After I moved to San Francisco, the quirky rotunda at 532 Market Street was a Sharper Image store stuffed with plasma balls and tourists trying out massage chairs.

The ETrade branch that took over the space closed just a few years ago, but last August, it got a latest tenant: Silicon Valley Bank.

Downtown SF hasn’t bounced back from the pandemic, but that is a first-rate location with a lot of foot traffic. Hopefully, after Silicon Valley Bridge Bank winds up its operations, a viable business will move in.

But that’s only one street corner. The second-largest bank failure in U.S. history goes to reshape the startup ecosystem for years to return.

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Silicon Valley Bank was greater than only a preferred selection for managing payroll and investor money: It also offered wealth management services and below-market-rate home loans and helped coordinate private stock sales. It was also a selection for many consumers 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 profit, and what are a number 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 within the VC class,” said Gavet.

“It was already on the best way, but this might be going to speed up it, because SVB was also a preeminent provider of loans for GPs to make their capital commitment polls.”

Thanks very much for reading,

Walter Thompson
Editorial Manager, TechCrunch+
@yourprotagonist

The AI revolution has outgrown the Turing test: Introducing a latest framework

A friend recently asked me to discover a block of ChatGPT text that they’d embedded in an email. I used to be in a position to easily, but only since the passage was particularly boring and didn’t sound like them in any respect.

Although generative AI is exceeding my expectations, the Turing test is usually intact in my personal experience. But for the way for much longer?

Entrepreneur/investor Chris Saad says we’d like a latest benchmark that goes beyond Turing’s “simplistic pass/fail basis,” which is why he developed “a latest approach to evaluating AI capabilities based on the Theory of Multiple Intelligences.”

Constructing 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 4 tactics teams can use to collect, analyze and leverage customer data to take the guesswork out of pricing decisions.

“When the time involves make decisions about product packaging and pricing, the primary place you switch to needs to be the metering pipeline for historical usage data,” he writes.

Time to trust: Questions cybersecurity customers ask and the best way 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 assist teams shorten the “time to trust” interval, he asks several questions cybersecurity customers are prone to pose while evaluating vendors, together with motion items that might help provide convincing answers.

“It is crucial to have in mind that trust is built over an extended time, but it may well be lost instantly,” 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 services or products that I care deeply about and educate myself in regards to the company’s market,” she says.

“I need to see a good valuation of the business and a well-defined market value at the very least $100 million.”

Coming in hot is an incredible technique to cut short an investor meeting. To assist first-time founders avoid waving red flags, she breaks down the Berkus method and explains why uninformed founders often seek unrealistic valuations.