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

For most of the Information Age, companies that wanted to scale invested in server farms and hired teams to keep them running.

At one of my first startup jobs, I walked in one day to find two sleeping co-workers who’d spent the night configuring servers at a co-locating facility 60 miles away. Soon after, when 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 may be hitting the limits of their efficacy, and in many cases, ROIs are diminishing,” writes Thomas Robinson, COO of Domino Data Lab.


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I started 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 or even hundreds of GPUs and terabytes or even petabytes,” companies at scale can’t simply dial back their data usage.

Because “the great repatriation” now taking place among public companies also has direct implications for startup DevOps teams, Robinson shares suggestions for “a few things that can be done to ensure future flexibility for where workloads are created.”

Thanks for reading TC+ this week,

Walter Thompson
Editorial Manager, TechCrunch+
@yourprotagonist

When it comes to early-stage growth marketing, it’s often better to imitate than innovate

Plastic banana beside real banana
Plastic banana beside real banana

Image Credits: Jorg Greuel (opens in a new window) / Getty Images

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, is also the founder of SalesKiwi.

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

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

SaaS is still open for business, but it’s going to take longer to buy and sell

Close-Up Of Blue Sand Falling In Hourglass
Close-Up Of Blue Sand Falling In Hourglass

Image Credits: Ruslan Malysh/EyeEm (opens in a new window) / Getty Images

More than 225,000 tech workers have been laid off in the last year, which is having a direct effect on SaaS renewal and purchase cycles.

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

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

How to pitch CVCs

CVC, corporate venture capital,
CVC, corporate venture capital,

Image Credits: Getty Images

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

PitchBook found that CVCs played a part in 56.2% of all venture deals that took place last year, “up only a hair over 2021’s 25.6%,” reports Rebecca Szkutak, who spoke to a few experts to find out 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 want to work together, it would be hard for us to work together,” said Andrew Ferguson, VP of corporate development and ventures at Databricks.

10 tips for de-risking hardware products

Image Credits: Frisco / Getty Images

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

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

“You need to make decisions based on people’s actual behavior,” he said in an interview with TechCrunch+. “You need to make sure that the data you’re tracking is coming from the right types of people.”

Thinking about pulling the plug on your startup?

Close up of web page button on monitor screen
Close up of web page button on monitor screen

Image Credits: SEAN GLADWELL (opens in a new 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 find product-market fit “are going through an excruciating psychological journey.”

Entrepreneurs are indoctrinated to pursue success at all costs, but “chasing endless pivots trying to find PMF is a bridge to nowhere,” wrote Rajaram, who shared a story about a founder who returned funds to investors before winding down operations:

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

If 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 touch, 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.
Rolled dollar bills hang from a bonsai tree.

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

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

According to 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 large companies are “achieving returns on their data and AI investments.”