Extra Crunch roundup: 2 VC surveys, Tesla’s melt up, The Roblox Gambit, more

This has been quite a week.

Instead of walking backward through the last few days of chaos and uncertainty, here are three good things that happened:

  • Google employee Sara Robinson combined her interest in machine learning and baking to create AI-generated hybrid treats.
  • A breakthrough could make water desalination 30%-40% more effective.
  • Bianca Smith will become the first Black woman to coach a professional baseball team.

Despite many distractions in our first full week of the new year, we published a full slate of stories exploring different aspects of entrepreneurship, fundraising and investing.

We’ve already gotten feedback on this overview of subscription pricing models, and a look back at 2020 funding rounds and exits among Israel’s security startups was aimed at our new members who live and work there, along with international investors who are seeking new opportunities.

Plus, don’t miss our first investor surveys of 2021: one by Lucas Matney on social gaming, and another by Mike Butcher that gathered responses from Portugal-based investors on a wide variety of topics.

Thanks very much for reading Extra Crunch this week. I hope we can all look forward to a nice, boring weekend with no breaking news alerts.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist


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The Roblox Gambit

In February 2020, gaming platform Roblox was valued at $4 billion, but after announcing a $520 million Series H this week, it’s now worth $29.5 billion.

“Sure, you could argue that Roblox enjoyed an epic 2020, thanks in part to COVID-19,” writes Alex Wilhelm this morning. “That helped its valuation. But there’s a lot of space between $4 billion and $29.5 billion.”

Alex suggests that Roblox’s decision to delay its IPO and raise an enormous Series H was a grandmaster move that could influence how other unicorns will take themselves to market. “A big thanks to the gaming company for running this experiment for us.”

I asked him what inspired the headline; like most good ideas, it came to him while he was trying to get to sleep.

“I think that I had “The Queen’s Gambit somewhere in my head, so that formed the root of a little joke with myself. Roblox is making a strategic wager on method of going public. So, ‘gambit’ seems to fit!”

8 investors discuss social gaming’s biggest opportunities

girl playing games on desktop computer

Image Credits: Erik Von Weber (opens in a new window) / Getty Images

For our first investor survey of the year, Lucas Matney interviewed eight VCs who invest in massively multiplayer online games to discuss 2021 trends and opportunities:

  • Hope Cochran, Madrona Venture Group
  • Daniel Li, Madrona Venture Group
  • Niko Bonatsos, General Catalyst
  • Ethan Kurzweil, Bessemer Venture Partners
  • Sakib Dadi, Bessemer Venture Partners
  • Jacob Mullins, Shasta Ventures
  • Alice Lloyd George, Rogue
  • Gigi Levy-Weiss, NFX

Having moved far beyond shooters and sims, platforms like Twitch, Discord and Fortnite are “where culture is created,” said Daniel Li of Madrona.

Rep. Alexandria Ocasio-Cortez uses Twitch to explain policy positions, major musicians regularly perform in-game concerts on Fortnite and in-game purchases generated tens of billions last year.

“Gaming is a unique combination of science and art, left and right brain,” said Gigi Levy-Weiss of NFX. “It’s never just science (i.e., software and data), which is why many investors find it hard.”

How to convert customers with subscription pricing

Giant hand and magnet picking up office and workers

Image Credits: C.J. Burton (opens in a new window) / Getty Images

Startups that lack insight into their sales funnel have high churn, low conversion rates and an inability to adapt or leverage changes in customer behavior.

If you’re hoping to convert and retain customers, “reinforcing your value proposition should play a big part in every level of your customer funnel,” says Joe Procopio, founder of Teaching Startup.

What is up with Tesla’s value?

Elon Musk, founder of SpaceX and chief executive officer of Tesla Inc., arrives at the Axel Springer Award ceremony in Berlin, Germany, on Tuesday, Dec. 1, 2020. Tesla Inc. will be added to the S&P 500 Index in one shot on Dec. 21, a move that will ripple through the entire market as money managers adjust their portfolios to make room for shares of the $538 billion company. Photographer: Liesa Johannssen-Koppitz/Bloomberg via Getty Images

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

Alex Wilhelm followed up his regular Friday column with another story that tries to find a well-grounded rationale for Tesla’s sky-high valuation of approximately $822 billion.

Meanwhile, GM just unveiled a new logo and tagline.

As ever, I learned something new while editing: A “melt up” occurs when investors start clamoring for a particular company because of acute FOMO (the fear of missing out).

Delivering 500,000 cars in 2020 was “impressive,” says Alex, who also acknowledged the company’s ability to turn GAAP profits, but “pride cometh before the fall, as does a melt up, I think.”

Note: This story has Alex’s original headline, but I told him I would replace the featured image with a photo of someone who had very “richest man in the world” face.

How Segment redesigned its core systems to solve an existential scaling crisis

Abstract glowing grid and particles

Image Credits: piranka / Getty Images

On Tuesday, enterprise reporter Ron Miller covered a major engineering project at customer data platform Segment called “Centrifuge.”

