If your AI product is going head to head with an incumbent, their distribution advantage will probably kill your startup…unless you fight back with a different game.

It is Summer 2023, and each day brings a new AI product demo that goes completely viral on Twitter/X or TikTok. Countless people are blown away by the product’s magic-like qualities, powered by GPT4, Stable Diffusion, or some other new language, video, or image model. And while it’s an incredibly exciting time to be building or investing in AI, it’s also a fiercely competitive one as well, especially for teams building in the application layer — the part of the technology stack that delivers real world products to end users interacting directly with software.

The competition is being fueled by what’s at stake: participation in a generational platform shift in which the capabilities (and the potential value) of the tools we use are reaching new heights, all because of AI. We haven’t witnessed a shift of this magnitude since the advent of cloud computing, the mobile revolution, or even the internet. In other words: the stakes are high.

But it also has just as much to do with the fact that building and launching new AI products is arguably easier than ever before, democratized by more accessible coding education, powerful IDEs (aka integrated development environments, which help engineers be more efficient when coding, even aiding them via AI through offerings like GitHub’s Copilot), and the AI itself: companies like OpenAI and Stability, to their credit, have made it really easy for startups to make products using their game-changing tech.

AI is a Commodity

The combination of the high stakes, the excitement, and the accessibility of the technology means that there are a lot of new startups out there building AI products. Seemingly every startup today can (and is!) incorporating AI into their products. But it’s not just the startups…bigger, more established players are also incorporating AI, too. And they’re moving really fast. As a result, AI has quickly become so ubiquitous, that it’s fair to say that it has become commoditized.

Historically, when new technologies have become commoditized, they have gone from being early differentiators for new entrants to becoming table stakes, and a requirement for most products and services to remain competitive. Take mobile apps as one example; shortly after the App Store launched, a handful of exciting and super innovative companies took the plunge and launched apps quickly and well before others. Some of these teams were rewarded handsomely; Instagram, WhatsApp, Uber, and others became big winners of the race to innovate on mobile before others did.

But as smartphone application development became cheaper, and the distribution for mobile became more ubiquitous (in the form of smartphone adoption), it was no longer a differentiator; it was a commodity. And not just for startups, but for incumbents, too.

Speed Matters, But Distribution Matters Most

Like startups, incumbents also want to win platform shifts, but in the early days, the advantage sits squarely on the side of the startups. Startups can see opportunities and act on them swiftly, like in the case of the examples of above. They get products out into the world fast, blitzscale, and win markets.

But as time goes on, the incumbents mobilize. While they may not ship as quickly as the startups, they can move with heft and might, deploying dozens, hundreds, or even thousands of engineers towards a common goal, often on a collision course with an entire category of startups. When this happens, incumbents hold a very valuable advantage, one that’s arguably much more valuable than the size of their teams or the quantum of their investments. That advantage is distribution.

While startups search to find an audience for their new products, incumbents have already found one. While startups iteratively progress to earn the right to spend precious capital on marketing, incumbents already have entire marketing departments. And while startups fight and claw to unearth new hidden promotional channels, incumbents distribute within products already adopted by millions.

All of this is to say that if a new startup is building an AI product that is likely to be offered by an incumbent, then the startup will inevitably face a very steep, uphill battle. After all, the incumbent can offer and aggressively market the same commoditized AI technology to an existing user base of highly qualified customers.

Just take a look at what Adobe is doing with Firefly, as one example. Earlier this year, a handful of super innovative startups were dazzling the world using open source libraries like Stable Diffusion to offer mind-blowing, AI-powered image generation tools. But in recent months, Adobe has moved decisively to offer similar capabilities directly inside of Adobe Photoshop, a product with vast distribution power and an ability to meet millions of creatives where they’re already doing work. And as I learned in a recent conversation with Adobe’s Chief Strategy Officer, Scott Belsky, the company has no plans to take their foot off the gas anytime soon.

How to Play a Different Game

Does this mean all hope is lost for startups? Of course not; after all, this same dynamic has played out repeatedly throughout history, and countless legendary startups have emerged, succeeded, and gone on to become generational companies. So then, what can AI-focused startups do? How can they gain an advantage for AI products in the application layer that are inevitably destined to go head to head with incumbents? Below are 3 examples of strategic tactics startups can take to fend off bigger companies’ home field advantage.

1. Unique Formats

Few tactics are as potent a weapon against incumbents as gaining adoption of a new, proprietary format. As I wrote about in The Standards Innovation Paradox, when new formats succeed, they provide startups with a huge competitive advantage. If a new team’s product outputs a unique, proprietary format that reaches scale, the startup becomes much more defensible than a product that operates with a standardized format (such as a standard image or video file). This is because the cost to others of adopting the new format for an existing product often requires reworking infrastructure, user experience, or even an entire business model.

Snapchat

There’s perhaps no better example than Snapchat’s introduction of their signature “snap” format to illustrate the point. Prior to Snapchat, the most common form of sharing on platforms like Instagram and Facebook was through basic image files. These products (and many others) were perfectly designed to support the static, non-dynamic nature of a standard photo format. But Snapchat’s signature “snap” format offered a new way to share moments (in the form of photos or short videos) which would then disappear after a specific amount of time, rather than live on in perpetuity on recipients’ devices. This unique format offered a fun and more spontaneous way of sharing that went far beyond a static photo that lived on forever. Users could create and share moments throughout their days without worrying about the permanence associated with legacy image formats. This made users feel less pressure to share more than on other platforms, which drove Snapchat’s engagement to the moon.

Snap then doubled down by introducing another unique format: stories. Launched in October of 2013, stories offered a new way to share multiple snaps together as a creative narrative, encouraging users to create even more content.

Eventually, after seeing the success of Snapchat’s unique formats, incumbents like Instagram and Facebook eventually raced to introduce their own versions of snaps and stories, but the challenger platform had already gained a substantial first-mover advantage, which helped propel it to the scale of a massive, publicly traded company worth tens of billions of dollars.

The strategy worked: while Snapchat didn’t invent the concept of image or video sharing, the proprietary formats they brought to the world with the snap and stories formats revolutionized social media and made it hard for others to follow without massive investment. By focusing on light-weight, ephemeral, narrative storytelling, Snapchat found a new way to engage users, demonstrating how unique formats can challenge even the biggest players in the market.

AI-First Formats

Now, AI is making it possible to introduce brand new formats that didn’t previously exist. Take generative video avatars, as one example. Previously, to make compelling sales or training videos, users had to capture raw video footage of human beings speaking. As a result, video editing products have adopted a standard mode of editing and production, entrenched in decades of common user experiences. This standard UX is built to support the specific workflows of capturing video, importing it to a timeline, and enabling users to edit these videos. But now, through AI-first products like Synthesia, Veed, Tavus, and HeyGen, videos of people speaking can be generated dynamically. This not only eliminates the need to capture raw video footage from cameras (saving users time and effort), but it means that the entire user experience associated with editing these videos can be reimagined from the ground up to support the new capability more efficiently. In some ways, this new approach invalidates the legacy user experiences of classic video editors, and forces incumbents to deeply rethink their products and businesses to support the emerging avatar use case.

2. Value Destruction

When a new product’s business model threatens to create value destruction for an incumbent, it becomes much more formidable. An example could be as simple as undercutting an incumbent’s prices or offering a service that the incumbent can’t replicate without severely wounding their existing business. And while this is a tried and true strategy for new entrants, it requires a delicate balance. Destroying others’ value can’t be the only tactic pursued; instead, startups must also create value for the customer in ways the incumbent cannot. Otherwise, the incumbent will also lower prices and leverage their massive scale to simply offer a better product.

Robinhood

One recent example of the business model destruction strategy is Robinhood, the company that offers commission-free trades of stocks, ETFs, and cryptocurrencies. Robinhood’s free trading, paired with a highly accessible, easy to use app (adding differentiated value beyond just undercutting prices), made it a no brainer for new traders to adopt. This was a major blow to incumbent brokerage firms, which typically charged fees for every single trade and catered to a more sophisticated type of trader. To compete, these traditional brokerages had to slash their fees, which represented a meaningful chunk of their revenues, effectively causing value destruction to their existing businesses. Many of the products in this market have since followed Robinhood’s lead, but the damage is already done; Robinhood is a publicly traded company worth more than $10B as of this writing.

Airbnb

And Airbnb created a platform that made it easy for people to rent out their homes or spare bedrooms to travelers, thus competing directly with hotels. This model was a game-changer in the hospitality industry and was a classic case of value destruction. Traditional hotels, bound by fixed costs and regulatory norms, found it difficult to compete without making substantial changes to their operational model. Value destruction…check.

AI-Inflicted Damage

AI makes it easier for certain types of startups to inflict swift and aggressive damage to incumbent business models. Take legal services, as one example. While there have been recent headlines around how AI-powered legal services have stumbled in the actual courts, it’s clear the technology has potential to be very disruptive for the category (or least leveraged as an efficiency driver). Think about it: Huge law firms charge exorbitant fees to fund (and profit from) the sheer human-power of their legal partners and associates. The skills of these highly educated lawyers is worth a lot, thus creating a large market for the best law firms in the world. But like highly skilled lawyers, new large language models have also proven to be effective at reading, analyzing, and even writing vast amounts of text. Startups like EvenUp are leveraging AI for specific legal services for a fraction of what they would typically cost. And law firms can’t simply turn around and use AI instead of people; this would make it impossible for them to justify the high costs of their lawyers, thus severely disrupting their business model. It’s a classic case of value destruction, all powered by AI.

