Serve Robotics, the Uber and Nvidia-backed sidewalk robot delivery company, debuted publicly on the New York stock exchange Thursday, making it the latest startup to choose going public via a reverse merger as an alternative path to capital needed to fund growth.
While Serve’s debut in the public markets comes from a reverse merger and not a SPAC, the two alternate paths to IPO are not too dissimilar.
However, Serve Robotics said it’s expecting enormous growth fueled by money generated by going public.
“I never thought that I would start a robotics company and then be in the ads business,” said a tired, but excited, Kashani in a phone interview minutes before the bell rang.
Upon the closing of the merger, Uber held a 16.6% stake and Nvidia an 14.3% stake in Serve, according to regulatory filings.
The round values the company at €100 million ($107 million), post money.
But finmid believes it has the potential to lock in more business specifically in its home region.
Unlike a bank, Wolt has access to the restaurants’ sales history, and finmid helps it leverage that data to decide who will see a pre-approved financing offer.
The working capital doesn’t come from Wolt, but from finmid’s financing partners.
For a platform like Wolt, embedding finmid is a way to make life easier for restaurants while generating additional revenue without much additional effort.
It is, after all, a lot easier to generate press for robots that look and move like humans.
For a while now, Collaborative Robotics founder Brad Porter has eschewed robots that look like people.
As the two-year-old startup’s name implies, Collaborative Robotics (Cobot for short) is interested in the ways in which humans and robots will collaborate, moving forward.
When his run with the company ended in summer 2020, he was leading the retail giant’s industrial robotics team.
AI will, naturally, be foundational to the company’s promise of “human problem solving,” while the move away from the humanoid form factor is a bid, in part, to reduce the cost of entry for deploying these systems.
Was the fundraising process any more or less difficult this time given the market?
If you really rewind the clock, back in 2018 when we raised our sixteenth fund, it was a “normal” environment.
There’s obviously a valuation reset going on for every company seemingly that’s not a large language model company, which is a lot of companies.
I’d guess that gives you easier access to top companies, but also hurts some of your existing portfolio companies.
I don’t think anyone has ever reached a great venture outcome feeling like, ‘Man, I got a steal on that deal.’ You always feel slightly uncomfortable.
At one point, Levin asked what Pichai tried to do to keep a company of 200,000 people innovative against all the startups battling to disrupt its business.
So I think, I think how do you as a company move fast?
“We recently said, we went back to a notion we had in early Google of Google Labs.
How do you allow people to prototype more easily internally and get it out to people?”Later, Levin asked what advances Pichai was most excited about this year.
Levin and Pichai start around 1 hour and 18 minutes in.
Dubbed ‘Caracal,’ this new release emphasizes new features for hosting AI and high-performance computing (HPC) workloads.
Indeed, as the OpenInfra Foundation announced this week, its newest Platinum Member is Okestro, a South Korean cloud provider with a heavy focus on AI.
But Europe, with its strong data sovereignty laws, has also been a growth market and the UK’s Dawn AI supercomputer runs OpenStack, for example.
“All the things are lining up for a big upswing and open-source adoption for infrastructure,” OpenInfra Foundation COO Mark Collier told TechCrunch.
That’s in addition to networking updates to better support HPC workloads and a slew of other updates.
The Linux Foundation last week announced that it will host Valkey, a fork of the Redis in-memory data store.
Valkey is backed by AWS, Google Cloud, Oracle, Ericsson and Snap.
AWS and Google Cloud rarely back an open-source fork together.
At the time, Redis said that despite this change for the modules, “the license for open-source Redis was never changed.
This fork originated at AWS, where longtime Redis maintainer Madelyn Olson initially started the project in her own GitHub account.
Bumble’s new CEO talks about her critical mission: to spice things up at the company Tis the season for turnaround CEOsSince Bumble’s blockbuster IPO at the height of the pandemic, investors’ ardor with the dating service has cooled.
Part of it ties to AI, which Bumble’s rivals are also leaning into more heavily.
But as we approach our 10-year anniversary, it’s a great moment to think about how we best serve our mission.
Historically, what we’ve seen is that a lot of men will come to Bumble who believe in women being empowered.
Bumble has always been great at community-based marketing: hosting events and finding ambassadors who really want to represent the brand.
Brands want to use generative AI to personalize their marketing efforts — but they are also deathly afraid of AI going off message and ruining their brand.
At its annual Summit conference in Las Vegas, Adobe today announced GenStudio, a new application that helps brands create content and measure its performance, with generative AI — and the promise of brand safety — at its center.
Adobe wants GenStudio, which it first previewed last September, to be an end-to-end solution to help marketers tailor their content to different channels and audience segments.
That, of course, is where generative AI comes in, since it can speed up content creation dramatically.
The tools also continuously checks that anything a user creates in GenStudio is within a brand’s guidelines.
“Thoras essentially integrates alongside a cloud-based service and it consistently monitors the usage of that service,” company CEO Nilo Rahmani told TechCrunch.
They launched the company right after the first of the year and closed their pre-seed funding just a few weeks ago.
In terms of AI, the company currently uses more task-based machine learning than generative AI and large language models (LLMs).
“A lot of the problems that we’re facing are systemic issues, and there are a lot of numbers involved.
They see LLMs being more useful in troubleshooting after the fact at some point as they fill out the product.