“Its purpose was to move data through Segment’s data pipes to wherever customers needed it quickly and efficiently at the lowest operating cost,” but as Ron reports, it was also meant to solve “an existential crisis for the young business,” which needed a more resilient platform.

Dear Sophie: Banging my head against the wall understanding the US immigration system

Image Credits: Sophie Alcorn

Dear Sophie:

Now that the U.S. has a new president coming in whose policies are more welcoming to immigrants, I am considering coming to the U.S. to expand my company after COVID-19. However, I’m struggling with the morass of information online that has bits and pieces of visa types and processes.

Can you please share an overview of the U.S. immigration system and how it works so I can get the big picture and understand what I’m navigating?

— Resilient in Romania

The first “Dear Sophie” column of each month is available on TechCrunch without a paywall.

Revenue-based financing: The next step for private equity and early-stage investment

Shot of a group of people holding plants growing out of soil

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

For founders who aren’t interested in angel investment or seeking validation from a VC, revenue-based investing is growing in popularity.

To gain a deeper understanding of the U.S. RBI landscape, we published an industry report on Wednesday that studied data from 134 companies, 57 funds and 32 investment firms before breaking out “specific verticals and business models … and the typical profile of companies that access this form of capital.”

Lisbon’s startup scene rises as Portugal gears up to be a European tech tiger

Man using laptop at 25th of April Bridge in Lisbon, Portugal

Image Credits: Westend61 (opens in a new window)/ Getty Images

Mike Butcher continues his series of European investor surveys with his latest dispatch from Lisbon, where a nascent startup ecosystem may get a Brexit boost.

Here are the Portugal-based VCs he interviewed:

  • Cristina Fonseca, partner, Indico Capital Partners
  • Pedro Ribeiro Santos, partner, Armilar Venture Partners
  • Tocha, partner, Olisipo Way
  • Adão Oliveira, investment manager, Portugal Ventures
  • Alexandre Barbosa, partner, Faber
  • António Miguel, partner, Mustard Seed MAZE
  • Jaime Parodi Bardón, partner, impACT NOW Capital
  • Stephan Morais, partner, Indico Capital Partners
  • Gavin Goldblatt, managing partner, Portugal Gateway

How late-stage edtech companies are thinking about tutoring marketplaces

Life Rings flying out beneath storm clouds are a metaphor for rescue, help and aid.

Image Credits: John Lund (opens in a new window)/ Getty Images

How do you scale online tutoring, particularly when demand exceeds the supply of human instructors?

This month, Chegg is replacing its seven-year-old marketplace that paired students with tutors with a live chatbot.

A spokesperson said the move will “dramatically differentiate our offerings from our competitors and better service students,” but Natasha Mascarenhas identified two challenges to edtech automation.

“A chatbot won’t work for a student with special needs or someone who needs to be handheld a bit more,” she says. “Second, speed tutoring can only work for a specific set of subjects.”

Decrypted: How bad was the US Capitol breach for cybersecurity?

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

While I watched insurrectionists invade and vandalize the U.S. Capitol on live TV, I noticed that staffers evacuated so quickly, some hadn’t had time to shut down their computers.

Looters even made off with a laptop from Senator Jeff Merkley’s office, but according to security reporter Zack Whittaker, the damages to infosec wasn’t as bad as it looked.

Even so, “the breach will likely present a major task for Congress’ IT departments, which will have to figure out what’s been stolen and what security risks could still pose a threat to the Capitol’s network.”

Extra Crunch’s top 10 stories of 2020

On New Year’s Eve, I made a list of the 10 “best” Extra Crunch stories from the previous 12 months.

My methodology was personal: From hundreds of posts, these were the 10 I found most useful, which is my key metric for business journalism.

Some readers are skeptical about paywalls, but without being boastful, Extra Crunch is a premium product, just like Netflix or Disney+. I know, we’re not as entertaining as a historical drama about the reign of Queen Elizabeth II or a space western about a bounty hunter. But, speaking as someone who’s worked at several startups, Extra Crunch stories contain actionable information you can use to build a company and/or look smart in meetings — and that’s worth something.


By Walter Thompson

How artificial intelligence will be used in 2021

Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

How 2020 shaped up for AI

Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.


By Kirsten Korosec

Arthur.ai snags $15M Series A to grow machine learning monitoring tool

At a time when more companies are building machine learning models, Arthur.ai wants to help by ensuring the model accuracy doesn’t begin slipping over time, thereby losing its ability to precisely measure what it was supposed to. As demand for this type of tool has increased this year, in spite of the pandemic, the startup announced a $15 million Series A today.

The investment was led by Index Ventures with help from new comers Acrew and Plexo Capital along with previous investors Homebrew, AME Ventures and Work-Bench.The round comes almost exactly a year after its $3.3 million seed round.

As CEO and co-founder Adam Wenchel explains, data scientists build and test machine learning models in the lab under ideal conditions, but as these models are put into production, the performance can begin to deteriorate under real world scrutiny. Arthur.AI is designed to root out when that happens.

