Nov 9, 2016 · 29m · 20vc

20VC: Who Will Own The Data? Is There An Incumbency Advantage in AI? When Will Artificial Super Intelligence Come To The Forefront with Rudina Seseri, Founder and Managing Director @ Glasswing Ventures

Rudina Seseri · 18m spoken Harry Stebbings · 9m spoken
0:00 / 0:00

gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this episode of The 20 Minute VC, host Harry Stebbings interviews Rudina Seseri, founder and managing partner of Glasswing Ventures, about her career journey, investment thesis in applied artificial intelligence, and perspectives on corporate incumbency. Sesari explores the evolution of data ownership, tech disruption cycles, key vertical opportunities in social robotics and cybersecurity, and her long-term vision for building a category-defining venture firm.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Harry holds 32.1% of the talking time here. How this is scored →

Harry as informed peer 3.5 Guest teaching 3.9 Guest disagreement 2.0 Harry pushing back 2.4
05100:0010:0020:004:00–6:58 · Harry as informed peer 3/10 Corporate Leadership Insights and Self-Disruption Challenges at Microsoft Harry asks about key takeaways from Microsoft and why AI is a viable focus today compared to a decade ago. Rudina explains corporate self-disruption hurdles at legacy tech behemoths and details how pervasive connectivity and declining cloud storage costs created market timing for narrow AI.6:58–11:11 · Harry as informed peer 4/10 Data Ownership, Consumer Privacy Rights, and Regulatory Trends Harry presses on whether proprietary data sets create competitive moats or alarming incumbency advantages for big tech giants. Rudina reframes the premise by arguing that founders and team execution matter far more than data sets alone, and notes that incumbents often get in their own way.11:11–13:41 · Harry as informed peer 4/10 Technology Disruption Cycles and Accelerated AI Adoption Speed Harry asks where we are in technological disruption cycles and correctly cites storage and data as drivers behind faster adoption. Rudina elaborates on the historical halving of adoption cycles from web to mobile and predicts AI adoption will be even faster due to AI-native younger generations.13:42–16:36 · Harry as informed peer 4/10 Positive Transformations and Business Models in AI Platforms Harry inquires about potential business model innovations needed for AI platforms beyond standard SaaS models. Rudina clarifies that AI represents underlying performance disruption and S-curve jumping rather than radical monetization shifts, noting SaaS remains highly applicable.16:37–19:44 · Harry as informed peer 4/10 Predictions on Superintelligence, Job Displacement, and Autonomous Driving Harry asks for predictions regarding superintelligence, job loss mitigation parameters, and autonomous vehicle market structure. Rudina playfully deflects the general AI timeline while using a horse carriage driver metaphor to reframe job displacement concerns into labor evolution.19:44–22:28 · Harry as informed peer 4/10 Glasswing's Vertical Focus Areas: Social Robotics and Predictive Cybersecurity Harry attempts to define AI's core differentiator as detecting causation rather than correlation. Rudina explicitly corrects him, stating causation is merely a facet and that continuous self-improvement is the true transformational core of AI software.22:31–25:31 · Harry as informed peer 3/10 Quick Fire Round: Favorite Book, Mentors, and Entrepreneurial Lessons In a rapid-fire sequence, Harry asks about favorite books, mentors, and the challenges of founding Glasswing. The exchange is highly collaborative, with Harry connecting their favorite blog to his own foundational AI knowledge.25:31–26:55 · Harry as informed peer 2/10 The Glasswing Name Origin and Five-Year Vision for the Venture Firm Harry asks about the five-year vision for Glasswing Ventures. Rudina breaks down the symbolism behind the Glasswing name—transparency, transformation, and structural durability—to outline her goal of building a top-tier firm.4:00–6:58 · Guest teaching 4/10 Corporate Leadership Insights and Self-Disruption Challenges at Microsoft Harry asks about key takeaways from Microsoft and why AI is a viable focus today compared to a decade ago. Rudina explains corporate self-disruption hurdles at legacy tech behemoths and details how pervasive connectivity and declining cloud storage costs created market timing for narrow AI.6:58–11:11 · Guest teaching 5/10 Data Ownership, Consumer Privacy Rights, and Regulatory Trends Harry presses on whether proprietary data sets create competitive moats or alarming incumbency advantages for big tech giants. Rudina reframes the premise by arguing that founders and team execution matter far more than data sets alone, and notes that incumbents often get in their own way.11:11–13:41 · Guest teaching 4/10 Technology Disruption Cycles and Accelerated AI Adoption Speed Harry asks where we are in technological disruption cycles and correctly cites storage and data as drivers behind faster adoption. Rudina elaborates on the historical halving of adoption cycles from web to mobile and predicts AI adoption will be even faster due to AI-native younger generations.13:42–16:36 · Guest teaching 4/10 Positive Transformations and Business Models in AI Platforms Harry inquires about potential business model innovations needed for AI platforms beyond standard SaaS models. Rudina clarifies that AI represents underlying performance disruption and S-curve jumping rather than radical monetization shifts, noting SaaS remains highly applicable.16:37–19:44 · Guest teaching 5/10 Predictions on Superintelligence, Job Displacement, and Autonomous Driving Harry asks for predictions regarding superintelligence, job loss mitigation parameters, and autonomous vehicle market structure. Rudina playfully deflects the general AI timeline while using a horse carriage driver metaphor to reframe job displacement concerns into labor evolution.19:44–22:28 · Guest teaching 6/10 Glasswing's Vertical Focus Areas: Social Robotics and Predictive Cybersecurity Harry attempts to define AI's core differentiator as detecting causation rather than correlation. Rudina explicitly corrects him, stating causation is merely a facet and that continuous self-improvement is the true transformational core of AI software.22:31–25:31 · Guest teaching 1/10 Quick Fire Round: Favorite Book, Mentors, and Entrepreneurial Lessons In a rapid-fire sequence, Harry