Illustration by Ian Hurley
What Are LA’s Hottest Startups of 2022? See Who VCs Picked in dot.LA’s Annual Survey
In Los Angeles—like the startup environment at large—venture funding and valuations skyrocketed in 2021, even as the coronavirus pandemic continued to surge and supply chain issues rattled the economy. The result was a startup ecosystem that continued to build on its momentum, with no shortage of companies raising private capital at billion-dollar-plus unicorn valuations.
In order to gauge the local startup scene and who’s leading the proverbial pack, we asked more than 30 leading L.A.-based investors for their take on the hottest firms in the region. They responded with more than two dozen venture-backed companies; three startups, in particular, rose above the rest as repeat nominees, while we've organized the rest by their amount of capital raised as of January, according to data from PitchBook. (We also asked VCs not to pick any of their own portfolio companies, and vetted the list to ensure they stuck to that rule.)
Without further ado, here are the 26 L.A. startups that VCs have their eyes on in 2022.
1. Whatnot ($225.4 million raised)
Whatnot was the name most often on the minds of L.A. venture investors—understandably, given its prolific fundraising year. Whatnot raised some $220 million across three separate funding rounds in 2021, on the way to a $1.5 billion valuation.
The Marina del Rey-based livestream shopping platform was founded by former GOAT product manager Logan Head and ex-Googler Grant LaFontaine. The startup made its name by providing a live auction platform for buying and selling collectables like rare Pokémon cards, and has since expanded into sports memorabilia, sneakers and apparel.
2. Boulevard ($40.3 million raised)
Boulevard’s backers include Santa Monica-based early-stage VC firm Bonfire Ventures, which focuses on B2B software startups. The Downtown-based company fits nicely within that thesis; Boulevard builds booking and payment software for salons and spas. The firm has worked with prominent brands such as Toni & Guy and HeyDay.
3. GOAT ($492.7 million)
GOAT launched in 2015 as a marketplace to help sneakerheads authenticate used Air Jordans and other collectible shoes. It has since grown at a prolific rate, expanding into apparel and accessories and exceeding $2 billion in merchandise sales in 2020. The startup sealed a $195 million funding round last summer that more than doubled its valuation, to $3.7 billion.
The Best of the Rest
VideoAmp ($578.6 raised)
Nielsen competitor VideoAmp gathers data on who's watching what across streaming services, traditional TV and social apps like YouTube. The company positions itself as an alternative to so-called "legacy" systems like Nielsen, which it says are "fragmented, riddled with complexity and inaccurate." In addition to venture funding, its total funding figure includes more than $165 million in debt financing.
Mythical Games ($269.4 million raised)
Seizing on the NFT craze, Mythical Games is building a platform that powers the growing realm of “play-to-earn games.” Backed by NBA legend Michael Jordan and Andreessen Horowitz, the Sherman Oaks-based startup’s partners include game publishers Abstraction, Creative Mobile and CCG Lab.
FloQast ($202 million raised)
FloQast founder Michael Whitmire says he got a “no” from more than 100 investors in the process of raising a seed round. Today, the accounting software company is considered a unicorn.
Nacelle ($70.8 million raised)
Nacelle produces docuseries, books, comedy albums and podcasts. The media company’s efforts include the Netflix travel series “Down To Earth with Zac Efron.”
Wave ($66 million raised)
A platform for virtual concerts, Wave has hosted performances by artists including Justin Bieber, Tinashe and The Weeknd. The company says it has raised $66 million to date from the likes of Warner Music and Tencent.
Papaya ($65.2 million raised)
Sherman Oaks-based Papaya looks to make it easier to pay “any” bill—from hospital bills to parking tickets—via its mobile app.
LeaseLock ($63.2 million raised)
Based in Marina del Rey, LeaseLock says it’s on a mission to eliminate security deposits for apartment renters.
Emotive ($58.1 million raised)
Emotive sells text message-focused marketing tools to ecommerce firms like underwear brand Parade and men's grooming company Beardbrand.
Dray Alliance ($55 million raised)
Based in Long Beach, Dray says its mission is to “modernize the logistics and trucking industry.” Its partners include Danish shipping company Maersk and toy maker Mattel.