Even as COVID has wreaked havoc throughout much of this year, the company has grown revenue 300% in the last six months smack dab in the middle of all that. “Over the course of 2020, we have begun to open up more and talk to [more] customers. And so we are starting to get some really nice initial customer traction, both in traditional enterprises as well as digital tech companies,” Wenchel told me. With 15 customers, the company is finding that the solution is resonating with companies.

It’s interesting to note that AWS announced a similar tool yesterday at re:Invent called SageMaker Clarify, but Wenchel sees this as more of a validation of what his startup has been trying to do, rather than an existential threat. “I think it helps create awareness, and because this is our 100% focus, our tools go well beyond what the major cloud providers provide,” he said.

Investor Mike Volpi from Index certainly sees the value proposition of this company. “One of the most critical aspects of the AI stack is in the area of performance monitoring and risk mitigation. Simply put, is the AI system behaving like it’s supposed to?,” he wrote in a blog post announcing the funding.

When we spoke a year ago, the company had 8 employees. Today it has 17 and it expects to double again by the end of next year. Wenchel says that as a company whose products looks for different types of bias, it’s especially important to have a diverse workforce. He says that starts with having a diverse investment team and board makeup, which he has been able to achieve, and goes from there.

“We’ve sponsored and work with groups that focus on both general sort of coding for different underrepresented groups as well as specifically AI, and that’s something that we’ll continue to do. And actually I think when we can get together for in person events again, we will really go out there and support great organizations like AI for All and Black Girls Code,” he said. He believes that by working with these groups, it will give the startup a pipeline to underrepresented groups, which they can draw upon for hiring as the needs arise.

Wenchel says that when he can go back to the office, he wants to bring employees back, at least for part of the week for certain kinds of work that will benefit from being in the same space.


By Ron Miller

AWS announces SageMaker Clarify to help reduce bias in machine learning models

As companies rely increasingly on machine learning models to run their businesses, it’s imperative to include anti-bias measures to ensure these models are not making false or misleading assumptions. Today at AWS re:Invent, AWS introduced Amazon SageMaker Clarify to help reduce bias in machine learning models.

“We are launching Amazon SageMaker Clarify. And what that does is it allows you to have insight into your data and models throughout your machine learning lifecycle,” Bratin Saha, Amazon VP and general manager of machine learning told TechCrunch.

He says that it is designed to analyze the data for bias before you start data prep, so you can find these kinds of problems before you even start building your model.

“Once I have my training data set, I can [look at things like if I have] an equal number of various classes, like do I have equal numbers of males and females or do I have equal numbers of other kinds of classes, and we have a set of several metrics that you can use for the statistical analysis so you get real insight into easier data set balance,” Saha explained.

After you build your model, you can run SageMaker Clarify again to look for similar factors that might have crept into your model as you built it. “So you start off by doing statistical bias analysis on your data, and then post training you can again do analysis on the model,” he said.

There are multiple types of bias that can enter a model due to the background of the data scientists building the model, the nature of the data and how they data scientists interpret that data through the model they built. While this can be problematic in general it can also lead to racial stereotypes being extended to algorithms. As an example, facial recognition systems have proven quite accurate at identifying white faces, but much less so when it comes to recognizing people of color.

It may be difficult to identify these kinds of biases with software as it often has to do with team makeup and other factors outside the purview of a software analysis tool, but Saha says they are trying to make that software approach as comprehensive as possible.

“If you look at SageMaker Clarify it gives you data bias analysis, it gives you model bias analysis, it gives you model explainability it gives you poor inference explainability it gives you a global explainability,” Saha said.

Saha says that Amazon is aware of the bias problem and that is why it created this tool to help, but he recognizes that this tool alone won’t eliminate all of the bias issues that can crop up in machine learning models, and they offer other ways to help too.

“We are also working with our customers in various ways. So we have documentation, best practices, and we point our customers to how to be able to architect their systems and work with the system so they get the desired results,” he said.

SageMaker Clarify is available starting to day in multiple regions.


By Ron Miller

Tecton.ai nabs $35M Series B as it releases machine learning feature store

Tecton.ai, the startup founded by three former Uber engineers who wanted to bring the machine learning feature store idea to the masses, announced a $35 million Series B today, just seven months after announcing their $20 million Series A.

When we spoke to the company in April, it was working with early customers in a beta version of the product, but today, in addition to the funding they are also announcing the general availability of the platform.

As with their Series A, this round has Andreessen Horowitz and Sequoia Capital coming back to co-lead the investment. The company has now raised $60 million.

The reason these two firms are so committed to Tecton is the specific problem around machine learning the company is trying to solve. “We help organizations put machine learning into production. That’s the whole goal of our company, helping someone build an operational machine learning application, meaning an application that’s powering their fraud system or something real for them […] and making it easy for them to build and deploy and maintain,” company CEO and co-founder Mike Del Balso explained.

They do this by providing the concept of a feature store, an idea they came up with and which is becoming a machine learning category unto itself. Just last week, AWS announced the Sagemaker Feature store, which the company saw as major validation of their idea.

