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Gillmor Gang: Twitter+

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The best thing about 2020 is we survived it. No need to say what the worst thing is, it’s hands down our collective stupidity in the choices we’ve made. That reality has forced us to refactor what we do moving forward.

If we had correctly understood the massive changes ahead, we would not be wondering when we will return to the old, new or any normal. The normal is what got us here. Unlimited air travel, freedom to do whatever we wanted without regard to the impact it would have on anybody else. Nationalism. What the hell is that all about? Keeping us in, everybody else out.

Take Twitter for one. When it first emerged, it felt like a pipe dream realized. For me, it still feels that way. Good people like it, so do bad people. Bad as in they use the global network to inflict damage on their political enemies. Does that mean the phone is a bad thing, too? Or cars, or popcorn butter? What about dramas? They’re sad, reward winners and losers? Do I wish Hollywood was only allowed to make rom-coms? Well, yes I do.

But only if it doesn’t abridge my rights, my freedom to pursue happiness. So when I see Twitter turn into a cesspool, I look for someone to blame. Let’s start with the bad guys. But what if they have a point about something? Their motives may be suspect, or just plain evil. What am I doing reading them anyway. It’s not like I chose them to follow. Well, apparently I did, by listening to people who retweet what these folks spew.

Retweets are another one of these things I love about Twitter. Let’s say I follow someone whose perspective I admire, and they in turn retweet others who they admire. A social cloud forms with interesting characteristics. Implicitly, the pattern of retweets, @mentions and likes can be plugged into readers or aggregators to reflect trends, emerging news, business analytics, and social dynamics of power, ethics, humor and stature.

So it’s not like a follow of the bad actors, but it is like I follow their relative position in the stream of those I follow. I can and do rationalize this monitoring of other than the chosen social group as a necessary early warning system for trouble ahead. These signals can be used prophylactically to measure how our message is carrying, but a typical impact is to pigeonhole our views as fodder for those who wish us ill.

Net net, this countervailing energy reduces the sense of fun I have with the global network. If I had to choose no Twitter over this problem, I still choose Twitter. In the early days of social media, I had a front row seat in observing how these little signals could have a surprising impact on the concerns of the day, on the projection of ideas around the network to and with others who together built support, and sometimes, business through the collective group mind.

Has this been lost in the partisan nature of our daily political noise? Of course, just try saying anything about anything and watch the nasty trolls rev up their schtick. Not fun. Also not effective, because the pushback creates a new rhythm of Pee Wee Herman yeah-but-what-am-I dynamics. What to do? How about a @botmention that argues with tagged trolls but silently removes the noise from the feeds of those who @like the @bot tag.

Implementing this semi-public stream is already doable inside a private network, with the “cost” of joining the agreement to provide access to an internal view that makes the stream less noisy and more responsive. We’ve been experimenting with just such a private/public backchannel to support production of the Gillmor Gang, but I’m not here to promote that. More usefully, the network functions efficiently in concert with Twitter.

The events of 2020, and the years leading up to the election and pandemic breakout, make clear that the kind of social media spread we have seen has consequences we should have countered but in fact exacerbated. Yet even in the volatile wind down of the election are some signs of a rebound from playing the chaos card. Whatever you think of Twitter’s history of or lack of backbone development, Jack Dorsey’s red line in the sand was a much needed call to arms against Trump’s bullying.

Even if the actual technology was limited in effect, the application of any pushback at all was a signal of what the world might look like if the election went the other way. The first amplification of that subtle shift came from social media’s biggest customer, mainstream media: pointed pushback in White House press conferences, silent movie montages of Republican senators refusing to answer shouted hallway questions, networks cutting away from events when the falsehood level reached fake mass.

Mitch McConnell’s move to tie additional stimulus help to Trump’s attempt to punish Twitter by repealing Section 230 protection proved effective in running out the clock. It also moved the ball from Trump’s control to the hard numbers of January 20. The Georgia runoff on January 5, followed the next day by the attempt to challenge the electoral college Biden win and the storming of the Capitol, changed everything. Twitter became Trump’s last super power. Note: This edition of the Gang was recorded minutes before Twitter permanently suspended the @realDonaldTrump account.