3. Hidden Data Moats

In the world of AI, the concept of a “data moat” refers to the advantage a company gains through its access to (and usage of) high-quality, differentiated data. In essence, the more unique data an AI product can learn from, the more effective it becomes. For AI startups, developing a data moat involves amassing unique, valuable data that isn’t easily accessible to others, and using it to train their own AI models. And once a startup has built a substantial data moat, it becomes extremely difficult for other companies (including incumbents) to catch up, unless they too can access the same volume or quality of unique data to train their AI systems. But once the moat forms and strengthens, it can be really hard for others to catch up.

So how can a startup that’s starting from zero build a data moat? There are a few potential paths, such as collecting data through their own unique services or doing partnerships with other companies that have unique datasets. Just keep in mind that incumbents can also do these things, so startups have to find a way to access a hidden moat not easily accessible by others.

Palantir

There are a few classic, recent examples of data moats. Palantir, as one example, built an initial moat through its work with the United States government. The company’s early product was built for the intelligence community and focused on assisting in work on counterterrorism. This involved processing vast amounts of data from hard to reach sources, such as reports from agents in the field, intercepted transmissions, and private bank transactions. But over time, as the company expanded its customer base beyond the government to include financial institutions, healthcare providers, and other industries, Palantir continued to strengthen its moat by integrating and analyzing their diverse, massive datasets. By amassing such a vast and unique dataset, Palantir created a competitive moat that has made it hard for other companies to compete to this day.

TikTok

A well known, more recent example of a data moat is the TikTok algorithm. TikTok has amassed a treasure trove of highly entertaining, short form video content. But more than that, they’ve found a way to tune the TikTok user experience such that everyone using it is matched with highly personalized and relevant content each and every time they use the product, all through a unique data moat. The result is a platform that’s so effective, it has forced all of its competitors to change the way they distribute content, which I wrote about in The End of Social Media. So how do they do it? TikTok dissects users’ behavior upon each and every viewing of a video, including tracking their duration of consumption and analyzing interactions with the user interface. It’s even rumored that they monitor facial expressions of users as they watch videos through smartphones’ front-facing cameras. The result is a nearly impenetrable data moat that both gets stronger — while also improving the product experience — every time someone uses the product.

AI-Propelled Moats

Perhaps it’s obvious, but data moats are by definition, baked into AI products. Providers of large language models, such as OpenAI, leverage their own massive data moats to ensure their models are the best. However, there are ways for startups to build their own moats by offering AI in new and unique experiences, thus in turn generating a new data moat. For example, recent chatbot platforms like Circle Labs, Character.ai, and Replika are using AI as the foundation of their experiences; specifically, users of these products chat directly with characters that are powered by AI. As conversations with these AI-characters go on, the data powering the characters becomes better, thus making the conversations higher quality, driving even more conversation. It’s a classic flywheel of improving engagement while strengthening a data moat, all propelled by AI.

The Twist: AI startups are just startups

Startups which want to fend off looming incumbents’ distribution advantages must attempt to make their businesses as defensible as possible. This requires being nimble, tactical, and leveraging as many strategic advantages as possible. And while unique formats, value destruction, and data moats can all help, there are many other tactics to be pursued, as well.

But here’s the thing about the above three tactics: they are not at all unique to AI startups. In fact, if you went back and re-read this entire essay but skipped all of the AI-specific sections and examples, the tactics would still hold true for all startups.

What does this all mean? It means that if you’re building an AI product — despite now being able to leverage an awesome, transformative technology — it’s really no different than building a non-AI product. AI can help your startup do magical things it simply couldn’t do previously, and the ways in which this is motivating teams to offer truly novel experiences is inspiring; however, in this context, AI is similar to the other tools your products can leverage, like cloud computing, mobile app development, live audio/video streaming, GPS, web3 . . . the list goes on and on.

At the end of the day, finding success for your product is not about leveraging the latest and greatest technology just because you can; it’s about building and shipping products that solve real problems for real people, and scaling those really f*cking fast once they find product market fit. After all, that’s what the incumbents did when they were startups, too. If your team can do that, you’ll walk away from the application layer battlefield victorious.

Thanks for reading! If you enjoyed this, I hope you’ll consider sharing it with a friend.

Want to share your AI-first product with me? Shoot me an email.

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Why Lightspeed is leading a $4.25M Seed in Granola

Today, Lightspeed is announcing that we’re leading a $4.25M seed investment in Granola, a new, London-based company with a bold vision to build the context-aware workspace of the future. Founded by Chris Pedregal and Sam Stephenson, Granola aims to change the way we all work through tools that deeply understand us, anticipate our actions, and augment our abilities. Though Granola is just getting started, we believe new breakthroughs in large language models, combined with Chris and Sam’s compelling vision for the future, and their best-in-class consumer product experience sets the company up well to dramatically improve how we all work.

However, our conviction in the Granola team began long before they had even conceived of the idea for the product. Around a decade ago, I met Chris in NYC as he was building his previous startup, Socratic, and I was building my company, Anchor. We bonded over our passion for powerful, yet easy to use consumer products and hyper rapid iteration that enabled us both to hack our way to product market fit. Even then, Chris had a strong vision for how technology could be leveraged to help make us smarter. In the case of Socratic, it was through a mobile app which helped students with their homework by using computer vision and AI. Way before LLMs were the coolest thing in tech, Chris and his team were able to deliver an AI-first consumer product that provided real value to tens of millions of students, and was ultimately acquired by Google.

In the years that followed, Chris would occasionally share with me his ideas for new consumer products. Most recently, he shared that he was building a new AI-powered product with another passionate and skilled product builder, Sam Stephenson. Upon learning more about their idea and getting to know Sam, we realized that the two were a perfect combination. Sam complemented Chris’ strong product vision and consumer go-to market ingenuity with the design skills to make complex experiences not only accessible, but beautiful. Plus, Sam had a compelling vision for how “tools for thinking” could be made both more powerful and easier for consumer audiences to grasp, with the help of AI. When they showed us what they were building, it became clear that the combination of these two founders working on this product at this exact moment in time had the potential to result in a generational company.

So what exactly are Chris and Sam building with Granola? While we don’t want to spoil their surprise, we can share that they are crafting a game-changing product that infuses the magic of modern code editors (IDEs) into everyday tasks like taking meeting notes, composing documents, scheduling meetings, and replying to emails. Chris and Sam believe that by tapping into the power of large language models (LLMs), Granola can revolutionize knowledge work, making it as seamless and enjoyable as coding with a modern IDE and GitHub CoPilot. They believe days of using “dumb text editors” for knowledge work will soon be a distant memory, replaced by smart tools that effortlessly grasp your context, predict your actions, and amplify your skills. If early iterations of the product are any indication, we very much share their belief.

We’re also excited that Chris and Sam are building Granola IRL in their home city of London. At Lightspeed, we believe London is quickly emerging as one of the world’s central hubs for AI-first product builders. We’re excited to see how Granola adds to this growing community as they assemble a world class team at their new office. The team is currently hiring a founding engineer to play a fundamental role in shaping the company and the product moving forward.

We’re on the cusp of a new era where intelligent tools empower us to unleash our full potential and focus on what truly matters. We believe Chris and Sam’s product will play a big part in this future, and we can’t wait to see its transformative impact in the years ahead. By bridging the gap between modern code editors and everyday tasks, Granola is poised to redefine the way we work, communicate, and collaborate.

-By Michael Mignano, Julie Kainz, Faraz Fatemi, and Paul Murphy

The Future is Context-Aware was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today, Lightspeed is announcing a new meetup dedicated to bringing together everyone building the future of AI products in New York City

Right now, every startup founder, engineer, designer, and product manager is thinking about how they can leverage AI to take their product to new heights.

And while there are plenty of places online for people to gather, learn, debate and share new ideas, we believe there’s a need for an offline place — somewhere IRL — where AI builders can gather to bond as a local community, learn from leading experts, and showcase the products they are building. So today Lightspeed is launching Generative NYC.

During the mobile computing revolution, NYC became a thriving tech ecosystem. Not only did it become home to some of the most exciting companies of the era — such as Foursquare, Behance, and Tumblr — it also spawned a new — now classic — type of tech meetup.

Events like the the NY Tech Meetup, Product Secrets, or Betaworks’ Thursday night demos became staples within the local community, giving builders a place to trade ideas with their peers and learn from people making their favorite products.

Demos of unpolished prototypes, on-the-spot hiring, and requests for beta testers were all hallmarks of these events (as was pizza and beer).

These meetups played a crucial role in fostering the growth and development of the NYC community during a time of unprecedented change within tech. And now, because of AI, tech in NYC is once again experiencing a surge of excitement, momentum, and change, similar to the one felt more than a decade ago. Which means it’s once again time to bring this electric surge of talent and excitement into a room, together.

Generative NYC is modeled after these classic NYC meetups. The event will be held monthly, and will be open to anyone building products (e.g. engineers, designers, PMs, and founders) that leverage AI. At each event, a featured speaker from the local AI community will share learnings from their work to help benefit the community, followed by a handful of demos from local teams. And just like the meetups of old, Generative NYC will also feature plenty of time for plain old networking over food and drink so builders can bond, brainstorm, and find ways to collaborate.

The very first Generative NYC meetup will take place on May 16th at Lightspeed NYC near Union Square at 6pm ET. Our first featured speaker will be Ziad Sultan, VP of Personalization (AI/ML) at Spotify and an AI pioneer. Ziad and his team are directly responsible for all-things AI for the world’s leading audio platform, such as the features that enable Spotify to perfectly program playlists and music discovery for each individual user, as well as the new AI DJ, which recently launched to much excitement. If you’d like to attend, head over to our event page here to sign up. Also, if you’re part of a local team that’s interested in demoing your product, please fill out this form.

