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Mirror founder Brynn Putnam on life with Lululemon — and whether or not she sold too soon

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Brynn Putnam has a lot to feel great about. A Harvard grad and former professional ballet dancer who opened the first of what have become three high-intensity fitness studios in New York, she then launched a second business in 2016 when — while pregnant with her son — she was exercising at home and couldn’t find a natural way watch a class on her laptop or phone. Her big idea: to install mirrored screens in users’ homes that are roughly eight square feet and through which they can exercise to all manner of streamed and on-demand exercise classes, paying a monthly subscription of just $39 per month.

If you’ve followed the home fitness craze, you already know these Mirrors quickly took off with celebrities, who gushed about the product on social media. Putnam’s company also attracted roughly $75 million in venture funding across several fast rounds. Indeed, by the end of last year, people had bought  “tens of thousands” of Mirrors, according to Putnam, and she was beginning to envision Mirrors as content portals that might feature fashion, enable doctor’s visits, and bring both kids classes and therapy into users’ homes. As she told The Atlantic back in January, “We view ourselves as the third screen in people’s homes.”

Then, in June, the company revealed it had sold for $500 million in cash — including a $50 million earn-out — to the athleisure company Lululemon. For Putnam, the deal was too compelling, allowing her to secure the future of her company, which continues to run as a subsidiary. Investors might have liked it, too, given that it meant a fast return on their investment, not to mention that Mirror had steep competition, including from Peloton, the biggest giant in the home fitness market.

The deal seems to be clicking right now. Just today, Lululemon announced that it is installing Mirrors in 18 of its now 506 U.S. locations, including in San Francisco, Washington, D.C., and Miami. Lulemon hasn’t started selling products directly through Mirror yet, but “shoppable content” is “certainly on our radar,” says Putnam. Meanwhile, Mirror’s revenues, expected earlier this year to reach $100 million, are now on target to surpass $150 million revenue, she says.

Still, as the pandemic has raged on, it’s easy to wonder what the young company might have grown into given the amount of time that people and their children are spending at home and in front of their screens. Late last week, we put the question to Putnam, who continues to manage a team of 125 people. You can listen to the full conversation here.

TC: People who follow the company know why you started Mirror, but how, exactly, did you start Mirror?

BP: In the case of Mirror, I had this concept for the product, and then really, the first step was buying a Raspberry Pi, a piece of one-way glass, and an Android tablet, and assembling it in my in my kitchen to see if this idea in my mind would be able to work and come to life.

TC: Did you take any coding classes? People might not imagine that a former ballet performer with a chain of fitness studios would put something together like that in her kitchen.

BP: No, I’ve been very fortunate to have a husband who has a bit of a development background. And so he helped me to put the first little bit of code into the Mirror and just really ensure that the concept I had in my mind could be brought to life. And then from there, obviously, over time, we hired a team.

TC: Are they manufactured in the States? In China? How did you start figuring out how to put those pieces together?

BP: I had heard a lot of hardware horror stories about teams working with design agencies to design these beautiful products and who, by the time they actually got to manufacturing, found out that something wasn’t feasible about their design when it came to commercialization or just running out of money in the process. So I actually went backwards. I drew a sketch on a napkin and did a small set of bullets of the things that I thought were really just crucial to make the product a success. And then I went to find factories in China that were familiar with digital signage, working with large pieces of glass, large mirrors, learned about their systems and processes, and then brought it back to the U.S. to a local manufacturer here on the East Coast to refine into a prototype. And then we eventually moved to Mexico when we were ready to scale.

TC: The mirror is about $1,500 dollars. How did you go about winning the trust of consumers that would lead them to make such a sizable investment?

BP: When you’re when you’re building an innovation product, you can’t really compete on specs and features like you do with phones or laptops. So you’re really building building a brand, which means that you’re telling stories. And in our case, we spent a lot of time, from the very early days, really imagining the life of our members and figuring out how to craft that story and tell that story.

And then we were fortunate early on to have members fall in love with the product. And then they started to tell our story for us. So once you have that customer flywheel that starts to kick in, your job becomes much easier.

TC: You had actors, celebrities, designers, and social media influencers talking about their Mirrors. Was it just a matter of sending it off to a few people who started getting online and sharing [their enthusiasm for the product] with their followers? Was it that simple?

