I’m spending a lot of time using AI to build out my own “personal back office.” Many friends, founders I work with, and my partners at USV are, too.

Before AI, it was not feasible from a cost or time perspective to invest in building custom software to help one automate and/or manage various aspects of our non-professional lives. But with AI, we can delegate away tasks that give us superpowers or that we simply find annoying or tedious.

Just a few examples:

I’ve been iterating on an Advisor agent that serves as a public equities advisor. First I used it to translate a specific investment thesis into a strategy. Then, it took that strategy to build me a portfolio that it automatically monitors daily, evaluates for excessive drift or thesis changes, and rebalances as necessary. I may open source this agent soon.

I also have a Nutritionist agent that pulls in all of my personal health data (historical blood tests, x-rays and MRIs, VO2 max tests, DEXA, plus real-time Apple Health data) and maps it against my daily meals to help me hit my current fitness goals. It checks in with me around meal time to make sure I’m logging accordingly, and then lets me know how I’m doing on my calorie and protein goals throughout the day.

Here’s a different kind: my partner Fred Wilson built a Restaurant Recommendation agent that actively monitors a number of his trusted sources for restaurants that it believes he will like and adds them to a database. Then, if Fred wants to eat at any of them, it automatically accesses his Resy, OpenTable, Doordash, and Blackbird accounts to order food, book reservations, etc.

I’m sure many reading this have their own AI “personal back office” stack, too.

There is a real possibility that over the long run, I’ll end up ditching a lot of the stuff I’ve built and that the value will have come more from the fun of building the back office than the actual output. Will Manidis’ excellent essay Tool Shaped Objects captures this well.

But I’m optimistic that for every few abandoned agents I leave behind, something real will stick.

And I’m excited to see the tools other people build, along with the full-fledged products startups will create to support the new “personal back office” movement.

More than a decade ago, I had the privilege of pitching USV when I was raising capital for the podcasting startup I was building at the time, Anchor. While I dreaded many fundraising pitches, I was excited for this one, for a few reasons. First, USV was one of the world’s most legendary and best performing venture firms, especially in New York, where I’m from and where we were building the company. Second, I knew they were into products that more mainstream VCs wouldn’t touch, and few others seemed willing to back a podcast company at the time. Third, I had learned a lot about building my startup as a daily reader of Fred Wilson’s blog, AVC.

We didn’t get the investment. But the conversations stuck with me: it was obvious that they were students of the world’s best products and founders, deeply opinionated, and genuinely curious about things that were still too weird for most VCs to take seriously. I got to know them much better over time, as both a friend to the firm and eventually an LP. And what I consistently heard over the years since was that USV’s reputation with both their founders and LPs is every bit as strong as their returns. Occasionally, after exiting Anchor to Spotify and becoming a VC myself, I found myself channeling their sensibility through my own investments when they still looked strange to most people.

USV describes itself as “a conversation”. They form their strategy by talking to each other. A lot. They do this as a very small and collegial partnership, in person, in NYC. It’s how I believe I’ve done my best work, too: building Anchor with my co-founder Nir, in a small office, where we’d talk strategy and whiteboard obsessively, sometimes for weeks, before touching a line of code. Or at Spotify post-acquisition, with Gustav Söderström, Daniel Ek, and the team, where we’d debate deeply, and align as a 6,000+ person company before building anything. Gustav has a famous saying within Spotify: “talk is cheap, so you should talk a lot.” His point: building the wrong thing is expensive, so earn the right to build by thinking hard first. USV operates the same way. Talk a lot, form true clarity of thought, and don’t be afraid to look weird before being right.

Venture capital has a prevailing strategy right now, and it’s not a secret: raise a war chest of capital and deploy aggressively into companies that look like they can become category leaders. Capital and velocity are the weapons. And it has worked extraordinarily well. The funds that deployed into consensus-winning AI companies over the past few years, backing the infrastructure, the foundation models, and the clear category leaders early, will generate some of the best returns this industry has ever seen.

