1Mby1M Virtual Accelerator AI Investor Forum: With Ashmeet Sidana, Chief Engineer at Engineering Capital (Part 1)

Ashmeet Sidana, Chief Engineer at Engineering Capital, talks about his AI investment thesis. It’s a wonderful discussion that not only entrepreneurs should listen to, but investors should also listen in to calibrate their own investment thesis.

Sramana Mitra: Good morning, everybody. Welcome to today’s One Million by One Million Strategy Roundtable for Entrepreneurs. 1Mby1M, as you know, is the first and only global virtual accelerator for technology startups. We run it from Silicon Valley, but we have a global footprint. And we’ve been doing this for a very, very long time. We founded the company in 2010. It was actually based on this experiment of online mentoring that we started back in 2008 that predates the One Million by One Million program in its full format.

This is the 645th roundtable and we are going to focus today’s session on a conversation with my friend, Ashmeet Sidana, Chief Engineer, as he calls himself, of Engineering Capital. I want to underscore what he is claiming here is to be a hands-on techie. Founders or partners of VC firms generally introduce themselves as General partner or Managing Partner or Managing Director, but Ashmeet has chosen to call himself Chief Engineer, which is amazing because he is a hardcore techie person. I always learn a lot talking to him about our industry, our business, the startup business, the venture funded startup business.

Today, we’re going to do something very special. Actually, this is sort of a kick-off for a series that we have just started, which is talking to investors about their artificial intelligence (AI) investment thesis. It’s super contemporary and important. We have been covering AI entrepreneurship for a while now, as you know, but at the moment we are at the peak of the market, and a lot has happened. On this forum, we’re going to unpack what’s going on and why what’s going on is going on. So welcome, Ashmeet. It’s a real pleasure to have you back here and let’s get started.

Ashmeet Sidana: Thank you, Sramana. I’m always happy to be back.

Sramana Mitra: So what is your investment thesis? In the age of AI, what are you thinking? How are you looking at the market?

Ashmeet Sidana: My focus at Engineering Capital is very early stage investments in software companies that have a technical insight. That technical insight could come from AI, a very recent phenomenon that has become much more successful and popular and has reached a tipping point. We’ll talk about some of those tipping points in AI. But at Engineering Capital, for me, it doesn’t have to come only from AI.

The other important attribute is that I invest in capital-efficient companies, companies that take a very small amount of capital, which are leveraging their technical insight to hopefully build a big business.

Those are the two dimensions of my investment focus.

Sramana Mitra: So Ashmeet, you have invested in some companies where the technical insight comes from AI, yes?

Ashmeet Sidana: Yes, for sure.

Sramana Mitra: Can we pick maybe a few of those case studies and double click down, tell us what you’ve invested in, why you’ve invested in, what was the technical insight and just some general understanding of a few case studies where you have played your hand.

Ashmeet Sidana: Sure. So I want to start by recognizing that AI itself is a field that people have been working on for decades, and we have slowly been making progress from a technical perspective in terms of what we were able to do with, broadly speaking, machine learning and AI aspects of technology.

However, a few years ago, we reached a tipping point. I think the popular tipping point in the media and the press was ChatGPT, but I would say the technical tipping point was really a paper written about a decade ago, an ImageNet paper by Geoffrey Hinton from the University of Toronto, which really demonstrated that you could recognize dogs versus cats in images using machine learning. It was a phenomenal development that they were able to show using neural networks.

That is when I think far-sighted companies like Google, Microsoft, etc., started investing very deeply in AI. I got involved with the University of Toronto and started looking at the AI space. About four or five years ago, I made some investments, recognizing that we were reaching a tipping point. Of course, we couldn’t predict that there would be so much popular press and popular coverage. We didn’t know ChatGPT was coming, but it was clear that the technology had reached a tipping point for usefulness.

So here are two examples. I was the first investor in a company called Evinced. The founders had the observation that you could use machine learning and machine vision to help developers make websites and mobile applications accessible. Accessible here means making it available to people with special needs. If that was the only insight, it would have been an interesting company. It certainly would have made the world a better place for people who have special needs.

But what Navin and Gal layered on top of that was this insight that they were able to do it in a way, which made it better for regular people, for people who did not have special needs. The fact that they were doing it with AI, they were doing it with machine vision, made it, of course, available at scale, very cheaply and very quickly. Today, they’ve built a substantial business. The example I always like to give when describing Evinced is, imagine watching TV with closed captioning. Today, we take closed captioning for granted. You can go on YouTube, you can turn it on, you put your TV on, it comes on.

Of course, closed captioning is a feature that was designed for people who are hearing impaired. So special needs, you’re hearing impaired, you can read what someone is saying. However, it turns out that 99% of the people who are not hearing impaired have still used closed captioning occasionally.

That’s what I mean by making it better for people who don’t have special needs. It’s an accessibility feature that occasionally makes it better for everyone and makes the whole product a better experience. So that’s what Evinced did with machine vision. They’re a huge business now. We raised a series A with Microsoft and series B with Insight. They’re a very large company now. I’m very proud to have been the first and only investor in their pre-seed round when it was just a twinkle in the eye of Navin and Gal.

Sramana Mitra: What they’re selling is a closed captioning product?

Ashmeet Sidana: No, closed captioning is only an example I’m using to explain how an accessibility feature makes a product better for everyone. The product that they sell is a developer tool which allows companies like hospitals, airlines, hotels, and people who have large consumer bases to make their products accessible. Their business model is B2B2C. So if you go on the Capital One website or if you’re a customer of Salesforce, now all your products are accessible, thanks to Evinced.

This segment is part 1 in the series : 1Mby1M Virtual Accelerator AI Investor Forum: With Ashmeet Sidana, Chief Engineer at Engineering Capital
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