BP: We knew that we wanted to make big bets early on to make the Mirror brand seem larger and more established than it was, because it’s a premium product in a new category. And we wanted people to trust in us and the brand. And so we did things like out-of-home advertisements quite early, we moved into television quite early, and we also did some very strategic early celebrity placements. But the way in which the celebrity placements grew and expanded was very much not intended and was just kind of a fascinating early example of the network effects of the product. One celebrity would get it and then another would see it in their home. Or they would see it in their stylist’s home or their agent’s home. And it spread through that community very, very quickly in one of the earliest examples of member love for us.

TC: How did you convince early adopters that your business had staying power, and were investors persuaded as quickly?

BP: Trying to assure customers that they wouldn’t invest in this Mirror, and then the company would go out of business in a few years and they would be they’d be left with a piece of hardware but no access to the content or the community that they’d fallen in love with was very important. It was one of the factors in deciding to partner with Lululemon and have the incredible brand stability and love of such a premium global brand.

In terms of fundraising, I think we were we were really fortunate to have a product that once you saw it, you got it and fell in love with it in a market that was clearly big and growing, with a really good competitive data point in Peloton.

TC: Who started that conversation with Lululemon? Were you talking to Peloton and other potential acquirers?

BP: I’ve been really fortunate to actually work with Lululemon for my entire fitness career. There was a team of Lululemon educators here in New York who were the very first clients of my studio business, and frankly, in many ways were responsible for helping that business to grow and thrive and to give me confidence as a first-time small business owner. Then we reconnected with Lululemon about a year before the acquisition as an investor; they made a small minority investment in the company. And we began to work together on various projects . . .From there, really, the partnership just grew. Mirror was not for sale. We were not looking for an acquirer. But it’s really your responsibility as a founder to always be weighing your vision, your responsibility to your team and your responsibility to your shareholders. And so when the opportunity presented itself — before COVID actually — it felt like really just too good an opportunity to pass up.

TC: But you also you had ambitions of turning this into a much broader content portal where you would maybe have doctor visits and other things, which I would think won’t happen now.

BP: The vision for Mirror very much remains the same and we’re excited to continue to expand the types of content that we offer via the Mirror platform, really with an eye toward any type of immersive experience that makes you a better version of yourself. So I think you will see a broader range of content from us in the coming years.

TC: You’ve mention in the past as a selling point that Mirror is a product that’s used by families. Is there children’s programming or is that coming soon?

BP I think one of the things that surprised us but delighted us about Mirror has been the number of households that have over two members. More than 65% of our households have over two members, which means that you’re often getting younger members of the household involved. I do think that is a function of both the versatility of the platform and the fact that multiple people can participate in more content. At the same time, we’ve actually seen the number of users under 20 grow about 5x during the COVID months as young adults have returned home to be with their their families or teenagers have started doing remote schooling. So we’ve leaned into that with what we call “family fun” content that’s designed to be performed by the whole family together.

TC: Do you see a secondary market for refurbished Mirrors in the future? Will there be a second version, if there isn’t already?

BP: We’ll obviously continue to to refine the hardware over time, but the real focus of the business is through improving the content, community and experience, and so for us — unlike Apple, where the goal is to really release a new model every year and continue to have folks upgrade the hardware — we focus on providing updates via the software and the content, so that we’re continuing to add value onto the baseline experience.

TC: What can people look forward to on this front?

BP: We’re taking a major step toward  building a connected community through our community feature set launching this holiday, including a community feature that enables members to see and communicate with each other and their instructor; face offs that allow members to compete head-to-head against another member of the community and earn points as you hit your target heart rate zones; and friending, so you can find and follow your friends in the Mirror community to share your favorite workouts, join programs together and cheer each other on.

TC: Are you still selling Mirrors to hotels and business outside of Lululemon?

BP: We do have b2b relationships. You can find mirrors in hotels, small gyms, buildings, residences, and then obviously direct-to-consumer through the Mirror website, the Lululemon website, and both of our stores

TC: When you look at Peloton now and how its stock has completely exploded this year,  do you think ever that you should have hung on a little longer? Do you ever think ‘maybe I sold too soon?’

I’ve woken up every day really for my entire career kind of focused on the same mission but trying to solve the problem and achieve my vision and in different ways. Which is: I really believe that confidence in your own skin is the foundation of a good and happy life. And fitness is an incredible tool for building that confidence that carries over into your personal relationships, your work performance, your friendships. And so for me, that’s always really been the North Star, which is, ‘How do we get more Mirrors into more homes and provide more access to to that self confidence?’ So I spend very little time comparing to competitors and much more time focused on our members’ needs and how to meet them.

