Avanish Sahai discusses the key macro tailwinds that enabled the emergence and rapid growth of the Indian technology industry.
Insight
Sahai: Startups' clean data slates rival incumbents' massive but dirty datasets
“Incumbents have the advantage of a lot of data, right? But they also have the baggage that often the data is not very clean. So as you build your models, as you build your foundation models, et cetera, are you relying on good data or not, right? Whereas a star…”
Assertion Partly supported
Sahai: McKinsey projected $200B Indian tech sector by 2020, actual hit $220B
“We, as a team, and I was leading the software and services practice here in actually very close to where we're recording in Palo Alto, and a number of us were involved in this, and we said, look, and if all these things happen in 2020, this could be about a tw…”
Insight
Sahai: India replicated Silicon Valley's ecosystem flywheel over 25 years
“It's what's made Silicon Valley so successful, right? You have investors, you have universities, you have an environment where people want to come and live. That same thing I would say happened in India over the last 25 years.”
Insight
Sahai: Shared data across multi-product SaaS makes platforms hard to displace
“And if we again have a platform strategy, if we can have the underlying data under all those sales, service, marketing, content, be a common data set, then we become a lot more difficult to displace.”
Opinion
Sahai: AI Disruption Is Happening Faster Than Any Previous Tech Wave
“I think the AI opportunity, however, having said all that, I have never ever seen something take hold And disrupt and force people to rethink business strategy, investment strategy, positioning, et cetera, as fast as this last two and a half years.”
Opinion
Sahai: Many Vertical Industries Are Ripe for Disruption Using Existing Data
“Because I think a lot, there's been a lot of great horizontal solutions, but there are a lot of industries that frankly can, should be disrupted with better technology, with better innovation, where the data has existed, but hasn't Proven to be very very well …”