asks about favorite books, mentors, and the challenges of founding Glasswing. The exchange is highly collaborative, with Harry connecting their favorite blog to his own foundational AI knowledge.25:31–26:55 · Guest teaching 2/10 The Glasswing Name Origin and Five-Year Vision for the Venture Firm Harry asks about the five-year vision for Glasswing Ventures. Rudina breaks down the symbolism behind the Glasswing name—transparency, transformation, and structural durability—to outline her goal of building a top-tier firm.4:00–6:58 · Guest disagreement 2/10 Corporate Leadership Insights and Self-Disruption Challenges at Microsoft Harry asks about key takeaways from Microsoft and why AI is a viable focus today compared to a decade ago. Rudina explains corporate self-disruption hurdles at legacy tech behemoths and details how pervasive connectivity and declining cloud storage costs created market timing for narrow AI.6:58–11:11 · Guest disagreement 3/10 Data Ownership, Consumer Privacy Rights, and Regulatory Trends Harry presses on whether proprietary data sets create competitive moats or alarming incumbency advantages for big tech giants. Rudina reframes the premise by arguing that founders and team execution matter far more than data sets alone, and notes that incumbents often get in their own way.11:11–13:41 · Guest disagreement 1/10 Technology Disruption Cycles and Accelerated AI Adoption Speed Harry asks where we are in technological disruption cycles and correctly cites storage and data as drivers behind faster adoption. Rudina elaborates on the historical halving of adoption cycles from web to mobile and predicts AI adoption will be even faster due to AI-native younger generations.13:42–16:36 · Guest disagreement 2/10 Positive Transformations and Business Models in AI Platforms Harry inquires about potential business model innovations needed for AI platforms beyond standard SaaS models. Rudina clarifies that AI represents underlying performance disruption and S-curve jumping rather than radical monetization shifts, noting SaaS remains highly applicable.16:37–19:44 · Guest disagreement 3/10 Predictions on Superintelligence, Job Displacement, and Autonomous Driving Harry asks for predictions regarding superintelligence, job loss mitigation parameters, and autonomous vehicle market structure. Rudina playfully deflects the general AI timeline while using a horse carriage driver metaphor to reframe job displacement concerns into labor evolution.19:44–22:28 · Guest disagreement 4/10 Glasswing's Vertical Focus Areas: Social Robotics and Predictive Cybersecurity Harry attempts to define AI's core differentiator as detecting causation rather than correlation. Rudina explicitly corrects him, stating causation is merely a facet and that continuous self-improvement is the true transformational core of AI software.22:31–25:31 · Guest disagreement 1/10 Quick Fire Round: Favorite Book, Mentors, and Entrepreneurial Lessons In a rapid-fire sequence, Harry asks about favorite books, mentors, and the challenges of founding Glasswing. The exchange is highly collaborative, with Harry connecting their favorite blog to his own foundational AI knowledge.25:31–26:55 · Guest disagreement 0/10 The Glasswing Name Origin and Five-Year Vision for the Venture Firm Harry asks about the five-year vision for Glasswing Ventures. Rudina breaks down the symbolism behind the Glasswing name—transparency, transformation, and structural durability—to outline her goal of building a top-tier firm.4:00–6:58 · Harry pushing back 1/10 Corporate Leadership Insights and Self-Disruption Challenges at Microsoft Harry asks about key takeaways from Microsoft and why AI is a viable focus today compared to a decade ago. Rudina explains corporate self-disruption hurdles at legacy tech behemoths and details how pervasive connectivity and declining cloud storage costs created market timing for narrow AI.6:58–11:11 · Harry pushing back 4/10 Data Ownership, Consumer Privacy Rights, and Regulatory Trends Harry presses on whether proprietary data sets create competitive moats or alarming incumbency advantages for big tech giants. Rudina reframes the premise by arguing that founders and team execution matter far more than data sets alone, and notes that incumbents often get in their own way.11:11–13:41 · Harry pushing back 3/10 Technology Disruption Cycles and Accelerated AI Adoption Speed Harry asks where we are in technological disruption cycles and correctly cites storage and data as drivers behind faster adoption. Rudina elaborates on the historical halving of adoption cycles from web to mobile and predicts AI adoption will be even faster due to AI-native younger generations.13:42–16:36 · Harry pushing back 3/10 Positive Transformations and Business Models in AI Platforms Harry inquires about potential business model innovations needed for AI platforms beyond standard SaaS models. Rudina clarifies that AI represents underlying performance disruption and S-curve jumping rather than radical monetization shifts, noting SaaS remains highly applicable.16:37–19:44 · Harry pushing back 4/10 Predictions on Superintelligence, Job Displacement, and Autonomous Driving Harry asks for predictions regarding superintelligence, job loss mitigation parameters, and autonomous vehicle market structure. Rudina playfully deflects the general AI timeline while using a horse carriage driver metaphor to reframe job displacement concerns into labor evolution.19:44–22:28 · Harry pushing back 3/10 Glasswing's Vertical Focus Areas: Social Robotics and Predictive Cybersecurity Harry attempts to define AI's core differentiator as detecting causation rather than correlation. Rudina explicitly corrects him, stating causation is merely a facet and that continuous self-improvement is the true transformational core of AI software.22:31–25:31 · Harry pushing back 1/10 Quick Fire Round: Favorite Book, Mentors, and Entrepreneurial Lessons In a rapid-fire sequence, Harry asks about favorite books, mentors, and the challenges of founding Glasswing. The exchange is highly collaborative, with Harry connecting their favorite blog to his own foundational AI knowledge.25:31–26:55 · Harry pushing back 0/10 The Glasswing Name Origin and Five-Year Vision for the Venture Firm Harry asks about the five-year vision for Glasswing Ventures. Rudina breaks down the symbolism behind the Glasswing name—transparency, transformation, and structural durability—to outline her goal of building a top-tier firm.