As we embrace a new era of AI-driven innovation, Generative NYC aims to be a center of gravity for the local AI community. Through informative talks from local experts, product demos, and networking, we aspire to create a thriving environment where builders of AI products can collaborate and inspire one another.

Whether you’re building a product that’s already found product market fit or hacking away on your first prototype, please join us at the inaugural Generative NYC meetup on May 16th. Let’s build the future of AI together, right here in NYC.

Introducing Generative NYC was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why Lightspeed is leading a $43M Series B in Tome

Today, Lightspeed is announcing that we’re leading a $43M Series B in Tome, the AI-powered storytelling format and the fastest-growing productivity company to reach 1M users. It’s been thrilling to watch just how fast Tome has ascended and become beloved by so many people, so quickly. But why is this happening? The answer is surprisingly simple, and follows a common pattern among software, technology, and creativity. Note: you can also view the below essay as a tome.

The democratization of creativity

As I wrote in my recent essay, The Creativity Supply Chain, “the history of computing has proven time and time again that all forms of creativity eventually become democratized by technology.” As humans, we seek products that enable us to reduce the friction between idea and execution, helping us become more productive, creative, and expressive. While this trend certainly did not begin as a result of computers and high speed bandwidth, it has greatly accelerated in recent decades.

In some cases, the democratization of creativity happens purely through a better, faster, cheaper, or more powerful creative tool. For example: early Internet standouts like Wordpress and Blogger enabled millions of people to publish free form writing with ease and break out of the traditional publishing process. The iPhone camera made it possible for anyone to take and share high quality images quickly and easily, no longer requiring them to carry clunky cameras that had no Internet connectivity. The company I co-founded with Nir Zicherman, Anchor, enabled millions to easily publish podcasts directly from their smartphones, whereas legacy workflows required creators to be tethered to desktop computers, bulky microphones, and challenging editing software. All of these examples led to titanic breakthroughs in creativity and generated billions of dollars in aggregate economic value.

However, in many of the most successful cases of the democratization of creativity, the phenomenon happens through a combination of tools that are easier to use which also invent a brand new format.

This new format both solves an existing, ubiquitous pain point while establishing something more innovative, more beloved by people, and much more valuable, all at once. As mentioned in the examples above, Wordpress and Blogger made it easier to publish traditional longform writing. However, it was Twitter and Facebook which created more accessible short-form writing formats that generated tremendous value for both creators and the platforms which created them. And while a number of tools made video creation more accessible, TikTok’s highly creative and entertaining format revolutionized the way we all share and consume it.

In each of these cases, creators were able to more easily express their creativity through reduced friction and vast distribution, while the platforms (Twitter, Facebook, TikTok) captured tremendous economic value in parallel through unique, ownable formats.

Through this lens, it’s easy to see that when products reduce friction between idea and execution while also establishing innovative new formats, generational companies are built.

Inertia kills innovation

For the past 25 years or so, knowledge workers have been sharing information, strategies, and stories in the workplace through presentations, often referred to as slide decks (or just “decks”). The format has become ubiquitous; in any given month, hundreds of millions of people create tens of millions of presentations on a daily basis. Given the scale of adoption, in a sense, the presentation format has become standardized.

While this has no doubt accelerated the rise of the presentations, it’s also led to an innovation stalemate. As I wrote in the Standards Innovation Paradox, when standards-based products reach a critical mass of adoption, “the pace of innovation ultimately flatlines due to market inertia and consensus.”

A perfect example: to create these presentations today, creators use tools that are outdated and hard to maneuver, spending countless hours tinkering with the sizing of tables or in many cases paying thousands of dollars to consultants simply to make their presentations look marginally nicer. Meanwhile, once they finalize their presentation, the output inevitably disappoints both creator and consumer. It is fixed to a rigid, 16x9 grid that’s nearly impossible to view on a smartphone or tablet. Its content is flat and static as a result of the limited web technology that existed when the format was created. It gets passed around through email as a large file that often must be downloaded to experience the full presentation, given the lack of cloud infrastructure that existed when the format was created. The benefit is that everyone knows what a presentation is. The downside is that presentations never get any better; there’s a ceiling to how compelling they can be. The format is a relic of an ancient era of the internet and it only still exists for one simple reason: inertia.

And this is where Tome comes in.

Empowering anyone to tell a compelling story

Tome’s mission is to help anyone tell a compelling story. As a company, they believe that if the gaps between idea and presentation are removed, society will be more likely to act on its best ideas. To that end, the Tome team has spent the past 2 years building a new type of presentation product through a combination of 1) beautiful, modern creation tools that are both powerful and easy-to-use for anyone and 2) a new, flexible, responsive, and cloud-native presentation format that was built for the web of today, not the static file-driven world of the 1990s.

Tools

On the creative tools side, Tome just works. Creators can quickly and easily drag in an array of useful, beautiful, and modern tools. Text looks great instantly; no need to spend time endlessly searching for the perfect font or adjusting letter spacing or shadows. Tables are simple, useful, and powerful.

Modern products like Figma, Airtable, Framer, Miro, and Looker integrate seamlessly to enable creators to share designs and connect external sources of data without needing to be a spreadsheets wizard. And content from videos, images, websites, and even tweets display perfectly from the moment a creator places them on the canvas; no need to tediously recreate oft-used design elements. Tome’s tools make it so that anyone (not only professionals and/or management consultants) can tell a compelling story (a “tome”) in minutes (not hours, days, or thousands of dollars spent on professional deck design).

Format

And Tome’s format makes it just as compelling to consume as it is to create. It all starts with the fact that tomes are cloud-native, which means all data is presented live — not frozen in time — and presentations no longer need to be sent as static, bloated .ppt or .pdf files to be downloaded on local devices.

Instead, tomes can be instantly consumed on any device — desktop, table, or smartphone — something classic presentations still can’t do well to this very day. If you’ve ever tried viewing a 30 page deck five minutes before an important meeting, you’ve no doubt experienced first hand just how frustrating this process can be.

Tome’s responsive grid system (which is only now possible through modern web technologies that didn’t exist at the dawn of legacy presentation software) ensures the experience of consuming a Tome is just as native to smartphones as is viewing a TikTok video.

One more thing: AI

This combination of a powerful, yet easy-to-use creation tool and an innovative, new presentation format had already put Tome on a path to disrupting the massive, workplace storytelling market. But then, the Tome team took things a big step further: they turbo charged creation through AI and took the cost of storytelling to zero.

Now, the limitations of Tome are only bound by a creator’s imagination. Tome’s prompt bar enables users to simply ask Tome to create whatever they want — literally — and the product just magically delivers. It can do this based on an existing long-form doc, a simple outline, or even a single sentence. But it does a lot more than that. It also auto-creates images and visuals that match written content, ensuring output is aesthetically beautiful and thematically appropriate. It suggests re-wording copy and re-titling pages.

And soon, Tome will proactively offer high quality suggestions about any and all content, automatically insert citations, and magically add content that exists in other connected data sources, such as your team’s KPI progress (from your metrics dashboard) or information about teammates (from your website’s About page or HR platform).

Remember the first time you went from doing freehand math to using a calculator? It feels like that level of magic.

Through these AI superpowers, Tome’s powerful tools, and an innovative new format, anyone can tell a great story. You don’t need to be a native speaker to find the right words. You don’t need design chops to find the perfect image for your point. And you don’t need to be a slide wizard to craft a compelling narrative flow. You just need Tome.

That’s why creators of all types — including designers, product managers, founders, students, marketers, engineers, and a lot more — are already sharing their stories with Tome, making it the fastest-ever productivity company to reach 1M users. And Tome’s format is making it compelling for people to make more than just presentations; Tome is also being used to create design portfolios, lesson plans, microsites, moodboards, children’s stories, travel itineraries, and a lot more.

Team Tome

While the strategy, product, and future potential alone made an investment in Tome obvious for us at Lightspeed, the strength of the team building this exact product made it a no brainer. Tome’s founders Keith Peiris and Henri Liriani — alongside the exceptional group of highly creative, mission driven teammates they’ve assembled — are the perfect people to be building this company. Keith and Henri’s careers are rooted in building creative tools for hundreds of millions of people through their collective work at Instagram and Messenger. They’ve already mastered the art and science of packaging up world-changing technology and delivering it in a form that enables anyone — regardless of natural talent, access to technology, or economic status — to be creative. You couldn’t write a better resume for a team building this product.

Plus, they’ve been supported by incredible investors and builders along the way, such as Reid Hoffman (partner at Greylock and founder of LinkedIn) and Dan Rose (partner at Coatue and one of Facebook’s and Amazon’s earliest leaders). We at Lightspeed are excited to join such an amazing team, and to share what I’ve learned building creative tools and media platforms for hundreds of millions of people during my time at Anchor, Spotify, Aviary, and Adobe.

The future is now

AI is clearly turning the internet — and possibly the world as we know it — upside down. Over the coming years, AI will enable everyone to instantly eliminate the gap that exists between their ideas and the manifestations of their creativity. It will fundamentally change how we all live, work, communicate, and express ourselves.

But to build a generational company leveraging AI will require more than simply bolting a large language model onto an existing product. It will take a strategy like Tome’s — a highly innovative approach to creativity alongside a unique innovative format, backed by the perfect team building in the perfect space at the perfect time. That will change the world. We can’t wait to see what that world looks like.

Want to learn more about Tome? Shoot me an email or reach out to me on Twitter or LinkedIn.

