How successful startups built high-converting waitlists?, Metrics to measure GenAI sucess? | VC & Startup Jobs.
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👋 Hey, Sahil here! Welcome to this bi-weekly venture curator newsletter, where we dive into the world of startups, growth, product building, and venture capital. In today’s newsletter -
Deep Dive: How successful startups built high-converting waitlists. What they did right and what you can steal.
Quick Dive:
Built with top VCs and founders: Explore our full stack of startup resources.
What are the right metrics to measure generative AI’s success?
Growth slowing down? Here’s what works -
Major News: Meta’s $14.3B deal with Scale AI looks like a buyout—but isn’t, Neuralink chip might give humans infrared sight. OpenAI to bring generative AI to toys & Wikipedia pauses AI summaries after editor backlash.
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📜 TODAY’S DEEP DIVE
How successful startups built high-converting waitlists. What they did right and what you can steal.
Every founder gets excited when the waitlist grows. A thousand signups. Ten thousand. But unless those people convert, you’ve just collected names, not demand.
Last week, I wrote about what makes a good waitlist conversion rate. The numbers were clear: most waitlists convert under 5%. That’s not traction. That’s noise. The difference between a dead waitlist and one that fuels real growth comes down to execution, structure, messaging, follow-up, and intent.
So I want to show you how some of the most product-savvy startups used their waitlists as growth engines. The one thing that is common among all startups is that they understand that a waitlist is not a landing page; it’s the first product experience.
Superhuman: Build desire before access
Superhuman had a waitlist of over 180,000 people before launch.
Instead of onboarding in bulk, they handpicked users. One by one. Every early user had to go through a short application and a 30-minute onboarding call. They weren’t just collecting signups. They were training evangelists.
Why it worked:
The onboarding call wasn’t just about setup; it was used to qualify, personalise, and close.
They used social proof and exclusivity. Users felt like they earned their seat.
Friends could invite others, but those referrals went to the top of the queue. That turned existing users into distributors.
It was slow. Deliberate. But it built demand. And when they did open up access, people were willing to pay $30/month, because they had been primed for value.
Arc Browser: Curate your early community
Arc didn’t go viral because of hype. It grew because they understood which users mattered early on and gave them a reason to care.
Their waitlist focused on PMs, designers, and early tech power users. The onboarding wasn’t automated. It was intentional. If you were in their target persona, you got access early. If not, you waited.
And while you waited? They stayed top of mind. Early users shared demos. YouTube creators reviewed private builds. Twitter was filled with people asking for invites. They turned their beta group into marketing.
Don’t open access too early. Quality over quantity.
Use content and community to keep people engaged while they wait.
Let the right people in first, not just the loudest.
Arc only dropped the waitlist once they hit v1.0. Until then, every access point was earned. That built loyalty and word-of-mouth.
Clubhouse: Manufacture FOMO with exclusivity
Clubhouse was the masterclass in waitlist-driven hype. You couldn’t join without an invite. Every time someone did get in, the app made it obvious, triggering notifications to friends, showing conversations, and sparking curiosity.
It felt like a secret club. You wanted in because you were left out.
Their growth didn’t come from paid ads or launch campaigns. It came from:
Invite-only access that created social urgency
Scarcity-driven hype amplified by influencers and VCs
Network design that made new users more visible to their circles
People were bidding for invites. That’s the power of perceived access.
Lesson: Exclusivity doesn’t work for every product, but if your app is social, communal, or time-based, a closed-door policy can be a marketing engine.
Robinhood: Make the waitlist the product
Robinhood’s launch playbook in 2014 became legendary. Their pre-launch site didn’t just collect emails; it created a live leaderboard. Everyone could see their position. Everyone could move up the queue by inviting friends.
The formula:
Sign up = join the waitlist
Invite 1 person = jump 100+ spots
Share more = get faster access
It gamified the process. It triggered urgency. And it worked.
They ended up with over 1 million users before launching.
You don’t need a fancy product to go viral. You need incentives. You need urgency. You need to show users how they win by spreading the word.
Dropbox: Reward referrals with product value
Long before waitlists were trendy, Dropbox hacked growth through its referral engine. When you signed up, you could earn more free storage by inviting friends. Not only did it grow their list, but it also taught people the product’s core value: cloud sharing.
The structure was simple:
Sign up and get 2 GB free
Invite a friend = 500 MB extra
Both people get rewarded
It was the perfect flywheel: the waitlist spread the product, and the product reinforced the value of spreading it.
Dropbox didn’t treat its waitlist as a launch strategy. They treated it as a distribution channel.
If your product has clear, shareable valuebake that into your signup flow. Let users market for you.