As Tecton defines it, a feature store is an end-to-end machine learning management system that includes the pipelines to transform the data into what are called feature values, then it stores and manages all of that feature data and finally it serves a consistent set of data.

Del Balso says this works hand-in-hand with the other layers of a machine learning stack. “When you build a machine learning application, you use a machine learning stack that could include a model training system, maybe a model serving system or an MLOps kind of layer that does all the model management, and then you have a feature management layer, a feature store which is us — and so we’re an end-to-end lifecycle for the data pipelines,” he said.

With so much money behind the company it is growing fast, going from 17 employees to 26 since we spoke in April with plans to more than double that number by the end of next year. Del Balso says he and his co-founders are committed to building a diverse and inclusive company, but he acknowledges it’s not easy to do.

“It’s actually something that we have a primary recruiting initiative on. It’s very hard, and it takes a lot of effort, it’s not something that you can just make like a second priority and not take it seriously,” he said. To that end, the company has sponsored and attended diversity hiring conferences and has focused its recruiting efforts on finding a diverse set of candidates, he said.

Unlike a lot of startups we’ve spoken to, Del Balso wants to return to an office setup as soon as it is feasible to do so, seeing it as a way to build more personal connections between employees.


By Ron Miller

Salesforce applies AI to workflow with Einstein Automate

While Salesforce made a big splash yesterday with the announcement that it’s buying Slack for $27.7 billion, it’s not the only thing going on for the CRM giant this week. In fact Dreamforce, the company’s customer extravaganza is also on the docket. While it is virtual this year, there are still product announcements aplenty and today the company announced Einstein Automate, a new AI-fueled set of workflow solutions.

Sarah Franklin, EVP & GM of Platform, Trailhead and AppExchange at Salesforce says that she is seeing companies facing a digital imperative to automate processes as things move ever more quickly online, being driven there even faster by the pandemic. “With Einstein Automate, everyone can change the speed of work and be more productive through intelligent workflow automation,” she said in a statement.

Brent Leary, principal analyst at CRM Essentials says that combined these tools are designed to help customers get to work more quickly. “It’s not only about identifying the insight, it’s about making it easier to leverage it at the the right time. And this should make it easier for users to do it without spending more time and effort,” Leary told TechCrunch.

Einstein is the commercial name given to Salesforce’s artificial intelligence platform that touches every aspect of the company’s product line, bringing automation to many tasks and making it easier to find the most valuable information on customers, which is often buried in an avalanche of data.

Einstein Automate encompasses several products designed to improve workflows inside organizations. For starters, the company has created Flow Orchestrator, a tool that uses a low-code, drag and drop approach for building workflows, but it doesn’t stop there. It also relies on AI to provide help suggest logical next steps to speed up workflow creation.

Salesforce is also bringing Mulesoft, the integration company it bought for $6.5 billion in 2018 into the mix. Instead of processes like a mortgage approval workflow, the Mulesoft piece lets IT build complex integrations between applications across the enterprise, and the Salesforce family of products more easily.

To make it easier to build these workflows, Salesforce is announcing the Einstein Automate collection page available in AppExchange, the company’s application marketplace. The collection includes over 700 pre-built connectors so customers can grab and go as they build these workflows, and finally it’s updating the OmniStudio, their platform for generating customer experiences. As Salesforce describes it, “Included in OmniStudio is a suite of resources and no-code tools, including pre-built guided experiences, templates and more, allowing users to deploy digital-first experiences like licensing and permit applications quickly and with ease. ”

Per usual with Salesforce Dreamforce announcements, the Flow Orchestrator being announced today won’t be available in beta until next summer. The Mulesoft component will be available in early 2021, but the OmniStudio updates and the Einstein connections collection are available today.


By Ron Miller

Industrial drone maker Percepto raises $45M and integrates with Boston Dynamics’ Spot

Consumer drones have over the years struggled with an image of being no more than expensive and delicate toys. But applications in industrial, military and enterprise scenarios have shown that there is indeed a market for unmanned aerial vehicles, and today, a startup that makes drones for some of those latter purposes is announcing a large round of funding and a partnership that provides a picture of how the drone industry will look in years to come.

Percepto, which makes drones — both the hardware and software — to monitor and analyze industrial sites and other physical work areas largely unattended by people, has raised $45 million in a Series B round of funding.

Alongside this, it is now working with Boston Dynamics  and has integrated its Spot robots with Percepto’s Sparrow drones, with the aim being better infrastructure assessments, and potentially more as Spot’s agility improves.

The funding is being led by a strategic backer, Koch Disruptive Technologies, the investment arm of industrial giant Koch Industries (which has interests in energy, minerals, chemicals and related areas), with participation also from new investors State of Mind Ventures, Atento Capital, Summit Peak Investments, Delek-US. Previous investors U.S. Venture Partners, Spider Capital and Arkin Holdings also participated. (It appears that Boston Dynamics and SoftBank are not part of this investment.)

