Mike Rothenberg, the once high-flying VC bent on bringing the party to Silicon Valley, must now pay a whopping $31.4 million to settle a California federal court ruling in favor of Security and Exchange Commission allegations.
TechCrunch deemed Rothenberg a “virtual Gatsby” back in 2016, when we first broke the news about the downfall of his venture capital firm, Rothenberg Ventures. It seemed he took it as a compliment, changing his Instagram handle to @virtualgatsby. Indeed, the name seemed appropriate for a man who seemingly lived a party-boy lifestyle and spent lavishly to woo startup founders — including going on Napa Valley wine tours, holding an annual “founder field day” where he rented the whole San Francisco Giants’ baseball stadium and spending unsparingly to executive produce a video for Coldplay.
But the party life came to a halt when top leadership jumped ship and the SEC started looking into the books. The SEC formally charged Rothenberg in August of 2018 for misappropriating millions of dollars of his investors’ capital and funneling that money into his own bank account. Rothenberg settled with the SEC at the time and, as part of the settlement, was barred from the brokerage and investment advisory business for five years.
Rothenberg was later caught up in several lawsuits, including one from Transcend VR for fraud and breach of contract, which ended in a settlement. Another suit between Rothenberg and his former CFO, David Haase, ended with Rothenberg being ordered to pay $166,000 in damages.
But there was more to come from the SEC, following a forensic audit in partnership with the firm Deloitte showing the misuse or misappropriation of $18.8 million in investor funding. Under that examination, Deloitte showed Rothenberg had used the money either personally, to float his flashy lifestyle, or for other extravagances, such as building a race car team and a virtual reality studio. Rothenberg has now been ordered to pay back the $18.8 million he took from investors, another $9 million in civil penalties, plus $3.7 million in interest.
Neither the SEC nor Rothenberg have responded for comment. It’s also important to note none of the charges so far have been criminal, but were handled in civil court, as the SEC does not handle criminal cases.
Through all of it, Rothenberg never admitted any guilt for his actions and it is important to note that, because of this he will be able to practice again after the bar is lifted in five years. He’s also made some decent early investments in startups like Robinhood, and many investor sources TechCrunch spoke to over the years seemed quite loyal to him as an investor, despite the charges, employee mass exodus and fund implosion that followed.
And it seems this saga is not over yet. Rothenberg told MarketWatch in a recent interview that he thought the ruling was, “historically excessive and vindictively punitive,” that he planned to appeal it and would be suing Silicon Valley Bank, which Rothenberg used to funnel several investments, over the matter.
Rothenberg Ventures already filed suit against Silicon Valley Bank in August of 2018, the same day the SEC filed formal charges against Rothenberg himself. In that suit, Rothenberg alleged negligence, fraud and deceit on the part of the bank and sought a trial before jury. Silicon Valley Bank said it would defend against the case at the time.
We’ve reached out to Silicon Valley Bank and are waiting to hear back. The real question is, if Rothenberg were to come back to investing in Silicon Valley, would anyone still trust him?
Venture capital investment exploded across a number of geographies in 2019 despite the constant threat of an economic downturn.
San Francisco, of course, remains the startup epicenter of the world, shutting out all other geographies when it comes to capital invested. Still, other regions continue to grow, raking in more capital this year than ever.
In Utah, a new hotbed for startups, companies like Weave, Divvy and MX Technology raised a collective $370 million from private market investors. In the Northeast, New York City experienced record-breaking deal volume with median deal sizes climbing steadily. Boston is closing out the decade with at least 10 deals larger than $100 million announced this year alone. And in the lovely Pacific Northwest, home to tech heavyweights Amazon and Microsoft, Seattle is experiencing an uptick in VC interest in what could be a sign the town is finally reaching its full potential.
Seattle startups raised a total of $3.5 billion in VC funding across roughly 375 deals this year, according to data collected by PitchBook. That’s up from $3 billion in 2018 across 346 deals and a meager $1.7 billion in 2017 across 348 deals. Much of Seattle’s recent growth can be attributed to a few fast-growing businesses.
Convoy, the digital freight network that connects truckers with shippers, closed a $400 million round last month bringing its valuation to $2.75 billion. The deal was remarkable for a number of reasons. Firstly, it was the largest venture round for a Seattle-based company in a decade, PitchBook claims. And it pushed Convoy to the top of the list of the most valuable companies in the city, surpassing OfferUp, which raised a sizable Series D in 2018 at a $1.4 billion valuation.
