Ash Fontana
7 statements across 1 episodes · 0 bullish · 2 bearish · 1 people on the record · first statement Nov 18, 2019 by Ash Fontana · said 3 times in 1 episodes since 2019 · across every show →
On the record as a speaker too: Ash Fontana's record, appearances and statements → this page counts the times other people say the name.
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brought up most by Harry Stebbings (3)
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Everything said about Ash Fontana, oldest first
Nov 18, 2019 bearish
Fontana: Audits household weekly for eco-friendly products and boycotts almonds
“Over the last couple of years, I've just been going systematically reducing my impact on the environment. And so this includes everything from cutting out meat, And not eating almonds and boycotting that industry. And actually every week I go through my house …”
Nov 18, 2019
Nov 18, 2019
Zetta evaluates data moats across accessibility, fungibility, dimensionality, breadth, and perishability
“Data moats are indeed real, and we evaluate those data moats very carefully at the diligence stage, and we look at things like accessibility, fungibility, dimensionality, breadth, and perishability.”
Nov 18, 2019 negative
Fontana: Workout-based networking is discriminatory; long walks are better
“I'm really hesitant to build work relationships this way because it can discriminate against people in all these little ways. So I think the only thing to try to answer Andy's question, the only thing I've found is, you know, just go on long walks with people …”
Nov 18, 2019
Fontana: Legacy Software Incumbents Cannot Easily Build Consequential AI Models
“If you're a company that's not an AI first company that started sort of before this AI era, you can't really build any predictive models of consequence because you don't have any of that customer data and you have to sort of go back to your customers, renegoti…”
Nov 18, 2019
Nov 18, 2019
Fontana: AI-first companies focus on predictive models, not product features
“AI first companies, on the other hand, they focus on building predictive models, not product features, getting quantitative feedback, not qualitative feedback, and from machine agents, from how the model's just running itself rather than human customers, and t…”