Coco ($43 million raised)
Coco makes small pink robots on wheels (you may have seen them around town) that deliver food via a remote pilot. Its investors include Y Combinator and Silicon Valley Bank.
HiveWatch ($25 million raised)
HiveWatch develops physical security software. Its investors include former Twitter executive Dick Costollo and NBA star Steph Curry’s Penny Jar Capital.
Popshop ($24.5 million raised)
Whatnot competitor Popshop is betting that live-shopping is the future of ecommerce. The West Hollywood-based firm focuses on collectables such as trading cards and anime merchandise.
First Resonance ($19.4 million raised)
Founded by former SpaceX engineer Karan Talati, First Resonance runs a software platform for makers of electric cars and aerospace technology. Its clients include Santa Cruz-based air taxi company Joby Aviation and Alameda-based rocket company Astra.
Open Raven ($19 million raised)
Founded by Crowdstrike and Microsoft alums, Open Raven aims to protect user data. The cybersecurity firm’s investors include Kleiner Perkins and Upfront Ventures.
Fourthwall ($17 million raised)
When an actor faces the camera and speaks directly to the audience, it’s known as “breaking the fourth wall.” Named after the trope, Venice-based Fourthwall offers a website builder that’s designed for content creators.
The Non Fungible Token Company ($15 million raised)
The Non Fungible Token Company creates NFTs for musicians under the name Unblocked. Its investors include Jay Z’s Marcy Venture Partners and Shawn Mendez.
Safe Health Systems ($15 million raised)
Backed by Mayo Clinic Ventures, Safe Health develops telehealth software and offers tools for enterprises to launch their own health care apps.
Intro ($11.6 million raised)
Intro’s app lets you book video calls with experts—from celebrity stylists, to astrologists, to investors.
DASH Systems ($8.5 million raised)
With the tagline “Land the package, not the plane,” DASH Systems is a Hawthorne-based shipping company that builds hardware and software for automated airdrops.
Ettitude ($3.5 million raised)
With a focus on sustainability, Ettitude is a direct-to-consumer brand that sells bedding, bathroom textiles and sleepwear.
Afterparty ($3 million raised)
Along similar lines as Unblocked, Afterparty creates NFTs for artists and content creators such as Clay Perry and Tropix.
Heart to Heart ($0.75 million raised)
Heart to Heart is an audio-focused dating app that “lets you listen to the story behind the pictures in a profile.” Precursor Ventures led the pre-seed funding round.
Frigg (undisclosed)
Frigg makes hair and beauty products that contain cannabinoids such as CBD. The Valley Village-based company raised an undisclosed seed round in August.
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Drew Grant
I had already spent an embarrassing amount of money downloading nearly 1,000 high-definition images of myself generated by AI through an app called Lensa as part of its new “Magical Avatar” feature. There are many reasons to cock an eyebrow at the results, some of which have been covered extensively in the last few days in a mounting moral panic as Lensa has shot itself to the #1 slot in the app store.
The way it works is users upload 10-20 photos of themselves from their camera roll. There are a few suggestions for best results: the pictures should show different angles, different outfits, different expressions. They shouldn’t all be from the same day. (“No photoshoots.”) Only one person in the frame, so the system doesn’t confuse you for someone else.
Lensa runs on Stable Diffusion, a deep-learning mathematical method that can generate images based on text or picture prompts, in this case taking your selfies and ‘smoothing’ them into composites that use elements from every photo. That composite can then be used to make the second generation of images, so you get hundreds of variations with no identical pictures that hit somewhere between the Uncanny Valley and one of those magic mirrors Snow White’s stepmother had. The tech has been around since 2019 and can be found on other AI image generators, of which Dall-E is the most famous example. Using its latent diffusion model and a 400 million image dataset called CLIP, Lensa can spit back 200 photos across 10 different art styles.
Though the tech has been around a few years, the rise in its use over the last several days may have you feeling caught off guard for a singularity that suddenly appears to have been bumped up to sometime before Christmas. ChatGPT made headlines this week for its ability to maybe write your term papers, but that’s the least it can do. It can program code, break down complex concepts and equations to explain to a second grader, generate fake news and prevent its dissemination.
It seems insane that when confronted with the Asminovian reality we’ve been waiting for with either excitement, dread or a mixture of both, the first thing we do is use it for selfies and homework. Yet here I was, filling up almost an entire phone’s worth of pictures of me as fairy princesses, anime characters, metallic cyborgs, Lara Croftian figures, and cosmic goddesses.