Convoy has managed to attract a slew of high-profile investors, including Amazon’s Jeff Bezos, Salesforce CEO Marc Benioff and even U2’s Bono and the Edge. Since it was founded in 2015, the business has raised a total of more than $668 million.
Remitly, another Seattle-headquartered business, has helped bolster Seattle’s startup ecosystem. The fintech company focused on international money transfer raised a $135 million Series E led by Generation Investment Management, and $85 million in debt from Barclays, Bridge Bank, Goldman Sachs and Silicon Valley Bank earlier this year. Owl Rock Capital, Princeville Global, Prudential Financial, Schroder & Co Bank AG and Top Tier Capital Partners, and previous investors DN Capital, Naspers’ PayU and Stripes Group also participated in the equity round, which valued Remitly at nearly $1 billion.
A number of other factors have contributed to Seattle’s long-awaited rise in venture activity. Top-performing companies like Stripe, Airbnb and Dropbox have established engineering offices in Seattle, as has Uber, Twitter, Facebook, Disney and many others. This, of course, has attracted copious engineers, a key ingredient to building a successful tech hub. Plus, the pipeline of engineers provided by the nearby University of Washington (shout-out to my alma mater) means there’s no shortage of brainiacs.
There’s long been plenty of smart people in Seattle, mostly working at Microsoft and Amazon, however. The issue has been a shortage of entrepreneurs, or those willing to exit a well-paying gig in favor of a risky venture. Fortunately for Seattle venture capitalists, new efforts have been made to entice corporate workers to the startup universe. Pioneer Square Labs, which I profiled earlier this year, is a prime example of this movement. On a mission to champion Seattle’s unique entrepreneurial DNA, Pioneer Square Labs cropped up in 2015 to create, launch and fund technology companies headquartered in the Pacific Northwest.
Boundless CEO Xiao Wang at TechCrunch Disrupt 2017
Operating under the startup studio model, PSL’s team of former founders and venture capitalists, including Rover and Mighty AI founder Greg Gottesman, collaborate to craft and incubate startup ideas, then recruit a founding CEO from their network of entrepreneurs to lead the business. Seattle is home to two of the most valuable businesses in the world, but it has not created as many founders as anticipated. PSL hopes that by removing some of the risk, it can encourage prospective founders, like Boundless CEO Xiao Wang, a former senior product manager at Amazon, to build.
“The studio model lends itself really well to people who are 99% there, thinking ‘damn, I want to start a company,’ ” PSL co-founder Ben Gilbert said in March. “These are people that are incredible entrepreneurs but if not for the studio as a catalyst, they may not have [left].”
Boundless is one of several successful PSL spin-outs. The business, which helps families navigate the convoluted green card process, raised a $7.8 million Series A led by Foundry Group earlier this year, with participation from existing investors Trilogy Equity Partners, PSL, Two Sigma Ventures and Founders’ Co-Op.
Years-old institutional funds like Seattle’s Madrona Venture Group have done their part to bolster the Seattle startup community too. Madrona raised a $100 million Acceleration Fund earlier this year, and although it plans to look beyond its backyard for its newest deals, the firm continues to be one of the largest supporters of Pacific Northwest upstarts. Founded in 1995, Madrona’s portfolio includes Amazon, Mighty AI, UiPath, Branch and more.
Voyager Capital, another Seattle-based VC, also raised another $100 million this year to invest in the PNW. Maveron, a venture capital fund co-founded by Starbucks mastermind Howard Schultz, closed on another $180 million to invest in early-stage consumer startups in May. And new efforts like Flying Fish Partners have been busy deploying capital to promising local companies.
There’s a lot more to say about all this. Like the growing role of deep-pocketed angel investors in Seattle have in expanding the startup ecosystem, or the non-local investors, like Silicon Valley’s best, who’ve funneled cash into Seattle’s talent. In short, Seattle deal activity is finally climbing thanks to top talent, new accelerator models and several refueled venture funds. Now we wait to see how the Seattle startup community leverages this growth period and what startups emerge on top.
Hello and welcome back to TechCrunch’s China Roundup, a digest of recent events shaping the Chinese tech landscape and what they mean to people in the rest of the world. Last week, we looked at how Alibaba and Tencent fared in the last quarter; the talk in Silicon Valley and Beijing this week is on Y Combinator’s sudden retreat from China. We will also discuss the enduring food delivery war in the country later.
The storied Silicon Valley accelerator Y Combinator announced the closure of its China unit just a little over a year after it entered the country. In a vague statement posted on its official blog, the organization said the decision came amid a change in leadership. Sam Altman, its former president who hired legendary artificial intelligence scientist Lu Qi to initiate the China operation, recently left his high-profile role to join research outfit OpenAI. With that, YC has since refocused its energy to support “local and international startups from our headquarters in Silicon Valley.”