What you can steal
Across all five, one pattern stands out: Great waitlists are designed like products. They create value before users log in. They spark emotion, curiosity, urgency, and exclusivity. And they move.
Here’s how to build one that converts:
Treat the waitlist like onboarding.
Your product experience starts before someone signs in. If you make them feel ignored or forgotten after signing up, you’ve already lost.
Use friction as a filter.
Application forms. Manual approvals. These aren’t bugs, they’re features. Scarcity builds desire. Make it feel like access has to be earned.
Engage before launch.
People get cold when left alone. Send updates. Share product behind-the-scenes. Show them progress. Make the wait feel like they’re part of something.
Make it move.
Let users skip the line by referring others or engaging. Waitlists should feel dynamic. Show them their position. Let them move up.
Start small. Learn fast.
Don’t try to manage 10,000 people. Start with 100 who are the right fit. Talk to them. Onboard them. Learn what converts and what doesn’t.
Remember, a waitlist is the beginning of the user journey. Superhuman made people wait, but kept them warm. Robinhood made it a game. Dropbox made it rewarding. Clubhouse made it elite. Arc made it intentional.
Don’t collect emails. Build anticipation. Design a journey. Because when it’s done right, the waitlist doesn’t just lead to growth, it becomes the growth.
📃 QUICK DIVES
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2. What are the right metrics to measure generative AI’s success?
Every time a new platform emerges, we scramble to measure its impact. But early on, it’s rarely clear what to measure or why it matters.
We saw this with the early internet. In the 90s, people tracked things like “internet hosts” or “hits”, which just counted how many files your browser downloaded from a web server. More GIFs meant more hits. Not exactly insightful.
Then came the smartphone wave, and the confusion shifted to installed base vs. unit sales vs. ARPU.
Social media added its evolution: first it was “registered users,” then MAU, then DAU/MAU ratios. Each wave had its metrics, often chosen to make a company look good, not to reflect actual value.
We’re at that same messy phase with generative AI.
Benedict Evans recently broke this down brilliantly. He pointed out that many of today’s AI metrics sound impressive but don’t tell us much.
“Tokens generated” charts from Google or Microsoft are like 1990s internet bandwidth graphs;, something is growing, but we can’t tell what or why.
“Weekly active users” (WAU) is another one nice to report, but weak. If someone’s using ChatGPT once a week, is that meaningful? Probably not.
Many surveys ask people, “Have you used AI?” but what does that even mean anymore? Are we counting Snapchat filters or full-blown enterprise LLM workflows?
Even when the numbers are specific, they often lack clarity:
If a model becomes more efficient, the same task needs fewer tokens, so are we seeing a drop in usage or just better technology?
On the flip side, agents and media tools burn more tokens per request, so growth might reflect heavier workloads, not more users.
And we’re still guessing at what generative AI will become. That makes choosing the “right” metric nearly impossible.
Will it mostly remain as user-facing chatbots? Or will it quietly integrate into everything, like SQL or cloud compute?
If it becomes infrastructure used everywhere but rarely seen, then asking “how many people use AI?” is as pointless as asking “how often do you use a database?”
So what should we be tracking?
Engagement quality – not just logins, but how deep and meaningful the interaction is.
Retention over time – are people coming back, and are they using it more meaningfully?
Task replacement – what jobs or workflows are users abandoning in favor of AI tools?
Behavioural feedback – are people satisfied on the first try, or do they constantly?
Enterprise integration depth – is it casual use by the marketing team or core to ops?
Other things like how companies like Google and Meta measure deeper signals: reformulated searches, bounce rates, completion behaviour. These are second and third-order indicators that show what’s working, and they often feed back into making the product better.
Right now, generative AI lacks those feedback loops. If you ask an LLM a question and don’t try again, was the answer perfect? Or did you give up and switch to Google? No one can tell.
At the end of the day, all metrics eventually resolve to money and time. That’s where clarity will come from, only once we know what generative AI is truly for. Until then, we’re still counting hits.
You can read the original article here.
3. Growth slowing down? Here’s what works -
Every fast-growing startup eventually hits this point: the growth engine that once seemed unstoppable starts to slow down. The acquisition loop that got you here, whether SEO, paid ads, virality, or sales, just isn’t moving the numbers like it used to.
You try optimisations. Run experiments. Nothing breaks through.
So what's next?
Casey Winters, cofounder of Superside and former growth leader at Pinterest and Grubhub, breaks this down with clarity: when growth plateaus, it’s tempting to chase new acquisition channels. But most of the time, that’s not the real answer.