speaking balance: gold is Harry, purple is the guest (3 minute bins)

0:00 · Harry 96.2% · guest 3.8%0:00 · Harry 96.2% · guest 3.8%3:00 · Harry 20.8% · guest 79.2%3:00 · Harry 20.8% · guest 79.2%6:00 · Harry 18.8% · guest 81.2%6:00 · Harry 18.8% · guest 81.2%9:00 · Harry 13.2% · guest 86.8%9:00 · Harry 13.2% · guest 86.8%12:00 · Harry 13.2% · guest 86.8%12:00 · Harry 13.2% · guest 86.8%15:00 · Harry 24.6% · guest 75.4%15:00 · Harry 24.6% · guest 75.4%18:00 · Harry 17.1% · guest 82.9%18:00 · Harry 17.1% · guest 82.9%21:00 · Harry 15.8% · guest 84.2%21:00 · Harry 15.8% · guest 84.2%24:00 · Harry 21.2% · guest 78.8%24:00 · Harry 21.2% · guest 78.8%27:00 · Harry 100% · guest 0%27:00 · Harry 100% · guest 0%
Sharpest disagreement ▶ 22:05 Direct rejection of host's AI thesis

Rudina flatly disagrees with Harry's hypothesis that detecting causation is AI's core differentiator, stating unequivocally 'No, I think that's only a facet' before offering her own definition.