Israel-based Percepto has now raised $72.5 million since it was founded in 2014, and it’s not disclosing its valuation, but CEO and founder Dor Abuhasira described as “a very good round.”

“It gives us the ability to create a category leader,” Abuhasira said in an interview. It has customers in around 10 countries, with the list including ENEL, Florida Power and Light and Verizon.

While some drone makers have focused on building hardware, and others are working specifically on the analytics, computer vision and other critical technology that needs to be in place on the software side for drones to work correctly and safely, Percepto has taken what I referred to, and Abuhasira confirmed, as the “Apple approach”: vertical integration as far as Percepto can take it on its own.

That has included hiring teams with specializations in AI, computer vision, navigation and analytics as well as those strong in industrial hardware — all strong areas in the Israel tech landscape, by virtue of it being so closely tied with its military investments. (Note: Percepto does not make its own chips: these are currently acquired from Nvidia, he confirmed to me.)

“The Apple approach is the only one that works in drones,” he said. “That’s because it is all still too complicated. For those offering an Android-style approach, there are cracks in the complete flow.”

It presents the product as a “drone-in-a-box”, which means in part that those buying it have little work to do to set it up to work, but also refers to how it works: its drones leave the box to make a flight to collect data, and then return to the box to recharge and transfer more information, alongside the data that is picked up in real time.

The drones themselves operate on an on-demand basis: they fly in part for regular monitoring, to detect changes that could point to issues; and they can also be launched to collect data as a result of engineers requesting information. The product is marketed by Percepto as “AIM”, short for autonomous site inspection and monitoring.

News broke last week that Amazon has been reorganising its Prime Air efforts — one sign of how some more consumer-facing business applications — despite many developments — may still have some turbulence ahead before they are commercially viable. Businesses like Percepto’s stand in contrast to that, with their focus specifically on flying over, and collecting data, in areas where there are precisely no people present.

It has dovetailed with a bigger focus from industries on the efficiencies (and cost savings) you can get with automation, which in turn has become the centerpiece of how industry is investing in the buzz phrase of the moment, “digital transformation.”

“We believe Percepto AIM addresses a multi-billion-dollar issue for numerous industries and will change the way manufacturing sites are managed in the IoT, Industry 4.0 era,” said Chase Koch, President of Koch Disruptive Technologies, in a statement. “Percepto’s track record in autonomous technology and data analytics is impressive, and we believe it is uniquely positioned to deliver the remote operations center of the future. We look forward to partnering with the Percepto team to make this happen.”

The partnership with Boston Dynamics is notable for a couple of reasons: it speaks to how various robotics hardware will work together in tandem in an automated, unmanned world; and it speaks to how Boston Dynamics is pulling up its socks.

On the latter front, the company has been making waves in the world of robotics for years, specifically with its agile and strong dog-like (with names like “Spot” and “Big Dog”) robots that can cover rugged terrains and handle tussles without falling apart.

That led it into the arms of Google, which acquired it as part of its own secretive moonshot efforts, in 2013. That never panned out into a business, and probably gave Google more complicated optics at a time when it was already being seen as too powerful. Then, SoftBank stepped in to pick it up, along with other robotics assets, in 2017. That hasn’t really gone anywhere either, it seems, and just this month it was reported that Boston Dynamics was reportedly facing yet another suitor, Hyundai.

All of this is to say that partnerships with third parties that are going places (quite literally) become strong signs of how Boston Dynamics’ extensive R&D investments might finally pay off with enterprising dividends.

Indeed, while Percepto has focused on its own vertical integration, longer term and more generally there is an argument to be made for more interoperability and collaboration between the various companies building “connected” and smart hardware for industrial, physical applications. It means that specific industries can focus on the special equipment and expertise they require, while at the same time complementing that with hardware and software that are recognised as best-in-class. Abuhasira said that he expects the Boston Dynamics partnership to be the first of many.

That makes this first one an interesting template. It will see Spot carrying Percepto’s payloads for high resolution imaging and thermal vision “to detect issues including hot spots on machines or electrical conductors, water and steam leaks around plants and equipment with degraded performance, with the data relayed via AIM.” It will also mean a more thorough picture, beyond what you get from the air, and potentially a point at which the data that the pairing sources results even in repairs or other work to fix issues.

“Combining Percepto’s Sparrow drone with Spot creates a unique solution for remote inspection,” said Michael Perry, VP of Business Development at Boston Dynamics, in a statement. “This partnership demonstrates the value of harnessing robotic collaborations and the insurmountable benefits to worker safety and cost savings that robotics can bring to industries that involve hazardous or remote work.”


By Ingrid Lunden

Onit acquires legal startup McCarthyFinch to inject AI into legal workflows

Onit, a workflow software company based in Houston with a legal component, announced this week that it has acquired 2018 TechCrunch Disrupt Battlefield alum McCarthyFinch.  Onit intends to use the startup’s AI skills to beef up its legal workflow software offerings.

The companies did not share the purchase price.