I also believe the AI era will produce something the last few years haven’t: an enormous proliferation of abundant software, abundant founders, and new business models where knowing what you’re looking for before it arrives becomes a distinct advantage. The total volume of what will be built over the next decade will dwarf anything we’ve seen before. In that environment, I believe the winning strategy will be to know exactly what you’re looking for before it arrives. In other words: to be thesis-driven. That’s always been the USV playbook, and I think it’s exactly the right one to be running in the years ahead of us.

I’m grateful to everyone who has shaped how I think and invest. I’m excited for what comes next. As always, I’ll be based in New York City but investing all over the world, mostly at the seed and Series A stages. If you’re building something on the edge, something that doesn’t automate, but instead obliterates, we’d love to hear from you.

Late last year, the AI note-taking product Granola released a feature called Crunched, their take on an end-of-year, Spotify Wrapped–style recap (Disclosure: Lightspeed is an investor in Granola). Crunched left me stunned. It made me realize just how much Granola had learned about me after transcribing many of my meetings throughout 2025. And judging by my X timeline, plenty of others felt the same. It made me wonder: What does ChatGPT know about me?

So I asked both Chat and Granola what they knew about me. I asked them to get specific, deep, personal. The differences were immediately clear: Chat was great at knowing the things I had specifically asked about: flight hacks, how to interpret my latest blood test results, things I liked to write about (which it affiliated with my interests), and problems I had tried to solve throughout the year.

Granola, on the other hand, seemed to have a deeper understanding of me as a person. My values, relationships, emotional intelligence, actual interests. It knew the things I had said out loud throughout the many conversations it had transcribed. Some of these were in group settings. And some were one-on-ones that bounced between professional and personal subject matter. But none of it specific questions I had asked the AI.

The differences in these two products’ understanding of me made one thing very clear: there’s a stark difference between active context and passive context.

Active Context

Most of what AI knows about us, it knows because we chose to share it. Asking ChatGPT for travel recommendations or daily workout plans is an example of Active Context. Querying Google or Gemini for a phone number or research help is another example.

If you directly and consciously engage with an AI, chances are you’re giving it Active Context. And OpenAI is clearly extremely well positioned to be the King of Active Context given how synonymous it’s become among consumers with AI question answering.

Passive Context

But there’s clearly magic, and potentially even greater value, in passive context. What Granola observes in meetings is one example. Access to our email inboxes is another. Crunched was a mind-blowing experience, but a few months earlier, I had a similarly impressive experience chatting with Poke, which made me negotiate with it to access its subscription tier.

Throughout our negotiation, it became clear that Poke knew an impressive amount about me just by doing inference on my email inbox. As one example, it knew I was both a VC and someone who was actively paying for a Whoop subscription, and therefore assumed I could easily afford a $9.99 Poke subscription.

Companies with significant stores of Passive Context include Google with email and calendar and docs, Apple with messages and email and health data, and Meta with interests and browsing behavior.

What AI Wants to Know

It seems likely that companies with a lot of Active Context will want more Passive Context, and that companies with a lot of Passive Context will want more Active Context.

We’ve seen ChatGPT launch several products aimed at capturing audio (Passive Context), and recent rumors suggest that this functionality will be the focus of their upcoming hardware device. Meta, on the other hand, seems to be investing heavily in consumer AI products, which if they nail would give them access to more direct Active Context.

While both are valuable, I expect Passive Context will ultimately be the more important, especially for consumer businesses. In consumer, products need to eventually be ad-supported (because ads are how consumer products become ubiquitous and available to all).

The consumer AI product that wins will be the one that gets people comfortable with an AI that can passively observe in the background.

Since I was a little kid, I’ve always loved watching live sports. But over the past few years, something has shifted. I’m enjoying them more than ever. I crave turning on the Knicks on a weekday evening to watch Jalen Brunson play heroball in the fourth quarter. Or hearing Scott Hanson take me through every scoring drive on RedZone. Or, when he was still on the Mets, seeing Edwin Díaz trot out from the bullpen to close out a ninth-inning high-wire act.

The true unpredictability of live sports seems to grow more irresistible with each passing year. Is it because I’m getting older? Am I turning into my father, who for my entire life has been a die-hard fan of sports at all levels? Or is something else at play?