Hardest push from Harry ▶ 9:56 Challenging the threat of data incumbency

Harry presses after a generalized response to ask whether tech giants like Microsoft and Google hold a worrying and formidable data incumbency advantage over startups.

Biggest teaching moment ▶ 17:32 Reframing technological job displacement

Rudina uses the historical transition away from horse carriage drivers to educate Harry on why technological shifts reallocate labor toward creative roles rather than destroying net employment.

Harry holds his own ▶ 12:36 Identifying hardware drivers of AI adoption

Harry demonstrates technical understanding by citing data volume and cloud storage reductions as the drivers behind faster adoption, which Rudina immediately validates with 'Yes, and yes, and yes.'

the scores for every segment, with the reasoning behind each
ChapterTopicHarry as informed peerGuest teachingGuest disagreementHarry pushing backWhy
Corporate Leadership Insights and Self-Disruption Challenges at Microsoft 3421 Harry asks about key takeaways from Microsoft and why AI is a viable focus today compared to a decade ago. Rudina explains corporate self-disruption hurdles at legacy tech behemoths and details how pervasive connectivity and declining cloud storage costs created market timing for narrow AI.
Data Ownership, Consumer Privacy Rights, and Regulatory Trends 4534 Harry presses on whether proprietary data sets create competitive moats or alarming incumbency advantages for big tech giants. Rudina reframes the premise by arguing that founders and team execution matter far more than data sets alone, and notes that incumbents often get in their own way.
Technology Disruption Cycles and Accelerated AI Adoption Speed 4413 Harry asks where we are in technological disruption cycles and correctly cites storage and data as drivers behind faster adoption. Rudina elaborates on the historical halving of adoption cycles from web to mobile and predicts AI adoption will be even faster due to AI-native younger generations.
Positive Transformations and Business Models in AI Platforms 4423 Harry inquires about potential business model innovations needed for AI platforms beyond standard SaaS models. Rudina clarifies that AI represents underlying performance disruption and S-curve jumping rather than radical monetization shifts, noting SaaS remains highly applicable.
Predictions on Superintelligence, Job Displacement, and Autonomous Driving 4534 Harry asks for predictions regarding superintelligence, job loss mitigation parameters, and autonomous vehicle market structure. Rudina playfully deflects the general AI timeline while using a horse carriage driver metaphor to reframe job displacement concerns into labor evolution.
Glasswing's Vertical Focus Areas: Social Robotics and Predictive Cybersecurity 4643 Harry attempts to define AI's core differentiator as detecting causation rather than correlation. Rudina explicitly corrects him, stating causation is merely a facet and that continuous self-improvement is the true transformational core of AI software.
Quick Fire Round: Favorite Book, Mentors, and Entrepreneurial Lessons 3111 In a rapid-fire sequence, Harry asks about favorite books, mentors, and the challenges of founding Glasswing. The exchange is highly collaborative, with Harry connecting their favorite blog to his own foundational AI knowledge.
The Glasswing Name Origin and Five-Year Vision for the Venture Firm 2200 Harry asks about the five-year vision for Glasswing Ventures. Rudina breaks down the symbolism behind the Glasswing name—transparency, transformation, and structural durability—to outline her goal of building a top-tier firm.