Storytelling at the Cost of Zero was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

But that doesn’t mean you’ve found product market fit.

I’m about 4 months into my job as a partner at Lightspeed after 15 years building products (including Anchor, which I co-founded and then sold to Spotify) and angel investing in 50+ startups. But while I’m still new to the job, I’ve already noticed a number of interesting trends throughout the hundreds of startup pitches I’ve been lucky enough to participate in, including early stage teams’ approach to growth. Like in most other areas of the economy, the recent downturn has affected startups’ spending habits, and as a result, teams are finding new and creative ways to grow their products.

However, one tactic that seems consistent across virtually every growth strategy I’ve come across is startups’ heavy reliance on TikTok as a (or the) key driver of user acquisition. But just because a startup is forgoing paid user acquisition in lieu of TikTok, it doesn’t mean that startup has found product-market fit.

TikTok is the 🐐

It’s no surprise why startups are leveraging the superpower that is TikTok. After all, it is insanely effective at driving highly targeted distribution of content. So much so, that even the world’s biggest social networks are abandoning social graph distribution in favor of TikTok-inspired algorithmic distribution (aka “recommendation media”, which I’ve written about extensively here). So it’s no wonder that companies of all shapes and sizes — including startups — would rely on TikTok’s engine to find customers for their products. TikTok can find an audience for pretty much anything. So if you make enough videos with the right mix of content and metadata, you have a pretty good chance at getting hundreds, thousands, or even millions of impressions on your marketing message. Given all of the above, TikTok may be the greatest startup growth engine of all time.

Product Market Fit (PMF) and ways to grow

If you’re building an early stage startup, you’ve no doubt heard the term “product market fit” (or PMF). While there are many definitions for this term, I appreciate Paul Graham’s simple explanation that “Product-Market fit is when you build something that people want.” When your product finds PMF, it often starts growing “organically” because the people who want what you’ve built start organically telling other people about it. This word of mouth dynamic can be really effective at helping a product grow, especially if the product itself helps accelerate it through built-in sharing, multiplayer product dynamics, and other product-led growth tactics. I would personally consider any type of growth that’s gained as a result of finding PMF to be organic.

However, finding PMF is really hard, and oftentimes, startups want to grow before they’ve actually found PMF. This can be for any number of reasons. For example: a team wants to have a critical mass of users to experiment with (to find PMF), they want to kickstart more organic growth flywheels, or they may want their product to appear to be growing faster than it actually is. I would classify this as inorganic growth. Historically speaking, the primary way to drive inorganic growth has been by paying for it through paid user acquisition, often via search ads on Google or the App Store), paid social media marketing (on Facebook or Instagram), PR, or influencer marketing (paying influencers directly to talk about your product), just to name a few. The main thing that all of these tactics have in common is that they cost a lot of money if they are to be effective. In fact, they’re often so expensive that they can dramatically reduce a startup’s runway.

There’s never been anything like TikTok

Before TikTok, it wasn’t really possible for a startup to say that they had organic growth (or more accurately that they weren’t paying for growth) if they didn’t actually have PMF. This is because there simply weren’t any truly scaled channels (such as the ones mentioned above) to find growth without paying for it.

But then TikTok came along and changed the startup growth game. Through TikTok’s perfect recommendation media engine and their massive / crazy-engaged user base, startups can now actually find truly scaled growth for their product without actually spending money on paid media, PR, influencer marketing. The result? Being able to declare that a startup has found the ever elusive, unpaid “organic growth” that is most typically associated with PMF.

Now, don’t get me wrong: this is great, and I seriously wish we had access to such a ridiculously effective tool when we were building Anchor. It would have saved us a lot of time spent manually curating Twitter Moments. Leveraging TikTok to find users means many of today’s startups can forgo spending precious capital normally spent on paid user acquisition, thus extending their runway.

But this is not organic growth. Just because a startup isn’t spending money on paid user acquisition doesn’t mean that startup has found product-market fit.

All of the paid user acquisition channels noted above are never referred to as “organic”. This is because the distribution of that startup’s marketing message through paid user acquisition has nothing to do with whether or not people actually adopt a product and choose to share it organically. By that same standard, growth on TikTok which is the result of a highly optimized content strategy that leverages the best recommendation engine to ever exist should probably also not be considered organic, even if it results in a flood of new users. TikTok may be really great at finding users to deliver targeted content, but that doesn’t mean those users will actually get value out of the product itself such that they’d share it on their own organically. Adoption is not the same thing as engagement, or more importantly, retention.

Platform giveth, Platform taketh

Bottom line: being able to leverage TikTok — the GOAT — to drive growth for your product instead of paid user acquisition is not indicative of a strong business or product market fit. And perhaps most importantly, this magical distribution channel can vanish at any time. After all, if the platform giveth, then the platform can just as easily taketh away (just ask Kylie Jenner, who famously appealed to Instagram’s Adam Mosseri after announcing the company’s shift away from friend graphs and towards recommendation media, thus devaluing her 360M+ followers).

In the meantime, it’s important for both startups and investors alike to recognize this unique tactic and moment in time and set TikTok-led growth aside for what it is: a beautiful gift from the platform gods… but definitely not product-market fit. Instead, my recommendation for early stage startups would be to tap the TikTok superpower while the gettin’ is good, but above all else, iterate relentlessly on product until you’ve found PMF. That’s the real superpower.

I write lots of other essays on startups, product, strategy, media, and VC on my Substack. You can also find me on Twitter or LinkedIn.

For Startup Growth, TikTok is the GOAT 🐐 was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI is letting us live out alternative realities on the digital platforms where we already spend our time

Much has been said of, written about, and invested in the “Metaverse.” For the uninitiated, the Metaverse is a conceptual version of the future in which we’re all interacting — for work, play, and everything in between — in an alternate version of reality that’s powered by VR, AR, BCI (brain computer interfaces) or a combination of all of the above. This promised future requires step changes to the behavior of how we all interact with both hardware and software on the internet. As a result, many billions of dollars have been invested in this future by countless companies, none more than Meta (fka Facebook), which went as far as rebranding themselves to signal their dedication to the cause.

But what if the internet, mixed with recent breakthroughs in artificial intelligence, is already enabling us to live in an alternate version of reality? Up until this point, we’ve been living our lives online in digital replicas of the real world, all with varying degrees of fidelity. Some are low fidelity, and mostly made up of text, photos, and asynchronous videos (e.g. social media platforms, email, messaging, etc), while others are much higher fidelity and more immersive, enabling us to get lost in experiences that more deeply connect with our senses (e.g. gaming, today’s VR/AR applications, etc). However, what all of these experiences have lacked thus far is a layer of high quality intelligence that’s been good enough to convince us that the people and experiences we’re interacting with on the other side of the screen are actually real.

All of this is now changing.

AI is making it possible for us to live out alternative versions of reality on the platforms where we’re already spending our time; not on futuristic devices and products that may not arrive for another decade. This is Fictional Reality, and it’s where much of our online behavior is headed.

But to understand what fictional reality is, we must first define reality itself.

What is reality?

Merriam-Webster’s dictionary defines reality as the quality or state of being real. I’d like to take that definition a step further and qualify it. I believe that reality is the overlap of three core elements of our lives:

• Our real identity
• Our real relationships
• Our real, collective experiences

We were all born with given names and appearances. Over time, we cultivate a cluster of personal and professional relationships. We collect unique experiences as we go about our lives, all of which shape who we are as people. For most of us, the vast majority of this reality has taken place offline, in the real world (aka IRL).

However, increasingly, over time, each of us are spending more and more of our realities online, in the digital world. This has meant mapping the three elements of reality — our identities, relationships, and experiences — to the digital world. For example:

• Most of us use our real names on the various digital platforms we use on a regular basis. To represent ourselves visually, we upload real photos of our actual, IRL appearances as our avatars.
• Our friends, acquaintances, and contacts littered across our social networks, email accounts, and address books often start by reflecting our IRL contacts.
• The experiences we partake in online, such as socializing, professional work, education, and gaming, most often happen among groups of people who are being represented by their real identities. For example, I post to Twitter, Slack with my colleagues, learn new skills, and play games with my friends all under the guise of my real, IRL identity.

But when our real identities, relationships, and experiences start becoming fictionalized online, reality becomes fictional reality.

Enter Fictional Reality

Fictional Reality is the concept of people living, playing, or working online through fictionalized versions of their identity, relationships, and experiences. While this is not a brand new behavior, fictional reality is becoming more positive, commonplace, and accepted.

This new wave of fictional reality is different from internet anonymity, or even pseudo-anonymity. People have been pursuing versions of their lives with varying degrees of anonymity since the beginning of the internet. Oftentimes (though not always), the intent behind online anonymity has been for negative, cynical, or nefarious reasons, such as to hide the true intent or impression of one’s actions. “Finstagram” accounts, pseudonymous accounts on Twitter, and burner Reddit profiles are just a few of the many ways people have chosen to conceal their true identities and intentions on the internet.

But fictional reality is something much different; with fictional reality, people are crafting new versions of themselves online for positive reasons, such as to express creativity, gain confidence, or to share a part of their identity or interests that they aren’t able to showcase to the offline world. This may be because the fictionalized version of themselves isn’t socially accepted IRL, or it’s simply not technically possible. Fictional reality can even enable people to seek companionship not found offline or to express an opinion shunned by one’s IRL community.

And just like how reality is the overlap between identity, relationships, and experiences, the same is true in fictional reality.

Your fictional identity

Fictional reality starts with identity. Increasingly, more and more people are representing themselves through fictionalized names, backstories, and even avatars.

