Home Artificial Intelligence TechCrunch+ roundup: Big Data’s cloud backlash, CVC pitch suggestions, de-risking hardware startups

TechCrunch+ roundup: Big Data’s cloud backlash, CVC pitch suggestions, de-risking hardware startups

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TechCrunch+ roundup: Big Data’s cloud backlash, CVC pitch suggestions, de-risking hardware startups

For a lot of the Information Age, firms that desired to scale invested in server farms and hired teams to maintain them running.

At certainly one of my first startup jobs, I walked in in the future to search out two sleeping co-workers who’d spent the night configuring servers at a co-locating facility 60 miles away. Soon after, once I worked at a publicly traded company, our on-prem data center was resilient enough to operate through a moderate earthquake.

The relatively recent shift to cloud computing promised to lower costs and boost productivity, but “cloud-first strategies could also be hitting the bounds of their efficacy, and in lots of cases, ROIs are diminishing,” writes Thomas Robinson, COO of Domino Data Lab.


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I began wearing sweaters at home after I got my last utility bill, but with enormous workloads from “ML, AI and deep learning programs that require dozens and even lots of of GPUs and terabytes and even petabytes,” firms at scale can’t simply dial back their data usage.

Because “the good repatriation” now going down amongst public firms also has direct implications for startup DevOps teams, Robinson shares suggestions for “a couple of things that could be done to make sure future flexibility for where workloads are created.”

Thanks for reading TC+ this week,

Walter Thompson
Editorial Manager, TechCrunch+
@yourprotagonist

In terms of early-stage growth marketing, it’s often higher to mimic than innovate

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I’m pleased to announce that self-described “growth marketing nerd” Jonathan Martinez has come aboard as a recurring TC+ contributor!

Martinez, who worked on growth teams at Uber, Postmates and Coinbase, can be the founding father of SalesKiwi.

In his latest article, he explains why copying your rivals’ most successful marketing strategies could be certainly one of the fastest ways to get traction with latest customers.

“There’s no have to continually reinvent the wheel,” he advises. “Conserve your resources to innovate for high-probability tests that you simply’re excited to try at various stages of your startup’s life.”

SaaS remains to be open for business, but it surely’s going to take longer to purchase and sell

Close-Up Of Blue Sand Falling In Hourglass

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Greater than 225,000 tech employees have been laid off within the last 12 months, which is having a direct effect on SaaS renewal and buy cycles.

SaaS customers that reduced headcount are buying fewer seat licenses and sales cycles are taking slightly longer than they used to, says Ryan Neu, CEO and co-founder of SaaS-buying platform Vendr.

“Over the past three years, our data has shown a gentle decline in multiyear deals,” he writes in TC+. “Yet now we have also seen a major increase in [average contract value] from purchase to renewal in mission-critical and sticky software categories, like CRM or email.”

Learn how to pitch CVCs

CVC, corporate venture capital,

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As individual VC firms pulled back and commenced amassing dry powder in 2022, corporate enterprise capital (CVC) funds stepped up.

PitchBook found that CVCs played a component in 56.2% of all enterprise deals that took place last 12 months, “up only a hair over 2021’s 25.6%,” reports Rebecca Szkutak, who spoke to a couple of experts to learn how startups in fundraising mode can get on their radar.

“If there isn’t a product integration angle, and we don’t see or can’t find evidence that a customer of ours or theirs would wish to work together, it could be hard for us to work together,” said Andrew Ferguson, VP of corporate development and ventures at Databricks.

10 suggestions for de-risking hardware products

Image Credits: Frisco / Getty Images

With the fitting team, a software startup might only need weeks to go from the concept stage to billing their first customers.

Conversely, all hardware startups grapple with high capital expenditures and wish time to ramp up production, which is why testing and evaluating demand are so necessary, says Narek Vardanyan, founding father of Prelaunch.com, which recently closed a pre-seed round.

“You want to make decisions based on people’s actual behavior,” he said in an interview with TechCrunch+. “You want to be sure that the info you’re tracking is coming from the fitting sorts of people.”

Desirous about pulling the plug in your startup?

Close up of web page button on monitor screen

Image Credits: SEAN GLADWELL (opens in a latest window) / Getty Images

I just read a Twitter post by angel investor Gokul Rajaram asserting that founders who raised large sums before the downturn but have yet to search out product-market fit “are going through an excruciating psychological journey.”

Entrepreneurs are indoctrinated to pursue success in any respect costs, but “chasing limitless pivots trying to search out PMF is a bridge to nowhere,” wrote Rajaram, who shared a story a couple of founder who returned funds to investors before winding down operations:

“The relief they felt once they realized investors and employees were on board and 100% supportive of their decision, was palpable. (All employees received solid severance before the corporate shut down.)”

Should you’re a founder who has decided to shut down (or an investor who’s counseled one), please consider sharing your story with TechCrunch+. To get in contact, send a note to guestcolumns@techcrunch.com.

Corporate investment in AI is on the rise, driven by the tech’s promise

Rolled dollar bills hang from a bonsai tree.

Image Credits: Karl Tapales (opens in a latest window) / Getty Images

Last 12 months, global investors poured $77.5 billion into AI startups, a 115% YoY increase, reported Tortoise Intelligence.

In accordance with Kyle Wiggers, corporate adoption of generative AI is fueling investor interest, as are the sector’s outsized returns: A 2022 poll found that 92% of huge firms are “achieving returns on their data and AI investments.”

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