Startups often think they just “missed a channel” and need to add paid ads or launch a sales team. But that’s rarely true. Your original channel worked for a reason; it was the most viable option at your stage. New channels only become viable when something fundamental in the business changes.
What do we mean by “new channels”?
Virality scales when users share the product because it makes them look smart (like Notion), earns them money (like Dropbox), or adds value when their friends join (like WhatsApp). But virality fades, and what’s exceptional quickly becomes normal.
Sales work when you have a high enough LTV to support commission, and leads are easy to find. But it’s not plug-and-play. If your payback period is weak, this channel breaks.
Paid acquisition requires great retention and monetisation. The best customers get targeted first. After that, it gets harder and more expensive. You need strong LTV or a network effect to offset that decay.
Content channels like SEO or user sharing work best when users generate the content. Company-created content rarely scales. Content loops are long games, not quick wins.
From the outside, adding new channels looks like growth. But from the inside, the reality is more nuanced.
You don’t “find” breakout channels. You earn them by improving unit economics, deepening retention, or growing your product surface.
When Casey led growth at Grubhub, they didn’t just tack on SEO or TV ads from day one. Those came after restaurant supply grew, user behaviour improved, and conversion economics got stronger.
In practice, that means asking questions like:
Has our LTV improved to the point where we can afford a longer payback period?
Do we now have enough user-generated content to fuel SEO or social?
Has our product bundle changed enough to justify new messaging or outreach?
Have engagement loops meaningfully lifted retention or usage?
If the answer is no, then adding a new channel might give you a small bump, maybe 5-10%, but not the next mountain of growth. And that’s a distraction.
In most cases, the harder path, strengthening the core product or unlocking better monetisation, ends up being the more worthwhile investment than chasing new channels too early.
Engagement loops can support this, but won’t save you alone
You can layer on engagement tactics to lift lifetime value:
People-based support (concierge, onboarding teams)
Product tweaks (features that increase daily utility)
Incentives (discounts, gamification, loyalty)
Messaging (email, push, reactivation campaigns)
But these are multipliers, not foundations. If your product doesn’t deliver recurring value, no amount of nudges will change the curve meaningfully.
Plateaus aren’t signs you’ve failed; they’re checkpoints. And the smartest founders don’t respond by throwing more at the wall. They zoom out, ask what’s truly changed, and only then commit to new loops.
Because new channels don’t work unless you’ve earned them. And you earn them by building something that holds attention, creates value over time, and gets better the more it’s used.
That’s when you’re ready for the next phase.
You can read the original article here.
THIS WEEK’S NEWS RECAP
🗞️ Major News In Tech, VC, & Startup Funding
New In VC
Seven Stars, a San Francisco, CA-based venture capital firm that partners with founders building the next generation of AI companies, launched its inaugural $40M pre-seed and seed-stage fund. (Read)
Felicis, a Silicon Valley-based early-stage venture firm, announced the close of a $900 million Fund X, its largest to date. (Read)
Major Tech Updates
Tesla alleges ex-employee Zhongjie “Jay” Li stole confidential data on robotic hand sensors to start Proception, a Y Combinator-backed robotics startup. (Read)
OpenAI and Barbie-maker Mattel have formed a partnership to integrate generative AI into toy design, entertainment content, and digital experiences. (Read)
Wikipedia's parent organization, the Wikimedia Foundation, halted its AI summary experiment after a swift and overwhelmingly negative reaction from volunteer editors. (Read)
Meta is acquiring a 49% stake in AI startup Scale, valuing the company at over $29 billion and providing “substantial liquidity” to shareholders and employees via dividends—not a share buyback. (Read)
Apple unveiled its new Liquid Glass design at WWDC 2025, its biggest UI overhaul in a decade, inspired by the Vision Pro’s mixed reality interface. (Read)
Nvidia will no longer include China in its revenue and profit forecasts due to ongoing U.S. chip export restrictions. (Read)
New Startup Deals
Antimetal, a NYC-based provider of an infrastructure management automation platform, raised $20M in Series A funding. (Read)
Commons Clinic, a Los Angeles, CA-based provider of a provider of an integrated, specialty-led healthcare platform, raised $26M in Series B funding. (Read)
Landbase, a San Francisco, CA-based agentic AI company, raised $30M in Series A funding. (Read)
OneBalance, a London, UK-based framework for credible accounts combining chain abstraction, gas abstraction, and permission management, raised $20m in Series A funding. (Read)
Autonomize AI, an Austin, TX-based company which speicializes in AI-driven healthcare solutions, raised $28M in Series A funding. (Read)
Tombot, a Los Angeles, CA-based robotic tech company, raised $6.1M in Series A funding. (Read)
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