Yesterday, Lulu Cheng Meservey published another brilliant essay called Standing Out in 2026. In it, she writes:

In this world, the real has never been more precious, refreshing, special, rare.

As the creator of tech’s “Go Direct” manifesto, Lulu is, of course, referring to how CEOs and tech leaders should tell their stories in an AI-first world. But I can’t help wondering if the realness of live sports is the key reason I feel so captivated by them right now.

Sunday night’s division-settling game between the Pittsburgh Steelers and the Baltimore Ravens is a great example. There were multiple moments in the fourth quarter alone when anyone watching was certain they knew the outcome, only to be proven wrong. It was thrilling and unpredictable in a way I no longer recognize in most other forms of content I consume.

Social media feels staged or purely generated. Modern music is formulaic. TV is algorithm optimized. Movies are mostly repackaged IP (key word mostly: film still feels like the medium where we’re seeing pockets of true creativity and there were many standout examples in 2025).

But sports remain dynamic. A safe haven for true, unpredictable realness. As we increasingly question the authenticity of nearly everything, I expect more of us to find comfort in good, old fashioned athletic competition, whether it’s a slow, boring Tuesday night baseball game or the bleak heartbreak of a season-ending, blown field goal.

The first phase of social media was defined by social graphs. Creators amassed followers, and when they posted content, it would be distributed to their followers’ newsfeeds. This guaranteed that at least some followers would consume a creator’s content, even if it wasn’t perfectly tailored to their interests. It wasn’t very efficient, but it worked.

Then came the second phase, defined by “the algorithm,” popularized by TikTok, and copied by Reels and every other platform. I called it “Recommendation Media” instead of social media (and wrote about it in The End of Social Media). This phase was far more efficient: creators no longer needed to build up a follower count to get distribution. They simply had to make good content and let the algorithm do the rest, finding the perfect consumer for any given video. This made content distribution dramatically more efficient, and platforms were rewarded with much higher engagement (a.k.a. addiction).

Ben Thompson summed it up perfectly in a conversation I had with him on this topic:

“The other angle is that to succeed with recommendation media, to be a recommendation engine, which I think is honestly another way to say to an aggregator, you need volume, you need huge amounts of stuff on your platform on both sides, both on the consumption side and the creation side. How do you get big volume? You get big volume through innovation, through doing something unique and so it touches on both sides of it in a way.”

We’re now entering the third (and final) phase of social media. Platforms love distribution efficiency because it drives consumption, and in this third phase, they’re guaranteed to get it. As generative models improve, platforms will pair on-the-fly content creation with algorithmic distribution. Instead of sourcing from creators, they can generate content tailored to individual preferences at scale. Production shifts from human to synthetic. With these models (like the ones we’re seeing in Sora and Vibes), platforms will increasingly generate content dynamically to perfectly match a consumer’s interests, rather than selecting from an ocean of human-made videos.

The good news is that people will keep creating, because it’s what we do. But the incentives will be very different.

On one end, we’ll see creators moving toward differentiated human experiences that resist commodification. Live events, community, and shared experiences are already on the rise, and that trend will accelerate. On the other end, we’ll see platforms rewarding uniqueness instead of eyeballs, likely through direct payments to creators when they feed the models (but skipping the distribution part). People will no longer be incentivized to simply reach as many viewers as possible, because platforms will be able to do that without them.

What platforms’ AI won’t be able to generate is something truly unique: a human’s name or likeness, the feeling a revered brand evokes, or a net-new meme or joke that only a human could dream up. We’re all going to help the models get smarter, until they no longer need us.

If there’s any silver lining to this third phase, it’s that some form of creativity will continue to be rewarded, at least for the foreseeable future.

Overall, the system will keep optimizing for distribution efficiency, and we’re fast approaching the point where platforms no longer need creators to feed the machine.

AI is going to accelerate the meme economy. By a lot.

Everyone knows that the world runs on memes, which is why we're all so performative on X, TikTok, Instagram, etc. Even in traditional media, everyone performs when the camera is rolling.

Every person at yesterday's White House press conference (on both sides - US and Ukraine) had likely planned out their exact individual performances in advance. It being the most viral clip on the internet is not an accident. No one knew exactly how it would play out but they certainly all wanted it to go viral.