And in the span of Friday night to Sunday morning, I watched new sets reveal more and more of me. Suddenly the addition of a nipple went from a Cronenbergian anomaly to the standard, with almost every photo showing me with revealing cleavage or completely topless, even though I’d never submitted a topless photo. This was as true for the male-identified photos as the ones where I listed myself as a woman (Lensa also offers an “other” option, which I haven’t tried.)
Drew Grant
When I changed my selected gender from female to male: boom, suddenly, I got to go to space and look like Elon Musk’s Twitter profile, where he’s sort of dressed like Tony Stark. But no matter which photos I entered or how I self-identified, one thing was becoming more evident as the weekend went on: Lensa imagined me without my clothes on. And it was getting better at it.
Was it disconcerting? A little. The arm-boob fusion was more hilarious than anything else, but as someone with a larger chest, it would be weirder if the AI had missed that detail completely. But some of the images had cropped my head off entirely to focus just on my chest, which…why?
Drew Grant
Drew as a "male" preference
According to AI expert Sabri Sansoy, the problem isn’t with Lensa’s tech but most likely with human fallibility.
“I guarantee you a lot of that stuff is mislabeled,” said Sansoy, a robotics and machine learning consultant based out of Albuquerque, New Mexico. Sansoy has worked in AI since 2015 and claims that human error can lead to some wonky results. “Pretty much 80% of any data science project or AI project is all about labeling the data. When you’re talking in the billions (of photos), people get tired, they get bored, they mislabel things and then the machine doesn’t work correctly.”
Sansoy gave the example of a liquor client who wanted software that could automatically identify their brand in a photo; to train the program to do the task, the consultant had first to hire human production assistants to comb through images of bars and draw boxes around all the bottles of whiskey. But eventually, the mind-numbing work led to mistakes as the assistants got tired or distracted, resulting in the AI learning from bad data and mislabeled images. When the program confuses a cat for a bottle of whiskey, it’s not because it was broken. It’s because someone accidentally circled a cat.
So maybe someone forgot to circle the nudes when programming Stable Diffusion’s neural net used by Lensa. That’s a very generous interpretation that would explain a baseline amount of cleavage shots. But it doesn’t explain what I and many others were witnessing, which was an evolution from cute profile pics to Brassier thumbnails.
When I reached out for comment via email, a Lensa spokesperson responded not by directing us to a PR statement but actually took the time to address each point I’d raised. “It would not be entirely accurate to state that this matter is exclusive to female users,” said the Lensa spokesperson, “or that it is on the rise. Sporadic sexualization is observed across all gender categories, although in different ways. Please see attached examples.” Unfortunately, they were not for external use, but I can tell you they were of shirtless men who all had rippling six packs, hubba hubba.
“The stable Diffusion Model was trained on unfiltered Internet content, so it reflects the biases humans incorporate into the images they produce,” continued the response. Creators acknowledge the possibility of societal biases. So do we.” It reiterated the company was working on updating its NSFW filters.
As for my insight about any gender-specific styles, the spokesperson added: “The end results across all gender categories are generated in line with the same artistic principles. The following styles can be applied to all groups, regardless of their identity: Anime and Stylish.”
I found myself wondering if Lensa was also relying on AI to handle their PR, before surprising myself by not caring all that much. If I couldn’t tell, did it even matter? This is either a testament to how quickly our brains adapt and become numb to even the most incredible of circumstances; or the sorry state of hack-flack relationships, where the gold standard of communication is a streamlined transfer of information without things getting too personal.
As for the case of the strange AI-generated girlfriend? “Occasionally, users may encounter blurry silhouettes of figures in their generated images. These are just distorted versions of themselves that were ‘misread’ by the AI and included in the imagery in an awkward way.”
So: gender is a social construct that exists on the Internet; if you don’t like what you see, you can blame society. It’s Frankenstein’s monster, and we’ve created it after our own image.
Or, as the language processing AI model ChatGPT might put it: “Why do AI-generated images always seem so grotesque and unsettling? It's because we humans are monsters and our data reflects that. It's no wonder the AI produces such ghastly images - it's just a reflection of our own monstrous selves.”
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