After evaluating a number of companies in the space, Onit focused on McCarthyFinch, which gives it an artificial intelligence component the company’s legal workflow software had been lacking. “We evaluated about a dozen companies in the AI space and dug in deep on six of them. McCarthyFinch stood out from the pack. They had the strongest technology and the strongest team,” Eric M. Elfman, CEO and co-founder of Onit told TechCrunch.

The company intends to inject that AI into its existing Aptitude workflow platform.”Part of what really got me excited about McCarthyFinch was the very first conversation I had with their CEO, Nick Whitehouse. They considered themselves an AI platform, which complemented our approach and our workflow automation platform, Aptitude,” Elfman said.

McCarthyFinch CEO and co-founder Whitehouse says the startup was considering whether to raise more money or look at being acquired earlier this year when Onit made its interest known. At first, he wasn’t really interested in being acquired and was hoping to go the partner route, but over time that changed.

“I was very much on the partner track, and was probably quite dismissive to begin with because I was quite focused on that partner strategy. But as we talked, all egos aside, it just made sense [to move to acquisition talks],” Whitehouse said.

The talks heated up in May and the deal officially closed last week. With Onit, headquartered in Houston and McCarthyFinch in New Zealand, the negotiations and meetings all happened on Zoom. The two companies’ principals have never met in person. The plan is for McCarthyFinch to stay in place, even after the pandemic ends. Whitehouse expects to make a trip to Houston whenever it is safe to do so.

Whitehouse says his experience with Battlefield has had a huge influence on him. “Just the insights that we got through Battlefield, the coaching that we got, those things have stuck with me and they’ll stick with me for the rest of my life,” he said.

The company had 45 customers and 17 employees at the time of the acquisition. It raised $5 million US dollars along the way. Now it becomes part of Onit as the journey continues.


By Ron Miller

IBM is acquiring APM startup Instana as it continues to expand hybrid cloud vision

As IBM transitions from software and services to a company fully focussed on hybrid cloud management, it announced  its intention to buy Instana, an applications performance management startup with a cloud native approach that fits firmly within that strategy.

The companies did not reveal the purchase price.

With Instana, IBM can build on its internal management tools, giving it a way to monitor containerized environments running Kubernetes. It hopes by adding the startup to the fold it can give customers a way to manage complex hybrid and multi-cloud environments.

“Our clients today are faced with managing a complex technology landscape filled with mission-critical applications and data that are running across a variety of hybrid cloud environments – from public clouds, private clouds and on-premises,” Rob Thomas, senior vice president for cloud and data platform said in a statement. He believes Instana will help ease that load, while using machine learning to provide deeper insights.

At the time of the company’s $30 million Series C in 2018, TechCrunch’s Frederic Lardinois described the company this way. “What really makes Instana stand out is its ability to automatically discover and monitor the ever-changing infrastructure that makes up a modern application, especially when it comes to running containerized microservices.” That would seem to be precisely the type of solution that IBM would be looking for.

As for Instana, the founders see a good fit for the two companies, especially in light of the Red Hat acquisition in 2018 that is core to IBM’s hybrid approach. “The combination of Instana’s next generation APM and Observability platform with IBM’s Hybrid Cloud and AI technologies excited me from the day IBM approached us with the idea of joining forces and combining our technologies,” CEO Mirko Novakovic wrote in a blog post announcing the deal.

Indeed, in a recent interview IBM CEO Arvind Krishna told CNBC’s Jon Fortt, that they are betting the farm on hybrid cloud management with Red Hat at the center. When you combine that with the decision to spin out the company’s managed infrastructure services business, this purchase shows that they intend to pursue every angle

“The Red Hat acquisition gave us the technology base on which to build a hybrid cloud technology platform based on open-source, and based on giving choice to our clients as they embark on this journey. With the success of that acquisition now giving us the fuel, we can then take the next step, and the larger step, of taking the managed infrastructure services out. So the rest of the company can be absolutely focused on hybrid cloud and artificial intelligence,” Krishna told CNBC.

Instana, which is based in Chicago with offices in Munich, was founded in 2015 in the early days of Kubernetes and the startup’s APM solution has evolved to focus more on the needs of monitoring in a cloud native environment. The company raised $57 million along the way with the most recent round being that Series C in 2018.

The deal per usual is subject to regulatory approvals, but the company believes it should close in the next few months.


By Ron Miller

Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups. Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 


By Frederic Lardinois

Computer vision startup Chooch.ai scores $20M Series A

Chooch.ai, a startup that hopes to bring computer vision more broadly to companies to help them identify and tag elements at high speed, announced a $20 million Series A today.

Vickers Venture Partners led the round with participation from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and several other unnamed investors. Today’s investment brings the total raised to $25.8 million, according to the company.

“Basically we set out to copy human visual intelligence in machines. That’s really what this whole journey is about,” CEO and co-founder Emrah Gultekin explained. As the company describes it, “Chooch Al can rapidly ingest and process visual data from any spectrum, generating AI models in hours that can detect objects, actions, processes, coordinates, states, and more.”