What was untold is the insurmountable challenge that multinationals face in their attempt to win in a wildly different market. Lu Qi, who wore management hats at Baidu and Microsoft before joining YC, was clearly aware of the obstacles when he said in an interview (in Chinese) in May that “multinational corporations in China have almost been wiped out. They almost never successfully land in China.” The prescription, he believes, is to build a local team that’s given full autonomy to make decisions around products, operations, and the business.
A former executive at an American company’s China branch, who asked to remain anonymous, argued that Lu Qi’s one-man effort can’t be enough to beat the curse of multinationals’ path in China. “All I can say is: Lu has taken a detour. Going independent is the best decision. When it comes to whether Chinese startups are suited for mentorship, or whether incubators bring value to China, these are separate questions.”
What’s curious is that YC China seemed to have been given a meaningful level of freedom before the split. “Thanks to Sam Altman and the U.S. team, who agreed with my view and supported with much preparation, YC China is not only able to enjoy key resources from YC U.S. but can also operate at a completely independent capacity,” Lu said in the May interview.
Moving on, the old YC China team will join Lu Qi to fund new companies under a newly minted program, MiraclePlus, announced YC China via a Wechat post (in Chinese). The initiative has set up its own fund, team, entity and operational team. The deep ties that Lu has fostered with YC will continue to benefit his new portfolio, which will receive “support” from the YC headquarters, though neither party elaborated on what that means.
The food delivery war in China is still dragging on two years after the major consolidation that left the market with two major players. Meituan, the local services company backed by Tencent, has managed to attain an expanding share against Alibaba-owned Ele.me. According to third-party data (in Chinese) provided by Trustdata, Meituan accounted for 65.1% of China’s overall food delivery orders during the second quarter, steadily rising from just under 60% a year ago. Ele.me, on the other hand, has lost nearly 10% of the market, slumping to 27.4% from 36% a year ago.
In terms of monetization, Meituan generated 15.6 billion yuan ($2.2 billion) in revenue from its food delivery segment in the quarter ended September 30. That dwarfs Ele.me, which racked up 6.8 billion yuan ($970 million) during the same period. Both are growing north of 30% year-over-year.
This may not be all that surprising given Alibaba has arguably more imminent battles to fight. The e-commerce leader has been consumed by the rise of Pinduoduo, which has launched an assault on China’s low-tier cities with its ultra-cheap products and social-driven online shopping experience. Meituan, on the other hand, is fixated on beefing up its main turf of on-demand neighborhood services after divesting its costly bike-sharing endeavor.
When both contestants have the capital to burn through — as they have demonstrated through heavily subsidizing customers and restaurants — the race comes down to which has greater control of user traffic. Meituan holds a competitive edge thanks to its merger with Dianping, a leading restaurant review app akin to Yelp, back in 2015. Dianping today operates as a standalone brand but its food app is deeply integrated with Meituan’s delivery services. For example, hundreds of millions of users are able to place Meituan-powered food delivery orders straight from Dianping.
Alibaba and Meituan used to be on more friendly terms just a few years ago. In 2011, the e-commerce giant participated in Meituan’s $50 million Series B financing. Before long, the two clashed over control of the company. Alibaba is known to impose a heavy hand on its portfolio companies by taking up majority stakes and reshuffling the company with new executives. That’s because Alibaba believes that “only when you operate can you generate synergies and really create exponential value,” said vice chairman Joe Tsai in an interview. “Whereas if you just make a financial investment, you’re counting an internal rate of return. You’re not creating real value.”
Ele.me lived through that transformation. As of September, Alibaba has reportedly (in Chinese) completed replacing Ele.me’s management with its pool of appointed personnel. Ele.me’s founder Zhang Xuhao left the company with billions of yuan in cash and joined a venture capital firm (in Chinese).
Meituan’s founder Wang Xing had more unfettered pursuits. In a later financing round, he refused to accept Alibaba’s condition for portfolio companies to eschew Tencent investments, a strategy of the giant to hobble its archrival. That botched the partnership and Alibaba has since been gradually offloading its Meituan shares but still held onto small amounts, according to Wang in 2017, “to create trouble” for Meituan going forward.
Deep learning is all the rage these days in enterprise circles, and it isn’t hard to understand why. Whether it is optimizing ad spend, finding new drugs to cure cancer, or just offering better, more intelligent products to customers, machine learning — and particularly deep learning models — have the potential to massively improve a range of products and applications.