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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Mike Cagney is testing the boundaries of the banking system for himself — and others

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Founder Mike Cagney is always pushing the envelope, and investors love him for it. Not long sexual harassment allegations prompted him to leave SoFi, the personal finance company that he cofounded in 2011, he raised $50 million for new lending startup called Figure that has since raised at least $225 million from investors and was valued a year ago at $1.2 billion.

Now, Cagney is trying to do something unprecedented with Figure, which says it uses a blockchain to more quickly facilitate home equity, mortgage refinance, and student and personal loan approvals. The company has applied for a national bank charter in the U.S., wherein it would not take FDIC-insured deposits but it could take uninsured deposits of over $250,000 from accredited investors.

Why does it matter? The approach, as American Banker explains it, would bring regulatory benefits. As it reported earlier this week, “Because Figure Bank would not hold insured deposits, it would not be subject to the FDIC’s oversight. Similarly, the absence of insured deposits would prevent oversight by the Fed under the Bank Holding Company Act. That law imposes restrictions on non-banking activities and is widely thought to be a deal-breaker for tech companies where banking would be a sidelight.”

Indeed, if approved, Figure could pave the way for a lot of fintech startups — and other retail companies that want to wheel and deal lucrative financial products without the oversight of the Federal Reserve Board or the FDIC — to nab non-traditional bank charters.

As Michelle Alt, whose year-old financial advisory firm helped Figure with its application, tells AB: “This model, if it’s approved, wouldn’t be for everyone. A lot of would-be banks want to be banks specifically to have more resilient funding sources.” But if it’s successful, she adds, “a lot of people will be interested.”

One can only guess at what the ripple effects would be, though the Bank of Amazon wouldn’t surprise anyone who follows the company.

In the meantime, the strategy would seemingly be a high-stakes, high-reward development for a smaller outfit like Figure, which could operate far more freely than banks traditionally but also without a safety net for itself or its customers. The most glaring danger would be a bank run, wherein those accredited individuals who are today willing to lend money to the platform at high interest rates began demanding their money back at the same time. (It happens.)

Either way, Cagney might find a receptive audience right now with Brian Brooks, a longtime Fannie Mae executive who served as Coinbase’s chief legal officer for two years before jumping this spring to the Office of the Comptroller of the Currency (OCC), an agency that ensures that national banks and federal savings associations operate in a safe and sound manner.

Brooks was made acting head of the agency in May and green-lit one of the first national charters to go to a fintech, Varo Money, this past summer. In late October, the OCC also granted SoFi preliminary, conditional approval over its own application for a national bank charter.

While Brooks isn’t commenting on speculation around Figure’s application, in July, during a Brookings Institution event, he reportedly commented about trade groups’ concerns over his efforts to grant fintechs and payments companies charters, saying: “I think the misunderstanding that some of these trade groups are operating under is that somehow this is going to trigger a lighter-touch charter with fewer obligations, and it’s going to make the playing field un-level . . . I think it’s just the opposite.”

Christopher Cole, executive vice president at the trade group Independent Community Bankers of America, doesn’t seem persuaded. Earlier this week, he expressed concern about Figure’s bank charter application to AB, saying he suspects that Brooks “wants to approve this quickly before he leaves office.”

Brooks’s days are surely numbered. Last month, he was nominated by President Donald to a full five-year term leading the federal bank regulator and is currently awaiting Senate confirmation. The move — designed to slow down the incoming Biden administration — could be undone by President-elect Joe Biden, who can fire the comptroller of the currency at will and appoint an acting replacement to serve until his nominee is confirmed by the Senate.

Still, Cole’s suggestion is that Brooks still has enough time to figure out a path forward for Figure — and if its novel charter application is approved, and it stands up to legal challenges — a lot of other companies, too.

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We read the paper that forced Timnit Gebru out of Google. Here’s what it says

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On the evening of Wednesday, December 2, Timnit Gebru, the co-lead of Google’s ethical AI team, announced via Twitter that the company had forced her out. 

Gebru, a widely respected leader in AI ethics research, is known for coauthoring a groundbreaking paper that showed facial recognition to be less accurate at identifying women and people of color, which means its use can end up discriminating against them. She also cofounded the Black in AI affinity group, and champions diversity in the tech industry. The team she helped build at Google is one of the most diverse in AI, and includes many leading experts in their own right. Peers in the field envied it for producing critical work that often challenged mainstream AI practices.