AI feeds on context, and we're increasingly willing to give it all the context it needs. And so the cameras are going to be rolling much more often. And the mics recording. It's not a coincidence that more and more of us are recording our Zoom meetings. And some, our IRL meetings as well.

As AI enhances every aspect of our lives, we'll all work harder to influence the AI's context window, especially as content in all settings becomes a commodity. The more unique our performance, the more likely we'll be able to get the AI to meet our objectives.

SEO, influencing the TikTok algorithm, etc — this was all just the warmup act.

“Wrapper” isn’t a dirty word. It means a product built on top of a technology. Almost every great product you’ve used it likely a wrapper of some other technology. Stripe, Robinhood, and Uber made existing technologies more accessible and valuable to everyday users. The same is true for Google, Microsoft, and Apple. Everything is a wrapper of a wrapper of a wrapper.

Yet somehow, in 2025, we’re obsessing over the underlying technology of AI. So much so, that today, January 27th, Wall Street is punishing the tech sector as a result of DeepSeek, the China-based research lab and model. The breakthrough of DeepSeek is driving down stocks like NVDA as much as -13% because it proved that state of the art AI models can be built and deployed for fractions of what it’s cost companies like OpenAI, Google, and Meta.

Does this matter? In the short term, absolutely yes. It challenges the fundamentals of many of the most important technology companies over the past 24 months. But on the other hand — no, it doesn’t. If anything, this will only accelerate the race to AI and great technology getting into products and the hands of customers.

Startups that focus on technology over product may have advantages in the near term. The “wow” factor. On the other hand, they are far less durable over the long term. Over a long enough period of time, technology always commodifies — especially on the internet.

Products, on the other hand, can live forever. Apple, Disney, Coca Cola, Ford, Nike, and the list goes on. There’s nothing more durable and less commodified than great products which are beloved by actual paying customers. And so not only is “wrapper” not a dirty word — it’s a winning strategy.

I’m looking forward to seeding more simple wrappers that consumers love but overly intellectual VCs hate.

About 10 years ago, my coworker Nir Zicherman and I got talking about podcasts. We felt that podcasts were exploding as a medium, but it was really hard for most people to make them. That conversation turned into an idea, which turned into a prototype, which turned into a company called Anchor, which somehow turned into the biggest podcasting platform in the world. Spotify ended up acquiring us.

More recently, we’ve been talking about education. We can’t make sense of the fact that despite all the technological advancements made over the past several decades — especially in AI — humans seem to be getting less intelligent. That conversation has also now turned into a company, and it’s called Oboe.

Our mission is make humanity smarter, and we’re building products that leverage AI to help people learn 10x more efficiently, effectively, and affordably than ever before. We do this through LLMs and a novel method of education that Nir has been building over the past year. While we’re co-founding this together, Nir is CEO this time around, and I’ll remain a full time partner at Lightspeed.

To help us get started, we’ve raised $4M from some of our former Anchor investors and a few of the smartest humans we know, including Eniac Ventures, Haystack, Factorial Capital, Homebrew, Offline Ventures, Scott Belsky, Kayvon Beykpour, Nikita Bier, Tim Ferriss, and Matt Lieber.

We’re currently hiring the core team—in particular, a product designer. If you or someone you know is based in the NYC area and is interested in joining us, please reach out at jobs@oboe.fyi. And if you're interested in beta testing Oboe once it's ready, sign up here and follow us on X @oboelabs.

Arthur C. Clark once said that “any sufficiently advanced technology is indistinguishable from magic.” The magic-like experiences each of us have felt using AI over the past few years help explain why so many startups have found instant Product Market Fit. Today’s AI products are so magical you just can’t help but want to use them. ChatGPT is the fastest growing consumer product ever. And public launches of other AI-native products like Waymo, Suno, Midjourney, and perhaps even Google’s new Notebook LM podcast feature are other recent examples of instant Product Market Fit. As one AI founder recently said to me: “it doesn’t make sense for us to launch the product until we reach a technical breakthrough, because the breakthrough is the product.”