Chooch is trying to differentiate itself from other AI startups by taking a broader approach that could work in any setting, rather than concentrating on specific vertical applications. Using the pandemic as an example, Gultekin says you could use his company’s software to identify everyone who is not wearing a mask in the building or everyone who is not wearing a hard hat at construction site.

 

With 22 employees spread across the U.S., India and Turkey, Chooch is building a diverse company just by virtue of its geography, but as it doubles the workforce in the coming year, it wants to continue to build on that.

“We’re immigrants. We’ve been through a lot of different things, and we recognize some of the issues and are very sensitive to them. One of our senior members is a person of color and we
are very cognizant of the fact that we need to develop that part of our company,” he said. At a recent company meeting, he said that they were discussing how to build diversity into the policies and values of the company as they move forward.

The company currently has 18 enterprise clients and hopes to use the money to add engineers, data scientists and begin to build out a worldwide sales team to continue to build the product and expand its go-to-market effort.

Gultekin says that the company’s unusual name comes from a mix of the words choose and search. He says that it is also an old Italian insult. “It means dummy or idiot, which is what artificial intelligence is today. It’s a poor reflection of humanity or human intelligence in humans,” he said. His startup aims to change that.


By Ron Miller

Databricks launches SQL Analytics

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a ‘lake house’ to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databrick’s co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But ‘lake house’ caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, Chief Product Officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience that Databricks users are familiar with. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.


By Frederic Lardinois

Intel has acquired Cnvrg.io, a platform to manage, build and automate machine learning

Intel continues to snap up startups to build out its machine learning and AI operations. In the latest move, TechCrunch has learned that the chip giant has acquired Cnvrg.io, an Israeli company that has built and operates a platform for data scientists to build and run machine learning models, which can be used to train and track multiple models and run comparisons on them, build recommendations and more.

Intel confirmed the acquisition to us with a short note. “We can confirm that we have acquired Cnvrg,” a spokesperson said. “Cnvrg will be an independent Intel company and will continue to serve its existing and future customers.” Those customers include Lightricks, ST Unitas and Playtika.

Intel is not disclosing any financial terms of the deal, nor who from the startup will join Intel. Cnvrg, co-founded by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from investors that include Hanaco Venture Capital and Jerusalem Venture Partners and PitchBook estimates that it was valued at around $17 million in its last round. 

It was only a week ago that Intel made another acquisition to boost its AI business, also in the area of machine learning modeling: it picked up SigOpt, which had developed an optimization platform to run machine learning modeling and simulations.

While SigOpt is based out of the Bay Area, Cnvrg is in Israel and joins an extensive footprint that Intel has built in the country specifically in the area of artificial intelligence research and development, banked around its Mobileye autonomous vehicle business (which it acquired for more than $15 billion in 2017) and its acquisition of AI chipmaker Habana (which it acquired for $2 billion at the end of 2019).

Cnvrg.io’s platform works across on-premise, cloud and hybrid environments and it comes in paid and free tiers (we covered the launch of the free service, branded Core, last year). It competes with the likes of Databricks, Sagemaker and Dataiku as well as smaller operations like H2O.ai that are built on open source frameworks. Cnvrg’s premise is that it provides a user-friendly platform for data scientists so that they can concentrate on devising algorithms and measuring how they work, not building or maintaining the platform that they run on.

While Intel is not saying much about the deal, it seems that some of the same logic behind last week’s SigOpt acquisition applies here as well: Intel has been refocusing its business around next-generation chips to better compete against the likes of Nvidia and smaller players like GraphCore. So it makes sense to also provide/invest in AI tools for customers, specifically services to help with the compute loads that they will be running on those chips.

It’s notable that in our article about the Core free tier last year, Frederic noted that those using the platform in the cloud can do so with Nvidia-optimized containers that run on a Kubernetes cluster. It’s not clear if that will continue to be the case, or if containers will be optimized instead for Intel architecture, or both. Cnvrg’s other partners include Red Hat and NetApp.

Intel’s focus on the next generation of computing aims to offset declines in its legacy operations. In the last quarter, Intel reported a 3% decline in its revenues, led by a drop in its data center business. It said that it’s projecting the AI silicon market to be bigger than $25 billion by 2024, with AI silicon in the data center to be greater than $10 billion in that period.

In 2019, Intel reported some $3.8 billion in AI-driven revenue, but it hopes that tools like SigOpt’s will help drive more activity in that business, dovetailing with the push for more AI applications in a wider range of businesses.


By Ingrid Lunden

Leena AI nabs $8M Series as it expands from chatbots to HR service platform

When we covered Leena AI as a member of the Y Combinator Summer 2018 cohort, the young startup was firmly focused on building HR chatbots, but in the intervening years it has expanded the vision to a broader HR policy platform. Today, the company announced an $8 million Series A led by Greycroft with help from several individual industry investors.

Company CEO and co-founder Adit Jain says that in 2018 the company was concentrating on building an intelligent virtual assistant for HR-related questions. It allowed employees to ask the bot questions like how many vacation days they have left or what holidays they have off this year.