A series of tweets, leaked emails, and media articles showed that Gebru’s exit was the culmination of a conflict over another paper she co-authored. Jeff Dean, the head of Google AI, told colleagues in an internal email (which he has since put online) that the paper “didn’t meet our bar for publication” and that Gebru had said she would resign unless Google met a number of conditions, which it was unwilling to meet. Gebru tweeted that she had asked to negotiate “a last date” for her employment after she got back from vacation. She was cut off from her corporate email account before her return.

Online, many other leaders in the field of AI ethics are arguing that the company pushed her out because of the inconvenient truths that she was uncovering about a core line of its research—and perhaps its bottom line. More than 1,400 Google staff and 1,900 other supporters have also signed a letter of protest.

Many details of the exact sequence of events that led up to Gebru’s departure are not yet clear; both she and Google have declined to comment beyond their posts on social media. But MIT Technology Review obtained a copy of the research paper from  one of the co-authors, Emily M. Bender, a professor of computational linguistics at the University of Washington. Though Bender asked us not to publish the paper itself because the authors didn’t want such an early draft circulating online, it gives some insight into the questions Gebru and her colleagues were raising about AI that might be causing Google concern.

Titled “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” the paper lays out the risks of large language models—AIs trained on staggering amounts of text data. These have grown increasingly popular—and increasingly large—in the last three years. They are now extraordinarily good, under the right conditions, at producing what looks like convincing, meaningful new text—and sometimes at estimating meaning from language. But, says the introduction to the paper, “we ask whether enough thought has been put into the potential risks associated with developing them and strategies to mitigate these risks.”

The paper

The paper, which builds off the work of other researchers, presents the history of natural-language processing, an overview of four main risks of large language models, and suggestions for further research. Since the conflict with Google seems to be over the risks, we’ve focused on summarizing those here. 

Environmental and financial costs

Training large AI models consumes a lot of computer processing power, and hence a lot of electricity. Gebru and her coauthors refer to a 2019 paper from Emma Strubell and her collaborators on the carbon emissions and financial costs of large language models. It found that their energy consumption and carbon footprint have been exploding since 2017, as models have been fed more and more data.

Strubell’s study found that one language model with a particular type of “neural architecture search” (NAS) method would have produced the equivalent of 626,155 pounds (284 metric tons) of carbon dioxide—about the lifetime output of five average American cars. A version of Google’s language model, BERT, which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate—nearly the same as a roundtrip flight between New York City and San Francisco.

Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources,” they write.

Massive data, inscrutable models

Large language models are also trained on exponentially increasing amounts of text. This means researchers have sought to collect all the data they can from the internet, so there’s a risk that racist, sexist, and otherwise abusive language ends up in the training data.

An AI model taught to view racist language as normal is obviously bad. The researchers, though, point out a couple of more subtle problems. One is that shifts in language play an important role in social change; the MeToo and Black Lives Matter movements, for example, have tried to establish a new anti-sexist and anti-racist vocabulary. An AI model trained on vast swaths of the internet won’t be attuned to the nuances of this vocabulary and won’t produce or interpret language in line with these new cultural norms.

It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities.

Moreover, because the training datasets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,” the researchers conclude. “While documentation allows for potential accountability, […] undocumented training data perpetuates harm without recourse.”

Research opportunity costs

The researchers summarize the third challenge as the risk of “misdirected research effort.” Though most AI researchers acknowledge that large language models don’t actually understand language and are merely excellent at manipulating it, Big Tech can make money from models that manipulate language more accurately, so it keeps investing in them. “This research effort brings with it an opportunity cost,” Gebru and her colleagues write. Not as much effort goes into working on AI models that might achieve understanding, or that achieve good results with smaller, more carefully curated datasets (and thus also use less energy).

Illusions of meaning

The final problem with large language models, the researchers say, is that because they’re so good at mimicking real human language, it’s easy to use them to fool people. There have been a few high-profile cases, such as the college student who churned out AI-generated self-help and productivity advice on a blog, which went viral.

The dangers are obvious: AI models could be used to generate misinformation about an election or the covid-19 pandemic, for instance. They can also go wrong inadvertently when used for machine translation. The researchers bring up an example: In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest.

Why it matters

Gebru and Bender’s paper has six co-authors, four of whom are Google researchers. Bender asked to avoid disclosing their names for fear of repercussions. (Bender, by contrast, is a tenured professor: “I think this is underscoring the value of academic freedom,” she says.)