This isn’t your typical Product Market Fit; instead, it’s what I call Magic Market Fit: when a technological breakthrough is so magical that it instantly meets a market demand. Unlike Product Market Fit (PMF), which typically requires months or even years of experimentation and constant iteration across product, design, engineering, and go-to market efforts, Magic Market Fit is met the moment a technical breakthrough is achieved. ChatGPT is a great example of this phenomenon. The design and user experience is ridiculously simple – it’s a basic chat interface. On the other hand, the technology underneath it is anything but simple, and the results are so impressive that people can’t help but be drawn to it. 

In the absence of technology, startups needed to hack their way to PMF through product and design.

The traditional search for PMF looks quite different from what we’re seeing today with AI, and products built during the mobile revolution of the 2010s serve as a good example of this distinction. After the launch of the iPhone and Android platforms, builders of early mobile apps had to navigate an evolving landscape supported by hardware, software, and infrastructure that was far from robust in the early days. It’s easy to forget how many of the iOS and Android features and services we all utilize today didn’t exist in the early days of smartphones. And hardware features like smartphone GPS and cameras were only just becoming available in the early 2010s. Plus, the lack of cloud computing, lightning fast internet speeds, and longer battery life hindered the capabilities of startups and their products. Of course, all of these things quickly came online over the years that followed, and teams raced to adopt them as a result. But it took time, grit, relentless adaptation, and product hacks for startups to break through, and all of the above contributed to teams needing to iterate their way to PMF.

Fast forward to 2024, and the infrastructure exists for new products to be quickly built and distributed to many millions of people. The challenge now, however, is that the market is extremely crowded and there’s already an existing market leader established for nearly every category. Just think about how many times you’ve seen a new mobile app launch and you’ve thought to yourself, “haven’t I seen a dozen different versions of this app?” But this ability to distribute your product to millions with the flip of a switch also partially explains why AI products can find Magic Market Fit today. Building products has become easy. But building magic? That’s special. And that’s exactly why customers are instantly drawn to AI products.

Tech iteration is the new product iteration.

Beyond this now-mature infrastructure which enables developers to quickly and cheaply build and deploy products, there are other major factors contributing to the rise of Magic Market Fit. First, the exponential growth in computing power, driven by advanced GPUs, has made it possible to train deep learning models at unprecedented scale, which would have seemed impossible only a few years ago. Plus, the availability of huge data troves from the internet, social media, and many of the products built during the mobile revolution, serve as the fuel needed for AI products to learn and perform. And lastly, large investments in AI research and talent from both the world’s biggest companies and startups alike has created huge demand for teams to translate their tech breakthroughs into market-ready products. All of these factors have converged to create a unique moment in time where magical technologies like ChatGPT, Midjourney, and GitHub Copilot can achieve Magic Market Fit once a new technology is unlocked.

With the search for Magic Market Fit driving roadmaps and hiring plans, perhaps it’s no surprise that startups look differently than they did only a few short years ago. Teams are forgoing hiring extra PMs and sales leaders in favor of doubling down on engineering and research, hoping to mine magic faster than their competitors. Even designers are becoming deprioritized in favor of “engineers who also know Figma.” But it’s hard to know if this trend will last. If history rhymes, and this AI market crowds just as quickly as the mobile market did, we may see things rotate back in the other direction, especially as engineering – and potentially AI research – becomes easier for less technical teams.

In the meantime, we should expect the pace of change – and investment – to accelerate towards a future where what really matters isn’t great product; it’s magic. 

Everybody hates ads. At least that’s what we all say out loud. But our revealed preferences tell a different story: we all actually love ads, because they fund most of the content we consume on the internet. Without ads, we’d have to pay for content with our hard earned dollars. But with ads, we pay with our attention, and we perceive the cost of the content as free. Ads have made the internet as we know it today possible.

Ads are also a really good business, because advertisers are willing to pay top dollar to access our attention. In the early days of the internet, this looked like simple banner ads brokered through manual deals on basic HTML websites. But over time, ads evolved into some of the greatest money printing machines ever invented, like Google and Meta. Even businesses we don’t often associate with ads, like Apple and Amazon, make a killing from them. And the little guys love them, too: many long tail publishers, websites, niche blogs, podcasters, and indie games are all funded by ads. Ads built the modern internet and funded our internet addiction, making billions of dollars and billions of us happy in the process. It's a perfectly tuned system that we all take for granted.