To some extent, this behavior goes back as far as AIM screen names; but look no further than Discord to witness the many new ways people are fictionalizing their identity through creative names/handles and backstories, signaling to the world that they are something other than their IRL counterparts. And this behavior is now extending to other platforms through visual identities and avatars, too.

As of this writing, it seems like half of the people I follow on Twitter have an avatar showing a graphical, highly exaggerated representation of themselves. Most of these are powered by an app called Lensa, which leverages generative AI, powered by Stable Diffusion (a text-to-image model created by Stability.ai, a Lightspeed portfolio company), to create these avatars based on 10–20 selfies you upload to the app. The app is immensely popular; it’s gone from relative obscurity to being the #1 overall app in the US App Store in a matter of days.

The explosion of popularity of Lensa — and therefore mainstream interest in artificial intelligence — is by itself interesting; what may be far more interesting is the sheer number of people who are excited to visually represent themselves as someone they are not IRL.

Not only are few challenging the usage of these fictional avatars, but instead, most are embracing it, rushing to the App Store to acquire their own. It seems like only a few short years ago, many of us were careful to not overly “Facetune” our selfies to reveal that they had been manipulated to conceal undesirable aspects of our true appearance. Yet all of a sudden, the opposite seems to be true: millions are excited to showcase the most exaggerated versions of ourselves online.

We may look back on this moment as the turning point in which many people on the internet became comfortable portraying themselves as fictionalized characters.

Your fictional relationships

But fictional reality is about more than just your identity; it’s also about the people — real or not — you choose to interact with.

If you imagine a world in which more and more of us are assuming fictional identities, then it’s only natural to assume that those we are interacting with online will be doing the same. Our friend graphs and inboxes, once dominated by real names and photos, may slowly shift to include more and more fictional characters. And in many cases, human beings won’t be controlling the identities; it’ll be artificial intelligence.

Look no further than ChatGPT, a conversational language model recently released by OpenAI that’s taken the internet by storm over the past week. If ChatGPT screenshots littering Twitter timelines and news articles are any indication, humanity has invested countless hours speaking to artificial intelligence — as if it were a human being — over the past week. In fact, on December 5th, only days after it had been launched, Sam Altman (OpenAI’s CEO) noted that 1 million people had already interacted with ChatGPT.

But why limit our communication with artificial intelligence to a single text box? That’s where Circle Labs comes in.

Circle Labs (disclosure: a Lightspeed portfolio company) is proof that people are becoming increasingly willing (and excited!) to talk to fictional friends where they normally talk to real friends. Through the company’s creator platform — which enables anyone to generate their own AI-powered friends (aka shapes) and then deploy them to platforms like Discord and Twitter — people have exchanged millions of messages with Circle Lab’s artificial intelligence.

And the ways people are interacting with these friends are fascinating; many are now deploying and including shapes as part of their IRL friend groups in their favorite online communities. For example: whereas it may have previously been five human friends chatting all day on Discord, now it’s those same five friends plus a few shapes to add a new dimension of companionship, creativity, entertainment, and conversation.

Your fictional experiences

If fictional reality does end up looking like the version of Metaverse that’s been so heavily portrayed in demo videos, essays, news articles, and Meta (the company) presentations, it’ll be through experiences. Virtual events, concerts, online meetings, and social gatherings will serve as the experience layer that brings us together and shapes our fictional realities. The closer the experiences get to our senses (such as through VR headsets), the more lifelike these fictional realities will become. But the most obvious application of fictional experiences is already one of the most popular forms of media today: gaming.

So many of us already spend countless hours daily as fictionalized characters in games like Fortnite or on platforms like Roblox. This is not a new phenomenon. More than a decade ago, games like World of Warcraft (and EverQuest before that) served as havens for millions to escape their realities and get intentionally lost among clans of new online friends. They’d go on quests, overcome group challenges, and level up their characters in beautiful, vast, and fictional worlds.

But lower fidelity — and lower friction — ways of sharing experiences among fictional identities are emerging. Mascot, a new social platform, enables anyone to create collaborative stories and fanfiction for their characters. Once users create and fully customize their characters (across a variety of attributes, including gender, pronouns, likes, dislikes, special powers, and backstories), they’re matched with other human-powered (yet fictional) characters with which they can create new stories to share with the world.

Many of the above games have their roots in paper-and-die games like Dungeons and Dragons and immersive fantasy novels that have been written for centuries. Imagining yourself navigating an alternate universe as an alternate version of yourself is clearly a fundamental impulse of human creativity. But now, AI is making it possible to go beyond imagining.

This is just the beginning. As more of us take on fictional identities and build networks of fictional relationships, more and more experiences will be purpose-built for this new version of reality.

What’s next?

Given the speed at which artificial intelligence is reshaping how we all interact online, it’s impossible to know what comes next. What’s clear, however, is that we need not wait for better, cheaper, faster — or more comfortable! — hardware (read: headsets) to give us permission to live in fictional realities. The digital platforms on which we already spend most of our time, money, and attention — plus AI — are already doing that for us.

Today’s fictional realities are well on their way to enabling many of us to live out more creative, expressive, and for some — human — versions of our lives. And for some, fiction may end up being far more interesting than reality. After all, if you spend more time on the internet than you do IRL, who’s to say which version of your identity is real and which is fictionalized?

What do you think? Are fictional realities already here? Or are we experiencing a temporary window of escapism along our path to a true, hardware-enabled Metaverse? I’d love to hear your feedback on Twitter or Linkedin.

Thanks to Paul Smalera, Meredith Kendall Maines, Moritz Baier-Lentz, Alex Taussig, and Faraz Fatemi for feedback and editing. Special thanks to Anushk Mittal, Noorie Dhingra, Artificial Intelligence, and tweets by Nikita Bier and Suhail Doshi for extra inspiration.

Fictional Reality: Why the Real Metaverse Doesn’t Require a Headset was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

What Elon Musk might do next after last week’s messy subscription rollout, and why the reports of Twitter’s death are greatly exaggerated

If you’ve spent any time on Twitter lately, you may have the impression that the platform is experiencing a rapid demise. It feels like there have been a near infinite number of tweets, blog posts, and op-eds in major publications over the past week declaring the end of the social media platform. Many are criticizing Elon Musk’s rollout of a new version of Twitter Blue, a subscription offering that gives users the ability to pay $8 per month to be verified, which Musk claims will have the second-order effect of eliminating spam and bots. I won’t re-cap the rollout here, but you can find many other blogs and Twitter threads on the topic with a quick search.

While it seems that the internet has unanimously declared Elon’s Twitter Blue strategy (and rollout) as failed, I have a slightly more nuanced take. In fact, it feels like I may be one of the few people on the planet who think that it was (and still may be) a very good strategy, albeit with very poor — yet recoverable — execution thus far. But how can it be recovered? Before I get into that, some quick additional context.

The New Twitter Blue

Musk has stated that the strategic purposes of the new Twitter Blue are to drive meaningful non-ads based revenue for the business and also eliminate spam and bots. The thinking for the latter is that by getting users to pay for a subscription service with a credit card, they can be considered verified as humans, which should earn them both a visual status confirming them as such, plus increased distribution for their tweets.

Verification tactic aside, I personally think widespread, actual verification of humans would be powerful for Twitter, and likely go very far at eliminating bots and spam and even a lot of abuse. And despite what’s transpired over the past week, I believe that this elimination of abuse via human verification was (and may still be) Musk’s plan. Especially if actual verification (or what I will call “human verification”) is also linked to distribution — a powerful incentive — as Musk said it will be.

However, in my opinion, the problem with the rollout thus far is that he used an existing feature (the previous verification system, which I will refer to as “blue check verification”) that people have come to equate with status. And therefore, users are not equating the new feature with human verification, even though that is what Musk has signaled he wants them to equate it with. So, regardless of the intent of the system, there has instead been confusion. Additionally, the rollout doesn’t enable non-paying users to be verified, which risks stifling free speech for users who can’t afford to pay.

What Musk’s plan might be moving forward

As crazy as this may sound to some, all of this seems fixable. It will be a messy cleanup, but it’s completely possible. There have been countless examples over the past few decades of messy rollouts that went on to be corrected and ultimately highly successful.

Below I’ve shared what I believe would have potentially been a more effective rollout, and perhaps it can still be implemented moving forward.

Step 1: Create a new Human Verification badge

First, I’d come up with a new type of human verification badge. I think it’s important to not conflate the existing status-oriented blue check with a new human verification badge. As users have made clear over the past week, there is simply too much association between the current blue check verification and status. Plus, the exact meaning of previous blue checks is still unclear: in fact, many users do equate it with human verification. As we all saw shortly after the Twitter Blue rollout, many trolls were able to easily spoof a critical mass of users with fake accounts pretending to be George W. Bush, OJ Simpson, among others. There is simply too much baggage associated with the blue check. Thus, a new badge is needed to wipe the slate clean and indicate whether or not a user has been verified as a human in this new version of Twitter Blue.

Step 2: Implement an actual human verification system

Next, I’d come up with a way of actually verifying that a user is the human they claim to be beyond simply linking human verification to a credit card payment. In Musk’s defense, I do believe credit card payment is a decent verification system, and I also probably would have attempted this as a first pass, as well. Credit cards aren’t super easy to acquire unless someone is an actual human, and in theory, users would likely fear having their credit card banned from the platform if they violate the terms of service, such as by impersonating George W. Bush. Yet, here we are, and there was indeed a George W. Bush impersonator, along with many others who don’t seem to mind losing the ability to pay for Twitter Blue with one of their credit cards.