Statements from this episode (15)

Insight
Large incumbent tech companies rarely succeed at self-disruption
“Well, behemoths and dinosaurs die slowly, so the ability for a large company to disrupt oneself is quite difficult. If they succeed in doing it, it's usually the exception, and I'm often hard-pressed to find such exceptions.”
Rudina Seseri Nov 9, 2016 ▶ 4:06
Insight
Pervasive connectivity and cheap cloud storage enable applied AI
“What has transformed or what has changed the market timing for adoption is this emergence of what we at Glasswing call pervasive connectivity and the inputs and outputs to it. So by pervasive connectivity, we're really referring to the notion that consumers an…”
Rudina Seseri Nov 9, 2016 ▶ 5:36
Prediction Not checkable as stated
Future data ownership will be a hybrid favoring consumer control
“I think reality will be that data will remain proprietary to a certain extent. You know, you have entire ecosystems around Google and Uber and Facebook where they're effectively AI companies leveraging the large data sets, but you'll also see this push from co…”
Rudina Seseri Nov 9, 2016 ▶ 7:39
Insight
AI cannot replace human execution in building a successful startup
“There is no artificial intelligence that replaces human execution.”
Rudina Seseri Nov 9, 2016 ▶ 9:01
Opinion
Barriers to overcoming big tech data incumbencies are surprisingly low
“I do think that there are big data incumbencies, and I do think that they require, if you will, they're a challenge to overcome I do think that the barriers to overcome them are not so high. As previously mentioned, the incumbents oftentimes get in their own w…”
Rudina Seseri Nov 9, 2016 ▶ 10:13
Opinion
The social and mobile technological revolution is completely over
“Then we see the social mobile wave, and that disruption, or that wave, in our view, the revolution is complete. There will be, there is evolutionary or incremental you know, improvements in nature, and we'll see new products, but from a transformational point …”
Rudina Seseri Nov 9, 2016 ▶ 11:40
Prediction Not checkable as stated
The AI adoption cycle will be twice as fast as mobile
“We think that AI, the AI wave has already, in fact, begun. We're probably three, four years into it, and we expect that, as in prior waves, the adoption cycle for AI-powered products and platforms will be shorter. From the web to the social mobile, we saw adop…”
Rudina Seseri Nov 9, 2016 ▶ 12:08
Prediction Not checkable as stated
AI will disrupt every enterprise department from IT to cybersecurity
“The applications for that is effectively disrupting every facet of the enterprise from IT to, you know, the departments from sales technologies, marketing tech, cybersecurity every single facet all of a sudden is ripe for disruption because of AI powered techn…”
Rudina Seseri Nov 9, 2016 ▶ 14:54
Prediction Not checkable as stated
AI startups will mostly stick to standard SaaS monetization models
“In my view, it's to a lesser extent about innovating around the business model, although it doesn't preclude you from doing so. So I could very much see a sales tech, a next generation sales tech company that is AI powered, but it's still a SaaS monetization m…”
Rudina Seseri Nov 9, 2016 ▶ 16:14
Prediction Not checkable as stated
Artificial superintelligence is still 20 to 30 years away
“I think their entire, you know, there's a field of futurists who should do that, but I do think we're 2030 years away from that, and when we get there, I hope we will put the right parameters in place to make the right decisions.”
Rudina Seseri Nov 9, 2016 ▶ 17:15
Prediction Not checkable as stated
Self-driving cars will see major market penetration in 5 to 10 years
“I think in the next five to 10 years, we'll probably see quite a bit more penetration of Self-driving cars, and again, I hope for, since becoming a parent, apparently the whole world revolves around my child, who knew, but I think, I suspect that by the time s…”
Rudina Seseri Nov 9, 2016 ▶ 18:45
Insight
Startups create and win new categories, not legacy incumbents
“Again, I am a believer that it's startups or new entrants that create new categories rather than the legacy auto, you know, manufacturers that win.”
Rudina Seseri Nov 9, 2016 ▶ 19:28
Prediction Not checkable as stated
The autonomous vehicle market will have a winners-take-most dynamic
“I suspect that it will be winners take most, not a winner take all.”
Rudina Seseri Nov 9, 2016 ▶ 19:39
Prediction Partly held up
Consumer robotics will take off with Jibo as the category leader
“I have a portfolio company in the interest of full disclosure called Jibo in the social robotics space that I seated a while ago, and I think that in, that the whole area and category of consumer robotics it's about to take off, and I clearly believe that Jibo…”
Rudina Seseri Nov 9, 2016 ▶ 20:52
Insight
AI's fundamental distinction is continuously self-improving software
“No, I think that's only a facet. I think the fundamental difference is about having a software that it's effectively to have systems that continuously self-improve. That, that is transformational.”
Rudina Seseri Nov 9, 2016 ▶ 22:18
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 1,200 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.