The key word though is ‘potential.’ While we have heard oodles of words sprayed across enterprise conferences the last few years about deep learning, there remain huge roadblocks to making these techniques widely available. Deep learning models are highly networked, with dense graphs of nodes that don’t “fit” well with the traditional ways computers process information. Plus, holding all of the information required for a deep learning model can take petabytes of storage and racks upon racks of processors in order to be usable.
There are lots of approaches underway right now to solve this next-generation compute problem, and Cerebras has to be among the most interesting.
As we talked about in August with the announcement of the company’s “Wafer Scale Engine” — the world’s largest silicon chip according to the company — Cerebras’ theory is that the way forward for deep learning is to essentially just get the entire machine learning model to fit on one massive chip. And so the company aimed to go big — really big.
Today, the company announced the launch of its end-user compute product, the Cerebras CS-1, and also announced its first customer of Argonne National Laboratory.
The CS-1 is a “complete solution” product designed to be added to a data center to handle AI workflows. It includes the Wafer Scale Engine (or WSE, i.e. the actual processing core) plus all the cooling, networking, storage, and other equipment required to operate and integrate the processor into the data center. It’s 26.25 inches tall (15 rack units), and includes 400,000 processing cores, 18 gigabytes of on-chip memory, 9 petabytes per second of on-die memory bandwidth, 12 gigabit ethernet connections to move data in and out of the CS-1 system, and sucks just 20 kilowatts of power.
A cross-section look at the CS-1. Photo via Cerebras
Cerebras claims that the CS-1 delivers the performance of more than 1,000 leading GPUs combined — a claim that TechCrunch hasn’t verified, although we are intently waiting for industry-standard benchmarks in the coming months when testers get their hands on these units.
In addition to the hardware itself, Cerebras also announced the release of a comprehensive software platform that allows developers to use popular ML libraries like TensorFlow and PyTorch to integrate their AI workflows with the CS-1 system.
In designing the system, CEO and co-founder Andrew Feldman said that “We’ve talked to more than 100 customers over the past year and a bit,“ in order to determine the needs for a new AI system and the software layer that should go on top of it. “What we’ve learned over the years is that you want to meet the software community where they are rather than asking them to move to you.”
I asked Feldman why the company was rebuilding so much of the hardware to power their system, rather than using already existing components. “If you were to build a Ferrari engine and put it in a Toyota, you cannot make a race car,” Feldman analogized. “Putting fast chips in Dell or [other] servers does not make fast compute. What it does is it moves the bottleneck.” Feldman explained that the CS-1 was meant to take the underlying WSE chip and give it the infrastructure required to allow it to perform to its full capability.
A diagram of the Cerebras CS-1 cooling system. Photo via Cerebras.
That infrastructure includes a high-performance water cooling system to keep this massive chip and platform operating at the right temperatures. I asked Feldman why Cerebras chose water, given that water cooling has traditionally been complicated in the data center. He said, “We looked at other technologies — freon. We looked at immersive solutions, we looked at phase-change solutions. And what we found was that water is extraordinary at moving heat.”
A side view of the CS-1 with its water and air cooling systems visible. Photo via Cerebras.
Why then make such a massive chip, which as we discussed back in August, has huge engineering requirements to operate compared to smaller chips that have better yield from wafers. Feldman said that “ it massively reduces communication time by using locality.”
In computer science, locality is placing data and compute in the right places within, let’s say a cloud, that minimizes delays and processing friction. By having a chip that can theoretically host an entire ML model on it, there’s no need for data to flow through multiple storage clusters or ethernet cables — everything that the chip needs to work with is available almost immediately.
According to a statement from Cerebras and Argonne National Laboratory, Cerebras is helping to power research in “cancer, traumatic brain injury and many other areas important to society today” at the lab. Feldman said that “It was very satisfying that right away customers were using this for things that are important and not for 17-year-old girls to find each other on Instagram or some shit like that.”
(Of course, one hopes that cancer research pays as well as influencer marketing when it comes to the value of deep learning models).
Cerebras itself has grown rapidly, reaching 181 engineers today according to the company. Feldman says that the company is hands down on customer sales and additional product development.
It has certainly been a busy time for startups in the next-generation artificial intelligence workflow space. Graphcore just announced this weekend that it was being installed in Microsoft’s Azure cloud, while I covered the funding of NUVIA, a startup led by the former lead chip designers from Apple who hope to apply their mobile backgrounds to solve the extreme power requirements these AI chips force on data centers.
Expect ever more announcements and activity in this space as deep learning continues to find new adherents in the enterprise.