So instead, Twitter needs something that can more accurately verify that someone is who they claim to be. For example, when I was “blue check” verified on Twitter a lifetime ago (for reasons still unbeknownst to me!), Twitter asked me to send in a scan of my photo ID and answer a few questions. While it certainly wasn’t a perfect system, it definitely went a long way in showing Twitter that I was who I said I was. I believe Twitter should expand this human verification functionality to the newer Twitter Blue service to establish more trust (and friction) into the system. This may certainly be hard to scale, but a number of emerging companies and technologies have emerged over the past few years that perform this task at scale. And I believe much of this process can now be powered by machine learning and AI, as many consumer applications have incorporated identity verification over the past few years. In fact, implementing this process may even make Twitter Blue seem even more valuable, and thus convince more people to pay for it.

Step 3: Allow users to pay to be human verified

As Musk is proposing to do with Twitter Blue, Twitter should let users pay (something like $8 per month) to be human verified. This would allow any user to pay to immediately access the human verification system referenced above in Step 2. Once verified, the account would be visually associated with the badge referenced in Step 1 and the user would have access to increased distribution that isn’t offered to non-human verified users. So this both proves that a user is real and gives them the added incentives of super charged tweets and engagement.

This should, in theory, go a long way to fighting spam, bots, and impersonators. If a user violates Twitter’s terms of service, the company would know exactly who the offender is and can punish accordingly.

However, there’s still an issue: what about people who can’t afford Twitter Blue? That’s where Step 4 comes in.

Step 4: Allow users to earn human verification

In addition to pay to be human verified, allow people to earn to be human verified. Personally, the main issue I’ve had with this new Twitter Blue plan all along is that someone could only be human verified if they paid for Twitter Blue. This seems contrary to the notion of Twitter being the global town square and giving “power to the people”. To build a platform for democratization of speech, you can’t have a platform that only enables people who pay money to get access to distribution.

“Earn human verification” would allow users who don’t want to pay for Twitter Blue the ability to be human verified if they can prove that they’re a “good actor” and positive contributor to the Twitter community over an extended period of time. Once proven, the user can access the same human verification system that Twitter Blue users pay for. This “good actor” status could be proven through a number of different signals, such as the quality of a user’s follower graph, the sentiment of their tweets, the duration of time on the platform, etc.

In fact, if a goal of Twitter’s is to drive revenue to the business through this new verification system, they could even instrument “earn to be human verified” in such a way that is ROI positive to the business. For example, perhaps the “good actor” status is only afforded to users who provide meaningful LTV (lifetime value) to Twitter through their engagement and retention on the platform. This would make it such that even if these earned users aren’t paying with dollars, they’re paying through the value they provide to the platform. A good inspiration for this idea might be Reddit Gold, which became both a VIP system and a rewards system that users can share amongst each other.

Step 5: Get rid of blue check legacy system altogether

Many have commented on how the recent Twitter Blue rollout ruined the value of the current blue checks by destroying the value of status on the platform. I understand this take. However, I believe there are plenty of other ways to earn status on Twitter, such as via growing a user’s follower count or landing viral tweets. However, if we want to move towards human-verified Twitter for the sake of free speech and zero-bot Twitter, why risk people conflating the old verification system with the new one? I say kill blue checks altogether to drive ultimate clarity around this new system.

So what went wrong?

I have no idea why the rollout was so messy because I have no inside knowledge of the situation; I’m only going off of what I’ve observed from afar, just like everyone else. And I’m definitely making a lot of assumptions in this essay, as it’s always easy to play Monday Morning Quarterback, especially in the world of flawed product rollouts. However, having spent enough time building and launching my own products, my sense is that a lot of what I’ve written above was (and perhaps still is!) largely Musk’s plan, but in the spirit of getting it done and having a big impact quickly, the team cut a lot of corners. The result ended up confusing and frustrating a lot of people instead.

That doesn’t mean Twitter is dead. Far from it! As Musk himself has stated, Twitter usage is at an all-time high. And data from Apptopia suggests that this is true:

The most engaged users of Twitter — or any product for that matter — will always be the most vocal about the product’s problems. But that doesn’t mean what they are declaring is reality or what they’re asking for is right for the business. When beloved products undergo massive change, the cool thing to do is almost always to dump on said change. But the reality is often much more nuanced. We often don’t understand the implications of major changes until long after they are made. And Musk is no stranger to these types of changes, especially through this experience building Tesla and SpaceX. Regardless of what happens next, I think it’s far too soon to dance on Twitter’s grave and declare the end of the platform. And if Elon Musk has proven anything to us over the past few decades he’s been in business, it’s that he usually finds a way to win.

What do you think? What changes would you make to Twitter at this point to recover from the recent Blue rollout? I’d love to hear you feedback on Twitter or LinkedIn.

Why Lightspeed is leading an investment in Circle Labs, a platform that enables anyone to become a creator of unique, virtual characters powered by AI

By Michael Mignano & Faraz Fatemi

A new AI revolution is upon us, and it is no longer a secret. In a matter of only a few months, recent LLM (large language model) breakthroughs have propelled AI from a niche trend to the dominating force behind much of the tech startup landscape.

However, unlike other recent hype trends of the past few years, the value of AI has quickly become clear, showing us all how photos, writing, video, and other forms of creative media will vastly evolve in the coming years. Generative AI has the potential to transform content creation and consumption across industries including social media, gaming, immersive reality, entertainment, commerce, among many other consumer categories.

However, while much of the world’s attention has been focused on AI’s impact on creative media in recent months, one of the earliest use cases of AI stands to also greatly benefit from recent tech advancements: AI-powered bots, or non-playable characters (NPCs).

Investing in Circle Labs

Today, Lightspeed is announcing that we’re leading a $4.2M seed round in Circle Labs, a platform that enables anyone to become a creator of unique characters powered by AI.

We couldn’t be more excited to back Circle’s incredible team, led by founders Anushk Mittal and Noorie Dhingra, alongside an amazing group of forward-thinking supporting investors like Haystack, Scott Belsky, and AI Grant Fund.

With this investment, Lightspeed is doubling down on our belief in generative AI, alongside our recent investments in Stability AI and others soon to be announced. While today is a continuation of our multi-decade strategy of investing in companies at the forefront of Artificial Intelligence, such as Snorkel AI, Moveworks, Fiddler Labs, Glean, ThoughtSpot, AppZen, and Yellow AI, we believe companies like Circle Labs represent a new frontier for how AI will shape consumer applications of the future.

The evolution of NPCs

NPCs were one of the earliest consumer-facing use cases of AI on the internet. Nearly a decade ago, they began showing up as virtual agents with greater frequency in customer support chats, interactive characters in video games, and even as virtual friends. However, these early-AI NPCs were often primitive. They rarely understood the intent behind our communications. They often blurted out incoherent responses to our prompts. They were mere assistants, and their value most often paled in comparison to the proposition they offered.

But there’s a big difference between the NPCs of old and the ones gaining traction today: the technology is now insanely good and makes conversations high-quality, human-like, and fun. Today’s NPCs are knowledgeable. They’re comedic. They have their own unique personalities. Since the LLMs powering them are based on a snapshot of the total internet, they know real things and have the ability to teach us. They even make us feel like we’re not alone. When you interact with the NPCs of today, one thing becomes clear: it’s not too dissimilar from interacting with a human.

But where will we actually talk to NPCs? Very few consumers spend time in developer tools or consoles, testing out the latest version of GPT·3. And no one wants to talk to customer support chatbots all day.

The answer seems inevitable: we’ll talk to NPCs where we already talk to humans online today. Every single day, each of us are communicating in greater amounts and with greater frequency than ever before on social media, messaging tools, or community platforms like Discord, Twitch, Twitter, WhatsApp, and Instagram.

Meet the main characters of Circle Labs

Anushk and Noorie, the founders of Circle Labs, have been pondering the above questions, and potential solutions, for some time now. Through a series of iterations, vetted hypotheses, rapidly shipped product updates, and an emerging social culture around Discord communities, they’ve built a new type of creator platform that lets anyone generate their own AI-powered NPCs (or as they call them, Shapes).

Once created, Shapes can be deployed across every social/communication platform (Discord, Twitter, Instagram, etc.) with persistent, cross-platform memory. In other words, just as you make new human friends and hang out with them across different social platforms, you will do the same with Shapes.

Shapes can be anything. They can be built with a base-level of personality, context, and intelligence that grows and persists as the Shape has more interactions. Today, creators using Circle Labs’ tools are most often making AI friends that anyone in Discord communities can interact with. However, over time, it’s easy to see how Shapes can fill a vast array of use cases.

A Shape can be a rendition of your favorite anime or video game character. It can be a virtual twin for your favorite YouTuber. It can be an AI representation of your favorite brand’s mascot, tweeting on behalf of that brand. And it can even be an AI version of you, ready to jump in on your own conversations when you’re not available. It’s not hard to imagine a world 10 years from now in which most of the world’s AI-enabled NPC interactions will be powered by Circle Labs.

The promise of Circle Labs

The Circle Labs team has been vetting the above product in closed beta for the past few months, with a critical mass of creators requesting access to the creator tool. What’s more, community members have already interacted with Shapes enough to trigger millions of messages over the short, several month duration that the product has been in beta. We believe this head start in an emergent market will further enrich the conversation datasets which power Circle’s NPCs, and serve as a competitive moat over time.

Given the speed at which AI is improving and the volume of collective communication which is now taking place online, it seems inevitable that AI-powered NPCs will be persistent across all of our communication channels in only a few years’ time.