Well, there is Zoom too. Its swappable background feature lets the ex-resident broadcast to the faithful as though nothing has changed. That’s why he came back from vacation early, to pre-pardon his production staff and hire a shadow cabinet. Secretary of Streaming, Chief Acting Legal Officer, Secretary of Horror Stephen Miller, Secretary of Bacteria Giuliani.

Zoom lets you do this behind a subscription paywall, but now Trump+ is competing against Disney+, Netflix, Apple+ and the bundles designed to lock-in the market until the vaccines take root. Or how about an ACA+ bundle that gives you preexisting coverage, the latest iPhone and any three + networks on a rotating basis to encourage competition for stream retention.

from the Gillmor Gang Newsletter

__________________

The Gillmor Gang — Frank Radice, Michael Markman, Keith Teare, Denis Pombriant, Brent Leary and Steve Gillmor. Recorded live Friday, January 8, 2021.

Produced and directed by Tina Chase Gillmor @tinagillmor

@fradice, @mickeleh, @denispombriant, @kteare, @brentleary, @stevegillmor, @gillmorgang

Subscribe to the Gillmor Gang Newsletter and join the backchannel here on Telegram.

The Gillmor Gang on Facebook … and here’s our sister show G3 on Facebook.

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Personal skin problems leads founder to launch skincare startup Nøie, raises $12M Series A

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Inspired by his own problems with skin ailments, tech founder Daniel Jensen decided there had to be a better way. So, using an in-house tech platform, his Copenhagen-based startup Nøie developed its own database of skin profiles, to better care for sensitive skin.

Nøie has now raised $12m in a Series A funding round led by Talis Capital, with participation from Inventure, as well as existing investors including Thomas Ryge Mikkelsen, former CMO of Pandora, and Kristian Schrøder Hart-Hansen, former CEO of LEO Pharma’s Innovation Lab.

Nøie’s customized skincare products target sensitive skin conditions including acne, psoriasis and eczema. Using its own R&D, Nøie says it screens thousands of skincare products on the market, selects what it thinks are the best, and uses an algorithm to assign customers to their ‘skin family’. Customers then get recommendations for customized products to suit their skin.

Skin+Me is probably the best-known perceived competitor, but this is a prescription provider. Noie is non-prescription.

Jensen said: “We firmly believe that the biggest competition is the broader skincare industry and the consumer behavior that comes with it. I truly believe that in 2030 we’ll be surprised that we ever went into a store and picked up a one-size-fits-all product to combat our skincare issues, based on what has the nicest packaging or the best marketing. In a sense, any new company that emerges in this space are peers to us: we’re all working together to intrinsically change how people choose skincare products. We’re all demonstrating to people that they can now receive highly-personalized products based on their own skin’s specific needs.”

Of his own problems to find the right skincare provider, he said: “It’s just extremely difficult to find something that works. When you look at technology, online, and all our apps and everything, we got so smart in so many areas, but not when it comes to consumer skin products. I believe that in five or 10 years down the line, you’ll be laughing that we really used to just go in and pick up products just off the shelf, without knowing what we’re supposed to be using. I think everything we will be using in the bathroom will be customized.”

Beatrice Aliprandi, principal at Talis Capital, said: “For too long have both the dermatology sector and the skincare industry relied on the outdated ‘one-size-fits-all’ approach to addressing chronic skin conditions. By instead taking a data-driven and community feedback approach, Nøie is building the next generation of skincare by providing complete personalization for its customers at a massive scale, pioneering the next revolution in skincare.”

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Comms expert and VC Caryn Marooney will detail how to get attention at TC Early Stage

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We’re thrilled to announce Caryn Marooney is speaking at our upcoming TechCrunch Early Stage virtual event in July. She spoke with us last year and we had to have her back.

Just look at her resume. She was the co-founder and CEO of The Outcast Agency, one of Silicon Valley’s best-regarded public relations firms. She left her company to serve as VP of Global Communication at Facebook, which she did for eight years, overseeing communication for Facebook, Instagram, WhatsApp and Oculus. In 2019 she joined Coatue Management as a general partner, where she went on to invest in Startburst, Supabase, Defined Networks and others.

Needless to say, Marooney is one of the Valley’s experts on getting people’s attention — a skill that’s critical when running a startup, nonprofit or school bake sale.