All good things must come to an end, and ads are no exception.

AI is increasingly challenging the business model of ads – the system that makes the open web feel free. For example: many of us are using Google search less frequently, along with the ad-supported websites that it leads us to. And in their place, we’re using answer engines like ChatGPT and Perplexity more. Each time we task these AIs with delivering us a single, definitive answer (in favor of wading through many potential answers via traditional search), the relationship between an advertiser and our attention is severed – and the ad never gets delivered.

Challenging the model even further, as AI models become more powerful and capable, they won’t just deliver us answers; they’ll also perform long, complex tasks on our behalf. They’ll do the busy work of research, making appointments, and even buying stuff for us. This will free us up to do other things, and as a result we’ll devote less of our attention to the content, and thus, the ads. At scale, this represents a massive change to the foundation of the open web. What happens to the perfectly tuned system when the funding dries up?

Premium content will become even more premium.

Tightly guarded walled gardens, especially built around premium content, will feel the least pain from this shift. Many will become even more valuable: as the overall amount of monetizable supply of ad supported content shrinks, the demand for higher quality content will rise. Ad dollars previously spent on commoditized content will be redirected to the best stuff on the internet. Plus, as we spend less time on busy work, we’ll spend more of our time entertaining ourselves, which means premium content will capture even more of our attention. And the best content will even find new ways to monetize: Reddit’s AI deal with Google serves as the perfect example of how valuable, niche content traditionally supported by advertising can be sold as training data for AI. And media formats that are more resilient to AI scraping, such as audio and video, will likely retain their advertising value longest. No matter how good an AI agent gets, you’re never going to ask it to watch your favorite Netflix show on your behalf.

But commoditized content, or anything that can be easily scraped by AI or replicated by competitors, will fare much, much worse. For this content to continue to exist, it’ll find new ways to be funded. For many of the sites we visit, this will increasingly be through user-funded subscriptions to offset the lost ad revenue. Those of us who can’t afford these will lose access to these services, widening the gulf between the haves and the have-nots. 

AI services will attempt to reinvent ads.

AI services will seek to capitalize on the end of traditional internet ads and invent new forms of advertising to capture the dollars that were previously delivered directly to publishers. Perplexity has recently revealed some early details of how ads will work on their platform, and OpenAI is rumored to be exploring this as well. While this is an obvious next step for the future of advertising, it’s unclear how directly the current ad spend will transfer over to this new model. And it’ll need to be structured such that publishers can participate in a way that incentives them to keep making content. Otherwise, they won’t be able to afford to exist.

Brand new models will also emerge to help publishers recoup lost ad revenues. As agentic traffic increasingly displaces human traffic, publishers will look to charge AI companies directly. Much of this will be through big, splashy content deals, such as OpenAI’s deals with News Corp and Axel Springer. But for smaller players that can’t afford to do deals themselves, they’ll work with third party services that will collect “tolls” on their behalf. Every time an AI hits their site to scrape content for their own service the AI will be blocked and forced to pay before the content is served. And as AI moves beyond content ingestion to agent-based task execution, APIs that help agents do their jobs will also be charged.

Content wants to be free.

For decades, the economic relationship between consumers of content and the providers of content has been hidden behind business models that were monetizing our attention. We didn’t put too much thought into it because we were getting what we wanted with minimal effort or cost. But as AI increasingly disrupts these models, the relationship between us and publishers will become much more transactional and direct. And we will feel it.

Over time, it’ll be a new internet, and the open web will be a memory. Great content will still find a way to reach us, just like it always has. But we’ll look back on the first few decades of the internet as the golden age of content, when everything felt free.

Thanks to Nir Zicherman, Sara Beykpour, Faraz Fatemi, Semil Shah, Lulu Cheng Meservey, Nikita Bier, and Kayvon Beykpour.