The paper’s goal, Bender says, was to take stock of the landscape of current research in natural-language processing. “We are working at a scale where the people building the things can’t actually get their arms around the data,” she said. “And because the upsides are so obvious, it’s particularly important to step back and ask ourselves, what are the possible downsides? … How do we get the benefits of this while mitigating the risk?”

In his internal email, Dean, the Google AI head, said one reason the paper “didn’t meet our bar” was that it “ignored too much relevant research.” Specifically, he said it didn’t mention more recent work on how to make large language models more energy-efficient and mitigate problems of bias. 

However, the six collaborators drew on a wide breadth of scholarship. The paper’s citation list, with 128 references, is notably long. “It’s the sort of work that no individual or even pair of authors can pull off,” Bender said. “It really required this collaboration.” 

The version of the paper we saw does also nod to several research efforts on reducing the size and computational costs of large language models, and on measuring the embedded bias of models. It argues, however, that these efforts have not been enough. “I’m very open to seeing what other references we ought to be including,” Bender said.

Nicolas Le Roux, a Google AI researcher in the Montreal office, later noted on Twitter that the reasoning in Dean’s email was unusual. “My submissions were always checked for disclosure of sensitive material, never for the quality of the literature review,” he said.

Dean’s email also says that Gebru and her colleagues gave Google AI only a day for an internal review of the paper before they submitted it to a conference for publication. He wrote that “our aim is to rival peer-reviewed journals in terms of the rigor and thoughtfulness in how we review research before publication.”

Bender noted that even so, the conference would still put the paper through a substantial review process: “Scholarship is always a conversation and always a work in progress,” she said. 

Others, including William Fitzgerald, a former Google PR manager, have further cast doubt on Dean’s claim: 

Google pioneered much of the foundational research that has since led to the recent explosion in large language models. Google AI was the first to invent the Transformer language model in 2017 that serves as the basis for the company’s later model BERT, and OpenAI’s GPT-2 and GPT-3. BERT, as noted above, now also powers Google search, the company’s cash cow.

Bender worries that Google’s actions could create “a chilling effect” on future AI ethics research. Many of the top experts in AI ethics work at large tech companies because that is where the money is. “That has been beneficial in many ways,” she says. “But we end up with an ecosystem that maybe has incentives that are not the very best ones for the progress of science for the world.”

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Daily Crunch: Slack and Salesforce execs explain their big acquisition

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We learn more about Slack’s future, Revolut adds new payment features and DoorDash pushes its IPO range upward. This is your Daily Crunch for December 4, 2020.

The big story: Slack and Salesforce execs explain their big acquisition

After Salesforce announced this week that it’s acquiring Slack for $27.7 billion, Ron Miller spoke to Slack CEO Stewart Butterfield and Salesforce President and COO Bret Taylor to learn more about the deal.

Butterfield claimed that Slack will remain relatively independent within Salesforce, allowing the team to “do more of what we were already doing.” He also insisted that all the talk about competing with Microsoft Teams is “overblown.”

“The challenge for us was the narrative,” Butterfield said. “They’re just good [at] PR or something that I couldn’t figure out.”

Startups, funding and venture capital

Revolut lets businesses accept online payments — With this move, the company is competing directly with Stripe, Adyen, Braintree and Checkout.com.

Health tech venture firm OTV closes new $170M fund and expands into Asia — This year, the firm led rounds in telehealth platforms TytoCare and Lemonaid Health.

Zephr raises $8M to help news publishers grow subscription revenue — The startup’s customers already include publishers like McClatchy, News Corp Australia, Dennis Publishing and PEI Media.

Advice and analysis from Extra Crunch

DoorDash amps its IPO range ahead of blockbuster IPO — The food delivery unicorn now expects to debut at $90 to $95 per share, up from a previous range of $75 to $85.

Enter new markets and embrace a distributed workforce to grow during a pandemic — Is this the right time to expand overseas?

Three ways the pandemic is transforming tech spending — All companies are digital product companies now.

(Extra Crunch is our membership program, which aims to democratize information about startups. You can sign up here.)

Everything else

WH’s AI EO is BS — Devin Coldewey is not impressed by the White House’s new executive order on artificial intelligence.

China’s internet regulator takes aim at forced data collection — China is a step closer to cracking down on unscrupulous data collection by app developers.

Gift Guide: Games on every platform to get you through the long, COVID winter — It’s a great time to be a gamer.

The Daily Crunch is TechCrunch’s roundup of our biggest and most important stories. If you’d like to get this delivered to your inbox every day at around 3pm Pacific, you can subscribe here.

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