She said it best last year: “People just fundamentally aren’t walking around caring about this new startup — actually, nobody does.” So how do you get people to care? That’s the trick and why we’re having her back to speak on this evergreen topic.

Watch her presentation from 2020 here. It’s fantastic.

One of the great things about TC Early Stage is that the show is designed around breakout sessions, with each speaker leading a chat around a specific startup core competency (like fundraising, designing a brand, mastering the art of PR and more). Moreover, there is plenty of time for audience Q&A in each session.

Pick up your ticket for the event, which goes down July 8 and 9, right here. And if you do it today, you’ll save a cool $100 off of your registration.


 

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Lightmatter’s photonic AI ambitions light up an $80M B round

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AI is fundamental to many products and services today, but its hunger for data and computing cycles is bottomless. Lightmatter plans to leapfrog Moore’s law with its ultra-fast photonic chips specialized for AI work, and with a new $80M round the company is poised to take its light-powered computing to market.

We first covered Lightmatter in 2018, when the founders were fresh out of MIT and had raised $11M to prove that their idea of photonic computing was as valuable as they claimed. They spent the next three years and change building and refining the tech — and running into all the hurdles that hardware startups and technical founders tend to find.

For a full breakdown of what the company’s tech does, read that feature — the essentials haven’t changed.

In a nutshell, Lightmatter’s chips perform certain complex calculations fundamental to machine learning in a flash — literally. Instead of using charge, logic gates, and transistors to record and manipulate data, the chips use photonic circuits that perform the calculations by manipulating the path of light. It’s been possible for years, but until recently getting it to work at scale, and for a practical, indeed a highly valuable purpose has not.

Prototype to product

It wasn’t entirely clear in 2018 when Lightmatter was getting off the ground whether this tech would be something they could sell to replace more traditional compute clusters like the thousands of custom units companies like Google and Amazon use to train their AIs.

“We knew in principle the tech should be great, but there were a lot of details we needed to figure out,” CEO and co-founder Nick Harris told TechCrunch in an interview. “Lots of hard theoretical computer science and chip design challenges we needed to overcome… and COVID was a beast.”

With suppliers out of commission and many in the industry pausing partnerships, delaying projects, and other things, the pandemic put Lightmatter months behind schedule, but they came out the other side stronger. Harris said that the challenges of building a chip company from the ground up were substantial, if not unexpected.

A rack of Lightmatter servers.

Image Credits: Lightmatter

“In general what we’re doing is pretty crazy,” he admitted. “We’re building computers from nothing. We design the chip, the chip package, the card the chip package sits on, the system the cards go in, and the software that runs on it…. we’ve had to build a company that straddles all this expertise.”

That company has grown from its handful of founders to more than 70 employees in Mountain View and Boston, and the growth will continue as it brings its new product to market.

Where a few years ago Lightmatter’s product was more of a well-informed twinkle in the eye, now it has taken a more solid form in the Envise, which they call a ‘general purpose photonic AI accelerator.” It’s a server unit designed to fit into normal datacenter racks but equipped with multiple photonic computing units, which can perform neural network inference processes at mind-boggling speeds. (It’s limited to certain types of calculations, namely linear algebra for now, and not complex logic, but this type of math happens to be a major component of machine learning processes.)

Harris was reticent to provide exact numbers on performance improvements, but more because those improvements are increasing than that they’re not impressive enough. The website suggests it’s 5x faster than an NVIDIA A100 unit on a large transformer model like BERT, while using about 15 percent of the energy. That makes the platform doubly attractive to deep-pocketed AI giants like Google and Amazon, which constantly require both more computing power and who pay through the nose for the energy required to use it. Either better performance or lower energy cost would be great — both together is irresistible.

It’s Lightmatter’s initial plan to test these units with its most likely customers by the end of 2021, refining it and bringing it up to production levels so it can be sold widely. But Harris emphasized this was essentially the Model T of their new approach.

“If we’re right, we just invented the next transistor,” he said, and for the purposes of large-scale computing, the claim is not without merit. You’re not going to have a miniature photonic computer in your hand any time soon, but in datacenters, where as much as 10 percent of the world’s power is predicted to go by 2030, “they really have unlimited appetite.”

The color of math

A Lightmatter chip with its logo on the side.