In a past life, I built consumer products. Building consumer products is all about “mining for flywheels” and extracting as much as possible from each well before it goes dry. Growth flywheels, talent flywheels, data flywheels, etc — all of these help your company grow bigger the faster they spin. But just like some things can make flywheels spin faster, other things can make flywheels spin slower. Adding unnecessary friction to your product onboarding flow can slow your product’s growth. Having an unappealing employer brand can slow your team growth. And so on.

Turns out, VC is a flywheel, too. And like consumer products, you want to get that flywheel spinning fast. When you invest in compelling companies, it makes it easier to meet other compelling founders, which in turn can lead to more compelling investments. When your investments return capital, you can recycle that capital into companies with bigger potential outcomes. When you surround yourself with other great partners, you increase the radius of potential investments (or potential hires). Conversely, having a bad “founder UX” when being pitched by startups can very quickly bring your VC flywheel to a screeching halt (founders never forget).

But the difference between VC flywheels and product flywheels is that in VC, the flywheel doesn’t always help you. In fact, it can sometimes hurt you. For example: just because you have access to more companies doesn’t mean you should invest in them all (you’d exhaust your bandwidth, let alone your capital base). Just because your flywheel is landing you invitations to speak at a bunch of conferences doesn’t mean you should waste your time flying all over the country (and not meeting founders). That’s because you, unlike a consumer product, are not software. You are not infinitely scalable. Instead, you need to get the flywheel spinning, but stick to your principles and the stuff that really matters for you. It’s kind of like staying up on a wave for as long as possible before toppling over near the shore. You want to specifically work on the things you need to improve in your form, not just try to copy Kelly Slater.

So get your VC flywheel spinning. But choose very carefully where and how to spend your time. Because just like a great product that’s shooting up the App Store charts, it’s hard to get the flywheel spinning again once it stops.

Thanks for reading! Feel free to follow me here on LinkedIn or X if you want to connect. And if you enjoyed reading this, I'd really appreciate it if you shared with your friends or teammates. 🙏

Something that happens often with startups is they choose to hire people who have really good resumes or experiences, even though they don’t have a specific job for them at any given moment.

Typically this happens because a VC or advisor wants to impress the startup, and they try to “show value” by connecting the company to someone with a compelling background that maps to the startup in some way. Then, the company falls in love with the idea and they bend over backwards to jam the person into the company. This can come in the form of excessive comp, inflated titles, or sacrifices on team values (such as IRL work vs. remote, etc).

But the worst part about it is that the person ends up doing a job that usually shouldn’t exist in the first place. This can have all sorts of negative downstream effects, such as: wasteful spend of capital / equity, constant misalignments between coworkers, and an inevitable firing that takes up mindshare and emotional capital of the founders. It’s a mess.

The cure to all of this? Just never hire opportunistically. Does it work out sometimes? Sure. And some teams are better at it than others. But it’s rare and much more commonly, a trap.

Instead, set a hard rule for your company: don’t hire anyone until something is actually broken. Then, define the exact specific role you need filled to fix the problem, along with requirements, level, comp, etc. And then go do the hard work of finding the exact perfect person for that role.

Hey everyone. It's been a while since I've written, mostly because I haven't had time to write the longer form essays that started this newsletter. But I've been missing writing. As a solution, I've started writing shorter-form "screenshot essays" on my X account, mostly focused on startup advice (based on my experiences as a startup founder and investor/advisor to other startups). These are much shorter posts, but I'm going to share a few of them here from time to time in case some of you find them interesting. Enjoy.

There’s a saying in startups: being early is the same as being wrong. If you’re right but you show up to the market early, it doesn’t matter – no one is ready to try your product. Shipping too late is just as bad, but for other reasons – especially if you’re doing so to overly prepare for GA in a closed beta.

First off, holding back on shipping to GA so you can optimize in a closed beta almost always leads to imperfect signals. Everyone in your closed beta is by definition biased. They proactively chose to join your small beta and jump through all the necessary hoops of signing up for it: filling out a Typeform, installing TestFlight, joining your Discord, and maybe even giving you feedback if they’re really pumped about what you’re building. They are extremely qualified, and this will show up in their engagement metrics and feedback. You will think your product is better than it actually is.

