When I moved to San Francisco, the quirky rotunda at 532 Market Street was a Sharper Image store full of plasma balls and tourists trying out massage chairs.
The ETrade branch that took over the space closed a few years ago, but last August, it got a new tenant: Silicon Valley Bank. Sigh.
Downtown SF hasn't bounced back from the pandemic, but this is a prime location with lots of foot traffic. Hopefully, after Silicon Valley Bridge Bank winds up its operations, a viable business will move in.
But that's just one street corner. The second-largest bank failure in U.S. history is going to reshape the startup ecosystem for years to come.
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Silicon Valley Bank was more than just a preferred choice for managing payroll and investor cash: It also offered wealth management services and below-market-rate home loans and helped coordinate private stock sales. It was also a required choice for many clients whose contracts required them to "use the firm for all or most of their banking services," CNBC reported.
So where does this bank's collapse leave the tech industry? Who's most vulnerable, who stands to benefit, and what are some of the long-term implications for VC? To learn more, Karan Bhasin and Ram Iyer interviewed:
Maëlle Gavet, CEO, Techstars
Niko Bonatsos, managing director, General Catalyst
Colin Beirne, partner, Two Sigma Ventures
"We’re probably going to see consolidation in the VC class," said Gavet.
"It was already on the way, but this is probably going to accelerate it, because SVB was also a preeminent provider of loans for GPs to make their capital commitment polls."
Thanks very much for reading,
Editorial Manager, TechCrunch+
The AI revolution has outgrown the Turing test: Introducing a new framework
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A friend recently asked me to identify a block of ChatGPT text that they'd embedded in an email. I was able to easily, but only because the passage was particularly boring and didn't sound like them at all.
Although generative AI is exceeding my expectations, the Turing test is mostly intact in my personal experience. But for how much longer?
Entrepreneur/investor Chris Saad says we need a new benchmark that goes beyond Turing's "simplistic pass/fail basis," which is why he developed "a new approach to evaluating AI capabilities based on the Theory of Multiple Intelligences."
Building a PLG motion on top of usage-based pricing
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Last July, Puneet Gupta, a former AWS general manager who's now CEO and co-founder of Amberflo.io, wrote a TC+ article explaining how SaaS startups can adopt usage-based pricing models.
In a follow-up, he shares four tactics teams can use to gather, analyze and leverage customer data to take the guesswork out of pricing decisions.
"When the time comes to make decisions about product packaging and pricing, the first place you turn to should be the metering pipeline for historical usage data," he writes.
Time to trust: Questions cybersecurity customers ask and how to answer them
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Putting yourself in your customers' shoes can raise uncomfortable questions, especially for cybersecurity startups, says angel investor Ross Haleliuk.
To help teams shorten the "time to trust" interval, he asks several questions cybersecurity customers are likely to pose while evaluating vendors, along with action items that can help provide convincing answers.
"It is important to keep in mind that trust is built over a long time, but it can be lost in an instant," writes Haleliuk.
Finding your startup’s valuation: An angel investor explains how
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In her latest column, TC+ contributor Marjorie Radlo-Zandi explains how angel investors like herself establish pre- and post-money valuations.
"While assessing prospective investments, I ensure it’s a product or service that I care deeply about and educate myself about the company’s market," she says.
"I want to see a fair valuation of the business and a well-defined market worth at least $100 million."
Coming in hot is a great way to cut short an investor meeting. To help first-time founders avoid waving red flags, she breaks down the Berkus method and explains why uninformed founders often seek unrealistic valuations.