And by designing a creator-first approach that enables anyone on the planet to build, deploy, and scale Shapes with ease, it’s clear to us that Circle Labs will serve as the foundation of many of the world’s digital conversations — and even friendships — in a not-so distant future.

Are you interested in helping shape the future of AI and communication? If so, good news: Circle Labs is hiring. Reach out to founders@circlelabs.xyz to inquire about engineering, product, and marketing roles.

Circle Labs: The Shape of AI-powered Friends to Come was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

Creativity has become the focal point of our modern Internet economy. What was once just a means of artistic expression now powers much of how we all interact, work, and play online.

People are inherently creative. As far back as the history books go, humans have always expressed themselves creatively, whether for communication, storytelling, or even art. As the tools that we’ve used to express ourselves have evolved and multiplied, so too have the manifestations of our creativity.

Today, not only are the tools easy to use, they’re also increasingly powerful. Plus, we now have immediate access to a limitless ocean of others’ creative work through our powerful smartphones and a world washed in high speed internet. This combination of ease-of-use and instant, ubiquitous access to content has unlocked the inner creativity in all of us, unleashing a massive, humanity-sized addressable market of opportunity.

Creativity ≠ The Creator Economy

But wait, isn’t the Creator Economy dying? According to The Information, as of October 2022, venture funding for Creator Economy startups had fallen for the previous five quarters. The category, recently immortalized (and even more recently betrayed) by venture capitalists, startup founders, and tech journalists, was the promise that creators on platforms like YouTube, Instagram, and TikTok could now start making a living from their content, en masse. The Creator Economy promised to deliver not only tools to help creators make money, but ultimately, the potential for true economic independence.

Pandemic about to take your job away? No problem; the Creator Economy is here to save you from unemployment and fund your daily Instagram habit. Right?

Not so much.

The Creator Economy is a sliver of a much bigger opportunity

As it turns out, the constrained opportunity of the Creator Economy hasn’t lived up to its sky-high expectations. As many creators quickly learned, finding 1,000 true fans is something very few people are actually able to do. Most creators can’t break through platforms’ algorithms. Most creators are not marketing experts. Most creators are not making content that is relevant to more than a handful of people. And no, I’m sorry to say, but most creators aren’t going to benefit from their content being “ownable” on the blockchain. This is not a criticism of most creators; it’s a simple acknowledgement that the market for creators’ work follows the same power law dynamic that most other markets face.

All of this is completely OK; there is nothing at all wrong with creating for the sake of creativity.

In fact, there’s a strong case to be made that the aggregate value of all creator content in the 99% (referenced in the “everyone else” category in the image above) is actually equal to or greater than the value in the head (the “top 10,000 creators” referenced). The 99%, however, is much harder to build for, aggregate, and monetize, which is likely why it was so overlooked in the context of The Creator Economy.

And for the few creators who can find 1,000 fans, they can no longer reliably count on their economic support in a market downturn. After all, if you lose your job and your paycheck, which expense will you choose to eliminate first: groceries, or your monthly donation to your favorite YouTuber?

In hindsight, the reasons for the failed promise seem somewhat obvious: the Creator Economy was never really meant to be a solution for 99% of the hundreds of millions (or quite possibly billions) of creators that exist today; instead, it was designed to operationalize the financial infrastructure for the top 1% — the creators who have already broken through and found demand for their work. The Creator Economy was never about democratization; it was about elitism.

The Creativity Supply Chain

But creativity is universal. And the 99% matter. There’s arguably nothing more democratic than everyone on the planet being able to create and share their work with the world.

What if the promise of the “Creator Economy” failed because we collectively (myself included) defined the opportunity for creativity as far too small? What if we were thinking about one small piece of the market for creativity? Surely there is much more to creativity than the tools that enable the top 1% of creators to make money. What if the opportunity for creativity is much bigger than what we’ve all been calling the Creator Economy? I believe it is. I call this opportunity, The Creativity Supply Chain.

You can think of it like this:

1. Supply: The people that create for all purposes in today’s modern economy, and the creative media assets which they then distribute to their audiences.
2. Incentives: Market mechanisms that financially and socially incentivize the creation, distribution, and consumption of creativity.
3. Demand: The people that consume creativity and the channels by which creative media is distributed and consumed on the internet.
4. Superpowers: Products, technologies, and services which democratize all forms of creative media, and give us unparalleled, immediate, and ubiquitous access to creativity. Today’s cutting edge technology both democratizes and turbo charges all of the above components, making them more accessible, efficient, and powerful. And the rate at which creative superpowers are improving is accelerating every day.

The Creativity Supply Chain is the global market for creativity in today’s modern economy. It is made up of the supply, incentives, and demand that drive how and why people create. And it rests on the technology-enabled superpowers which turbocharge all of the above, generating massive market value in the process.

Supply

I have always believed that people are inherently creative. I was raised by parents who encouraged me to pursue my many — and scattered — creative interests. I grew up playing the drums, learning to code in high school, studying Computer Science in college, and dabbling with music, photography, painting, and writing all along the way. As a career, my interest in creativity led me to build websites for artists at Atlantic Records and eventually build products which enabled hundreds of millions of people to take and edit photos at Aviary and Adobe. The belief that people are inherently creative was also one of the factors that led my former co-founder Nir Zicherman and me to build Anchor, a platform which democratizes podcast creation, and was acquired by Spotify in 2019.

But in 2022, I believe it should be obvious to nearly everyone that we are all creative. We write, take photos, edit videos, design presentations. We all create content using tools like our phone’s camera, Snapchat, Instagram, Facebook, TikTok, BeReal, Twitter, and YouTube. We make podcasts, webinars, short films, and crafts. We share the things we create with family, friends, our co-workers, and strangers all over the world. We spread knowledge with colleagues, partners, and prospective employers using tools like Google Docs, Coda, Photoshop, Final Cut Pro, Loom, and Descript, while sharing ideas, photos, memes from Giphy, and weekend plans with our friends and family using Pinterest, Google Photos, and WhatsApp. We’re all being creative all the time. We all create constantly, every single day.

Incentives

Our incentives to be creative are varied. We create to communicate. We create for money. We create to express ourselves artistically. We create because our work requires us to do so. We create for attention; we like the feeling we get when the view count on our video starts shooting up. Sometimes we create simply to make our friends laugh.

Technology has helped streamline these incentives. Notifications buzz our phones and inboxes to let us know when others interact with our content, giving us micro doses of joy and much-needed confidence. Our teammates quickly provide feedback on work in real time using a multitude of multi-player enabled creative tools. For those of us looking to monetize our work, we can easily sell our audience’s attention to the highest bidder on platforms like Instagram, YouTube, or TikTok, for we are today’s version of yesterday’s magazines, television stations, and billboards. With enough distribution, a few of us are even able to get our audience to pay us directly for our work through platforms like Patreon, Substack, OnlyFans, OpenSea, Fanfic, Pietra, and more.

Regardless of our own unique incentives, we all create because there is one universal incentive which all creators share in 2022: there is overwhelming demand for creativity.

Demand

You are a perfect example of this demand. Think about it: how do you spend your time? You watch videos on YouTube, look at stories and photos and Reels on Instagram, get sucked into TikTok for hours at a time, interact with designs by your colleagues on Figma, laugh at memes your friends send you on Telegram, binge watch shows on Netflix, look at friends’ baby photos on Facebook, read thoughtful essays on Substack, listen to podcasts on Spotify, scroll through beautiful websites on Chrome, read books on Kindle, scan your local news, get dinner recipes on Pinterest… the list goes on and on and on. You are the ultimate destination for a long list of content that once began as a spark of creativity in the mind of another person somewhere else in the world. This essay, which you are reading right now, and the hand drawn graphics referenced throughout are all expressions of my creativity.

By this time next year, you will probably be consuming even more creativity as more products and services launch and your devices become more powerful. And while your demand for creativity continues to grow, so too does that demand for nearly everyone else on this planet; especially people who are just coming online for the first time in their lives, such as in emerging nations which are just gaining access to high speed internet.

For every region which is nearing saturation of creativity supply and demand, there is another one just getting started on the bottom of the S-curve. We are not at the end of something; we’re at the beginning of global creative ubiquity.

Superpowers

All of the above is happening in greater volume and velocity than ever before through what I like to think of as creative superpowers. Technology has made us all more creative through better tools. It has given us the ability to more easily achieve our creative goals (whether they be financial, social, or otherwise). And it has made it possible for us to find an audience for our work, no matter where in the world we are, physically. As we consume, we become more inspired to create ourselves, fueling a virtuous cycle of creativity that spins faster and faster as technology improves.

Creative tools

The history of computing has proven time and time again that all forms of creativity eventually become democratized by technology. The costs of creating content are always being reduced by technology, and the abilities of people to create compelling content through technology are always increasing.

This trend began long ago (Printing press? Invention of the pencil? Rock, blood, and berry juice used to scribble on walls of caves?), but perhaps became most apparent on the internet through text and publishing of the written word. It was previously challenging for non-journalists to publish written text. But eventually, Blogger, Wordpress, and ultimately Medium made it vastly easier through easy-to-use tools and one-tap publishing capabilities. Platforms like Facebook and Twitter took it a step further by simplifying the format and enabling distribution of text to people within our social networks. Now nearly everyone writes and publishes some form of content.

The same happened for photos. It was previously extremely challenging for anyone other than professional photographers to take, edit, and share photographs at scale. Then, Instagram made it possible for anyone to take and share beautiful photos with the tap of a button. Now, everyone takes and shares photos.

