker in YouTube, Netflix, or even build your own services in-house given that early lead. So what the two graphs show-- right when technology A overtakes technology B, you see an uptick in adoption.

Here's another one of those difficult graphs. This is a complex system, which is a predator drone. And we are thinking about how to deal with a complex portfolio of technologies. Technologies which are slow and critical are where the threats are coming from. Oh, sorry. That's where the threats are coming from, and that's where you need to guard against.

Technologies which are fast and critical, those are things that you're focused on, everybody already knows is important. But the third quadrant is the most interesting because these are technologies which you may not think are critical but are improving very fast.

So 30 years ago, internet was in this bucket. 10 years ago, artificial intelligence was in this bucket. And if you knew this then-- so I invite you to put yourself in the shoes of the management at Kodak in 1980s. They had developed the first digital camera but couldn't take the management along with them or the rest of the organization along with them.

But had they known that these new emerging technologies were so much faster than their slow but critical photographic film, what might have been? You probably know this already. Competing solutions are slow, vague, and generally subjective, whereas we have probabilistic forecasts which get better with new data. It's all auditable, all hype-proof.

So we are actively working with all kinds of organizations all over the world. Regardless of where you are, we'll make it work, so please come see us at the booth. Thank you.