Second, because your closed beta is made up of highly qualified users, the feedback loop around user needs and product roadmap is heavily skewed. You end up building features that power users want – not the general population. The problem with this is that power users almost always want different things than the rest of the world, and you’re trying to get really big – not simply cater to a small, niche audience.

Third, and possibly worst of all: by the time you do finally get to GA, you’ve built a load of features specifically designed for power users – and now you have to support those features forever (or until you decide to pivot). You’ve signed yourself up for an eternal backlog of product and technical debt you must continue to pay off in perpetuity, thus slowing down your sprints on the stuff that actually matters. In other words, you are now simply a slow product team, maintaining features that can’t find and sustain PMF.

Conventional startup wisdom suggests you should perfect your product in beta before you go to GA. There’s a fear that you’ll “burn through your potential users” on an imperfect product. There are nearly 8 billion people in the world. You’ll be lucky if you can get a few thousand people to try your product in the first week, regardless of whether your product is great or terrible. I say skip right to GA, ship constantly (at least every week), and start learning on real-world data ASAP.

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.

Want to read more of my product strategy essays? Follow/subscribe on Substack, X, and LinkedIn.

Last week, the developer of Apollo (a popular third party Reddit client) announced that Reddit could charge the company $20M a year in API fees to continue accessing the platform's data.

In this new era of AI-first products, unique training data is the new gold. Data from user-generated platforms (like Twitter, which is also now charging for its API) holds exceptional value for AI training due to its sheer volume, diversity, and representation of human behaviors, emotions, and language use. The varied contexts and authentic interactions present in this data provide fertile ground for AI to learn, generalize, and predict effectively. Given that, it's no surprise that Reddit is now choosing to charge for it's API — an acknowledgement that they believe their data is now worth far more than they previously considered.

Think about it: with Reddit's vast assortment of user communities and topics, it offers a diversity of content that is particularly beneficial for training natural language processing (NLP) models. From light-hearted chats to intense debates, Reddit's rich text data encapsulates the complexity of human language.

Moreover, Reddit's unique structure enhances its value for AI training. Its upvote/downvote system is a goldmine for sentiment analysis, providing a ready-made mechanism for assessing user sentiment. In other words, it has reinforcement learning organically baked into its dataset.

The time-stamped nature of posts and comments also facilitates tracking trends over time, a feature that can be harnessed to anticipate future developments or comprehend the evolution of online discourse.

As we delve deeper into the AI age, the data from platforms like Reddit will continue to drive AI's growth, rendering it even more sophisticated and in sync with human intricacies. And so we should expect platforms to value it accordingly.

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.

Hello, reader! I’m trying something new with today’s post. Instead of my typical, biweekly-ish long read, I’m trying a shorter “thought starter” to head into the weekend (based on a tweet I quickly fired off a few hours ago). Enjoy! 🙏

Late in 2022, the media was writing off smart speakers like Alexa and Google Home as a “colossal failure.” Given the dramatic Amazon losses driven by the Alexa business, it seemed like a reasonable take. Here are a few example headlines:

• Amazon thought Alexa would be the next iPhone. Turns out it's a 'glorified clock radio.'

• Amazon Alexa is a “colossal failure,” on pace to lose $10 billion this year

• This article is more than 1 month old Alexa, how did Amazon’s voice assistant rack up a $10bn loss?

But then something happened: ChatGPT launched and everyone on the planet realized just how world-altering LLMs (large language models) and AI will be for our future.

Now, a mere two months later, the potential of AI has me realizing something pretty obvious: Amazon Alexa, Google Home, and smart speakers more broadly are going to go from “just OK for a limited set of tasks” to insanely great.

Just imagine: No longer will you only rely on your smart speaker for simple tasks like playing music setting a cooking timer. Your speaker will finally understand exactly what it is you're asking. It’ll be able to respond to you perfectly, just like a human would. It will answer complex questions, help you respond to your email, book air travel, and process detailed, multi-step commands.

A few years ago, many were predicting that “voice” would become the next great ubiquitous software platform. It didn’t happen, but that doesn’t mean it won’t. Perhaps it just needed today’s AI to become the future.

What do you think? Are we headed towards a voice-first future thanks to AI? Let me know on Twitter or LinkedIn.

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.

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.

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.

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.