YouTube and TikTok did the same for videos. Anchor did the same for audio and podcasts. And the same can be said about a number of other categories. No-code website development products, platforms that enable the easy creation of video games, and even software that democratizes the creation of businesses/products are a few examples of the trend playing out in other categories. Many others will undoubtedly follow.

Artificial intelligence

Technology-enabled creative tools have democratized creation to the point where any of us can create with the right tools, skills, and investment of time. But artificial intelligence (AI) is taking things many steps further. AI has recently reached a new tipping point, enabling any of us to achieve incredible feats of creativity by typing a few words into a text box. Cutting edge AI models such as Stable Diffusion, OpenAI’s DALL-E, and Midjourney make it possible to create visual images of limitless possibilities, making it possible for any of us to create things we previously could only imagine within seconds.

And while today’s mainstream AI models are focused mostly on imaging, it’s only a matter of time before audio, music, video, text, and countless other creative mediums are soon disrupted by AI. Even writing code itself is in the process of being disrupted by AI.This past weekend, Nathan Baschez and the Every team introduced a product called Lex that helps writers by using AI to provide automatically-generated suggestions for what to say next. Within hours, Lex had thousands of people signed up to its waiting list. It’s not hard to imagine a world in which the cost of creativity plummets as a result of the rise of AI-enabled generative media. Ten years from now, will major movie studios be investing hundreds of millions in blockbuster films? Or will a state of the art AI model do all the work instead for orders of magnitude less?

Ubiquitous access to ultra fast internet

As of 2022, a 83% of the world’s population has access to a smartphone. That means that the vast majority of us on this planet are carrying around pocket-sized supercomputers virtually everywhere we go. And here in the US, many of us now have access to 5G internet at all times. With 5G, we’re able to download or stream massive amounts of creative media to our devices virtually instantaneously. Just think about everything you are now able to do from your mobile device. You’re able to watch full length movies with the tap of a button, access the world’s complete catalog of music, and even edit 4K video collaboratively, with your teammates, in the cloud. Ubiquitous, ultra fast internet makes the Creativity Supply Chain hum.

And there are major tailwinds behind this superpower. 5G market penetration is only at 13% globally and it is expected to reach 64% by 2030. As more people gain access to 5G in the coming years, it’s only going to get easier to instantly perform creative tasks and consume creative media. And 6G, which will be even more powerful than 5G and make creative mediums like AR and VR more accessible, is expected to begin rolling out in the year 2030.

Machine learning

One of the biggest breakthroughs for creativity over the past few decades has been the rise of media platforms such as Instagram, YouTube, Snapchat, Netflix, Spotify, TikTok, Twitter, Outschool, MasterClass, LinkedIn, and countless others across a variety of categories. While media platforms have traditionally relied on curated friend and interest graphs to match supply-side media with demand, platforms have recently learned that leveraging machine learning can drive far greater efficiency in distribution. As a result, media platforms across nearly every category — including social networks — are abandoning curation in exchange for recommendation media, which I recently wrote about extensively in a recent essay. Further advancements in machine learning and recommendation media will have massive implications on both the supply and demand sides of creativity.

For creators, finding an audience for your creativity will happen automatically upon platform distribution. We’ll upload our content, and the platform’s ML algorithms will find the perfect audience at the perfect time for our work. And on the demand side, we will instantly and easily access the exact content we want to consume with far less friction. The supply and demand flywheel of creativity will spin far faster as a result of machine learning.

The Market for Creativity

Given how quickly all sides of creativity are becoming propelled by technology, it’s clear that each and every one of us will make up the total addressable market of the Creativity Supply Chain. But make no mistake; the Creativity Supply Chain isn’t some theoretical concept from the future; it’s happening right now.

As of this writing, public companies with core businesses in the Creativity Supply Chain — such as Spotify, Shopify, Squarespace, Adobe, and Snap — represent more than $370B of market capitalization. And companies with big stakes in the Creativity Supply Chain — like Meta, Apple, Alphabet, and Netflix — represent more than $7T of market capitalization.

In private markets, look no further than the latest major technology acquisition — Adobe’s $20 billion acquisition of Figma, the largest ever acquisition of a venture-backed company — for proof that there is massive demand for companies contributing to the Creativity Supply Chain.

And Canva, a company that makes it easy for anyone to create beautiful images and graphics for both professional and personal use cases recently topped 100M monthly active users and is reported to be generating more than $1B in annual revenue.

The demand for Creativity Supply Chain businesses will only increase. As we all spend more of our time creating and consuming, and more of our personal and professional lives become transformed by technology, creativity will be even more entrenched in nearly everything we do on a daily basis.

Creativity isn’t fleeting. Creativity is forever.

You can’t contain or limit the potential of creativity to specific incentives, such as the monetization of one’s top 1,000 fans. Yes, there will always be financial markets built around monetizing the work of top creators. But the reasons why people create are far more universal and fundamental to our common humanity, and these reasons result in a tremendous amount of aggregate value. And this is why the opportunity for creativity will continue to grow and flourish in lock step with technology. I like to imagine that today’s creative tools are the ones Steve Jobs was dreaming of when he first called computing a bicycle for the mind.

Everyone is creative. Technology increasingly fuels our collective ability to create, the incentives that motivate us to create, and our insatiable demand for the things others create. And through recent technological breakthroughs in artificial intelligence, machine learning, and high speed internet access, the market for creativity is destined to reach new heights in the coming years and decades.

What do you think? Is creativity becoming the central driving force behind our modern Internet economy? And what’s next for the technology that powers the Creativity Supply Chain? If you’re working in this space or have feedback on this essay, shoot me an email or reach out to me on Twitter or LinkedIn.

The Creativity Supply Chain was originally published in Lightspeed Venture Partners on Medium, where people are continuing the conversation by highlighting and responding to this story.

Today I’m announcing that I’m joining Lightspeed — one of the world’s leading venture capital firms — as a Partner.

Earlier this year, I announced that I was stepping away from my work on Anchor (after co-founding the company 8 years prior) and Spotify (where I led the podcast, live, and video businesses after we were acquired in 2019). When I announced that I was leaving, I shared that I had decided to pursue a new opportunity in the early stage investing space.

Today I finally get to share more specifics about what exactly I’ll be up to: I’m joining Lightspeed — one of the world’s leading venture capital firms — as a Partner on the Consumer team. I’ll continue to be based in New York City (and you can read more about Lightspeed’s growing footprint in NYC), but will be investing globally in the best, brightest, and most creative teams in the world, helping them to pursue their missions and build world-changing companies.

My interest in venture capital stems from two of my life’s passions. The first is in technology. I’ve long believed in the power of technology to advance our collective potential. This led me to learn how to program in middle school, seek a computer science degree in college, and build software for a living.

My other passion in life is creativity. For as long as I can remember, I’ve enjoyed creating, be it music, art, photography, software, or businesses. But I don’t just love creating myself, I also love helping others create, and this has become the foundation of my career. I’ve helped musicians make music during my time at Atlantic Records, photographers take photos with Aviary and Adobe, and millions of podcasters make podcasts through Anchor and Spotify.

Throughout my own experience at startups and building Anchor, I learned about the power of venture capital to accelerate both technology and the creation of transformational businesses. Over the past few years, I’ve been able to take my passion for helping others create in a new direction as a part-time angel investor to 50+ companies. I’ve loved working with small, hyper-focused founders and teams and sharing with them what I’ve learned throughout my own journey building products and scaling them to hundreds of millions of users. Now, I’m excited to make it my full time job in this new role with Lightspeed.

I first had the opportunity to get to know Lightspeed as I was building Anchor and raising capital. More recently, as I started to consider what I might want to do next for the next chapter of my career, I began spending a lot of time with a number of people who would eventually become my new partners, including Mercedes Bent, Faraz Fatemi, Ravi Mhatre, Justin Overdorff, Nicole Quinn, Bejul Somaia, Sydney Sykes, and Alex Taussig, to name a few. Also during that time, Paul Murphy (a friend from the NYC tech community who also hired me for my first startup job at Aviary), joined Lightspeed to help continue to build their growing presence in Europe. Through each meeting with the team, I was continually struck by how perfectly aligned and natural each conversation felt across a number of areas, including strategy, mission, and values. I even had the opportunity to angel invest alongside them in some incredible companies like Cameo and Zola.

I believe Lightspeed is building the venture capital firm of the future through four critical components, each of which strongly compelled me to join:

1. Global-first approach: Lightspeed is truly global, and has been for more than 15 years. This will be even more critical over time as the tools and skills for building technology businesses become further democratized.
2. Deep domain expertise: When I was building Anchor, it was always important to me to work with partners who had real experience building and helping some of the greatest companies in the world succeed. Lightspeed has this expertise across a variety of sectors, including Enterprise, Consumer, Growth, Fintech, Health, and many more.
3. Partnerships, not individuals: Lightspeed approaches venture through collaboration and partnership, not by simply elevating a small handful of individuals. I found this approach to be highly effective during my work at Anchor and Spotify, so it was important for me to join another team that operated similarly.
4. Full-stack support for teams: The best firms of the future will need to be able to back teams at all stages. This will become increasingly important given the uncertainty in the market ahead. Lightspeed’s $7B+ of new capital will give us scale and resilience in the years ahead.

Given Lightspeed’s unique approach, the strong relationships I’ve formed with the team, and the potential to work with incredible new companies, I couldn’t be more excited for this next chapter of my career. I’m ready to hit the ground running, so if you’re building a new consumer company and feel like I might be able to help you accelerate your mission, please get in touch. You can find me on Twitter or LinkedIn. And if you’d like to read some of my recent essays about product, strategy, and technology, check out my work here on Medium or on Substack.