Over the last couple of years since leaving Y Combinator, the company has moved into broader HR service delivery. “So I’m talking about having an intelligent case management, knowledge management and document management system, which is backing the virtual assistant as well,” Jain explained.

He says that users should think of it as an entire system where the chatbot is the user interface for employees to interact with the HR information on the back end. For example, he says that the knowledge management component is where the chatbots find the answers to questions, and as employees interact with the chatbot, it grows more intelligent based on the feedback from them.

The document management piece enables HR to write or import HR policies and the case management system comes into play when the situation is too complex for the chatbot to handle and it has to be escalated to a human HR representative.

When we spoke to Jain in September 2018 at the time of his startup’s $2 million seed round, he had 16 customers and hoped to have 50 in the next 12-18 months. Today the company actually has 100 enterprise customers with 300,000 employees using the platform worldwide.

In fact, the pandemic has fueled business with more than half of those customers coming on board this year. He says this is because companies are looking for ways to digitize processes like HR as employees are working from home more.

“This is a trend that’s going to continue as organizations have realized the value of doing things with more and more digital applications taking care of your processes […] especially mundane, repeatable tasks being handed over to technology more and more,” Jain said.

As the business has grown this year, the company has expanded from 30 to 75 employees and he hopes to double that number in the next year. As he does, he has discussed with his lead investor how to build a diverse and inclusive culture at Leena AI .

One thing he is trying to do is raise some money from a diverse group of investors, approximately $400,000, and his hope is that these diverse investors can help him build solid diversity programs as he adds employees to his growing company.

That the startup hasn’t only grown during these turbulent times, but thrived shows that companies are looking to modernize every part of the enterprise technology stack, and that includes HR.


By Ron Miller

Intel acquires SigOpt, a specialist in modeling optimization, to boost its AI business

Intel has been doubling down on building chips and related architecture for the next generation of computing, and today it announced an acquisition that will bolster its expertise and work specifically in one area of future technology: artificial intelligence.

The semiconductor giant today announced that it has acquired SigOpt, a startup out of San Francisco that has built an optimization platform that can be used to run modeling and simulations (two key applications of AI tech) in a better way. Anthony described SigOpt as a startup built to “optimize everything” when we covered its Series A last year, but Intel specifically will be integrating the tech into its AI business, specifically into its AI Analytics Toolkit, a spokesperson tells me.

Terms of the deal were not disclosed but SigOp already counted a number of large enterprises — “SigOpt’s customer base includes Fortune 500 companies across industries, as well as leading research institutions, universities and consortiums using its products” — among its customers. The product was still in a closed beta, however. Notably, it had raised money from an interesting group of investors that included In-Q-Tel (the firm associated with the CIA that makes strategic investments) and Andreessen Horowitz, and Y Combinator, among others. It had raised less than $10 million.

The plan will be to continue providing services to existing users, and to continue building out the company’s platform — co-founders Scott Clark (CEO) and Patrick Hayes (CTO) and their team are joining Intel.

“We will continue to work with SigOpt’s existing customers and will also integrate the technology into our product roadmap,” a spokesperson confirmed.

While Intel is working hard on streamlining its business around next-generation chips to better compete against the likes of NVIDIA (which itself is growing substantially with the acquisition of ARM) and smaller players like GraphCore, in part by divesting more legacy operations, it seems a strong opportunity in providing services for its customers alongside those chips, and these services specifically will help customers with the compute loads that they will be running on those chips.

The focus for Intel has been on the next generation of computing to offset declines in its legacy operations. In the last quarter, even as it beat expectations, Intel reported a 3% decline in its revenues, led by a drop in its data center business. It said that it’s projecting the AI silicon market to be bigger than $25 billion by 2024, with AI silicon in the data center to be greater than $10 billion in that period.

In 2019, Intel reported some $3.8 billion in AI-driven revenue but it hopes that tools like SigOpt’s will help drive more activity in that business, dovetailing with the push for more AI applications in a wider range of businesses.

“In the new intelligence era, AI is driving the compute needs of the future. It is even more important for software to automatically extract the best compute performance while scaling AI models,” said Raja Koduri, Intel’s chief architect and senior vice president of its discrete graphics division. “SigOpt’s AI software platform and data science talent will augment Intel software, architecture, product offerings and teams, and provide us with valuable customer insights. We welcome the SigOpt team and its customers to the Intel family.”

While there could potentially be a number of applications for SigOpt’s tech, this is a signal of how bigger players will continue to consolidate specific services around their bigger business, giving the small startup a much bigger horizon in terms of potential business (even if it is all tied to customers that only use Intel hardware).

“We are excited to join Intel and supercharge our mission to accelerate and amplify the impact of modelers everywhere. By combining our AI optimization software with Intel’s decades-long leadership in AI computing and machine learning performance, we will be able to unlock entirely new AI capabilities for modelers,” said Clark in a statement.


By Ingrid Lunden