Image Credits: Lightmatter

There are two main ways by which Lightmatter plans to improve the capabilities of its photonic computers. The first, and most insane sounding, is processing in different colors.

It’s not so wild when you think about how these computers actually work. Transistors, which have been at the heart of computing for decades, use electricity to perform logic operations, opening and closing gates and so on. At a macro scale you can have different frequencies of electricity that can be manipulated like waveforms, but at this smaller scale it doesn’t work like that. You just have one form of currency, electrons, and gates are either open or closed.

In Lightmatter’s devices, however, light passes through waveguides that perform the calculations as it goes, simplifying (in some ways) and speeding up the process. And light, as we all learned in science class, comes in a variety of wavelengths — all of which can be used independently and simultaneously on the same hardware.

The same optical magic that lets a signal sent from a blue laser be processed at the speed of light works for a red or a green laser with minimal modification. And if the light waves don’t interfere with one another, they can travel through the same optical components at the same time without losing any coherence.

That means that if a Lightmatter chip can do, say, a million calculations a second using a red laser source, adding another color doubles that to two million, adding another makes three — with very little in the way of modification needed. The chief obstacle is getting lasers that are up to the task, Harris said. Being able to take roughly the same hardware and near-instantly double, triple, or 20x the performance makes for a nice roadmap.

It also leads to the second challenge the company is working on clearing away, namely interconnect. Any supercomputer is composed of many small individual computers, thousands and thousands of them, working in perfect synchrony. In order for them to do so, they need to communicate constantly to make sure each core knows what other cores are doing, and otherwise coordinate the immensely complex computing problems supercomputing is designed to take on. (Intel talks about this “concurrency” problem building an exa-scale supercomputer here.)

“One of the things we’ve learned along the way is, how do you get these chips to talk to each other when they get to the point where they’re so fast that they’re just sitting there waiting most of the time?” said Harris. The Lightmatter chips are doing work so quickly that they can’t rely on traditional computing cores to coordinate between them.

A photonic problem, it seems, requires a photonic solution: a wafer-scale interconnect board that uses waveguides instead of fiber optics to transfer data between the different cores. Fiber connections aren’t exactly slow, of course, but they aren’t infinitely fast, and the fibers themselves are actually fairly bulky at the scales chips are designed, limiting the number of channels you can have between cores.

“We built the optics, the waveguides, into the chip itself; we can fit 40 waveguides into the space of a single optical fiber,” said Harris. “That means you have way more lanes operating in parallel — it gets you to absurdly high interconnect speeds.” (Chip and server fiends can find that specs here.)

The optical interconnect board is called Passage, and will be part of a future generation of its Envise products — but as with the color calculation, it’s for a future generation. 5-10x performance at a fraction of the power will have to satisfy their potential customers for the present.

Putting that $80M to work

Those customers, initially the “hyper-scale” data handlers that already own datacenters and supercomputers that they’re maxing out, will be getting the first test chips later this year. That’s where the B round is primarily going, Harris said: “We’re funding our early access program.”

That means both building hardware to ship (very expensive per unit before economies of scale kick in, not to mention the present difficulties with suppliers) and building the go-to-market team. Servicing, support, and the immense amount of software that goes along with something like this — there’s a lot of hiring going on.

The round itself was led by Viking Global Investors, with participation from HP Enterprise, Lockheed Martin, SIP Global Partners, and previous investors GV, Matrix Partners and Spark Capital. It brings their total raised to about $113 million; There was the initial $11M A round, then GV hopping on with a $22M A-1, then this $80M.

Although there are other companies pursuing photonic computing and its potential applications in neural networks especially, Harris didn’t seem to feel that they were nipping at Lightmatter’s heels. Few if any seem close to shipping a product, and at any rate this is a market that is in the middle of its hockey stick moment. He pointed to an OpenAI study indicating that the demand for AI-related computing is increasing far faster than existing technology can provide it, except with ever larger datacenters.

The next decade will bring economic and political pressure to rein in that power consumption, just as we’ve seen with the cryptocurrency world, and Lightmatter is poised and ready to provide an efficient, powerful alternative to the usual GPU-based fare.

As Harris suggested hopefully earlier, what his company has made is potentially transformative in the industry and if so there’s no hurry — if there’s a gold rush, they’ve already staked their claim.

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