Before you launch a freemium model, read this. | VC & Startup Jobs.
Common AI product issues, Prove your startup idea worth backing & Choose right North Star metrics.
👋 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: Before you launch a freemium model, read this.
Quick Dive:
Common AI product issues and how to fix them.
How to prove your startup idea is worth backing.
How to choose the right North Star metrics for your startup?
Major News: Meta offering multimillion-dollar pay for AI researchers, OpenAI's unreleased AGI paper could complicate Microsoft talk, Claude can now build AI apps, CoreWeave CEO becomes a deca-billionaire & Top Tesla execs leave.
20+ VC & Startups job opportunities.
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📜 TODAY’S DEEP DIVE
Before you launch a freemium model, read this -
Most founders assume offering a free tier is the best way to drive product-led growth, especially if you are a SaaS founder. Low friction, more users, bigger top-of-funnel, it sounds like a smart strategy.
But in practice? Freemium quietly breaks more startups than it helps.
We’ve explored this before, how to evaluate whether freemium fits your business model. It’s a nuanced decision, and getting it wrong can drain your resources without meaningful conversion.
Recently, I came across this post by Jason Cohen shared one of the best breakdowns I’ve seen on how freemium works and how to reframe it to protect your business. I’m sharing the most practical takeaways here, with additional thoughts for operators trying to make this model work.
Freemium isn’t a product strategy; it’s marketing spend
Founders often treat freemium like it’s part of product development. “Let’s give people access, see what they do, collect feedback, then improve.”
But here’s the catch: most of those users will never pay. Not even $1/month.
Their behaviour, feedback, and requests will skew your roadmap in the wrong direction because they’re not your customer. They're tourists.
Instead, you should treat every free user like a marketing lead. Ask: How much does it cost to support this user, and is the ROI worth it?
Free users are not your ideal customers
This is one of the most costly misunderstandings.
Free-tier users often look engaged they sign up, click around, and leave feedback. But they rarely convert. Because the value of your product isn’t high enough for them to pay. It’s not just a pricing issue it’s a fundamental mismatch of need.
Real customers, the ones willing to pay $20/month or $200/month, have different problems. You don’t hear their voice clearly in a freemium model because they’re outnumbered 100:1.
And that feedback imbalance leads to misguided product decisions.
Conversion rates are worse than you think
Even legendary freemium products like Dropbox only saw 4% conversion (even lower) from free to paid. Most startups don’t come close.
In OpenView’s data, the average visitor → freemium conversion was 6%. Freemium → paid was 5%. Which means total visitor → paid conversion was just 0.3%.
So if you’re paying for ads, SEO, or content, your actual CAC balloons. It costs more to get paying customers through freemium than it does by selling directly to the right audience.
You still pay for tech support, freemium doesn’t exempt you
Support is a hidden cost most founders forget. Free users ask questions. They run into bugs. They expect answers.
You can route them to community forums or try ignoring support requests from free users, but that erodes your brand and kills conversions. Or worse: it makes your support team pick and choose who deserves help.
And if support is one of your competitive advantages, degrading it undermines the entire product experience.
Freemium has real upsides, but they’re marketing benefits
There are real advantages to freemium, just not the ones founders usually assume.
It lowers onboarding friction
It builds a pipeline for upsells
It boosts social proof ("10,000+ users")
It blocks competitors from winning the same user
But notice the pattern: these are all marketing outcomes. Not monetisation, not retention, not product-market fit.
So if you want to use freemium, own what it is, a lead-gen channel, not a growth strategy.
So what’s the fix? Charge the marketing department
If freemium is marketing, then account for it like marketing.
Estimate how much each free user costs: infra, support, operations. Multiply by the number of users. That’s your real CAC under freemium.
Now compare it to your other channels: SEO, paid ads, partnerships. Which gives you better ROI?
If freemium leads to lower CAC and better retention, great, invest more. But if it’s dragging your business underwater while providing weak conversions, cut the cord.
The litmus test for freemium
Ask yourself:
Would I pay this much per user if this were an ad campaign?
Am I tracking free-to-paid conversion like I track funnel performance elsewhere?
Do I know the total cost of running the free tier?
Am I treating this as a revenue-driving channel or just doing it because “everyone else does”?
If you’re not asking those questions, you’re not being honest about what freemium costs you.
Remember, freemium isn’t evil. It works if you’re clear about what it’s for. But if you think it’s a free way to learn, grow, and build, chances are, it’s silently sabotaging your business. Reframe it.
And decide whether freemium is actually serving your startup, or just eating it alive.
You can read the original article here - Reframing “Freemium” by charging the marketing department.
Some interesting articles on the same topic:
How to create a profitable Freemium startup (spreadsheet model included).
Making freemium work.
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📃 QUICK DIVES
1. Common AI product issues and how to fix them.
Almost every software product today is racing to add AI. But across all these domains, one pattern keeps showing up: the AI implementations are eerily similar. Most default to, “let’s just add a chat panel.”
And so, the same problems show up again and again.
Luke Wroblewski recently shared some insights on these recurring issues. If you're building AI products or integrating LLMs into existing apps, here are three patterns worth reflecting on, with design ideas that can help.
Capability Awareness: What can this AI do?
One of the biggest issues with AI chat interfaces is the “invisible interface” problem.
People don’t know what they can ask. And even when they do, they don’t know how best to phrase it. Just like talented people, even capable AI has strengths and weaknesses, but those aren’t always obvious.
Without affordances or guideposts, users are lost.
What helps:
Let AI rewrite the prompt. Make the model auto-optimise people’s vague input into better prompts.
Suggested Questions. Highlight example prompts in the UI so users understand the boundaries and possibilities.
Context Awareness: What did the AI use to respond?
If capability is about what AI can do, context is about how it did it.
Was it using a search? A user profile? Your chat history? External tools? Some part of its training data?
With so many possible sources and context window limits, users don’t know whether they can trust the output or if hallucinations are sneaking in.
What helps:
Contextual Retrieval. Add background info to prompts automatically, to reduce vagueness.
Streaming Citations. Show what articles, PDFs, or data the AI pulled from, in real time.
Task-Specific Context Windows. Use background agents for different tasks so they each stay focused.
Walls of Text: Why does everything feel like a giant scroll?
AI chat has over-indexed on text. And while words are powerful, they’re not always the best way to absorb information.
Most interfaces still spit out long paragraphs in a linear thread. People find it hard to scan, extract, or recall what matters.
What helps:
Structured Outputs. Let users ask for slides, tables, spreadsheets, or visuals.
Better UI for Long Responses. Break responses into expandable sections or summaries.
Inline Streaming Images. Don’t just stream text, stream charts, images, or annotated screenshots too.
This is not a complete list, and we’re still early in the AI product cycle. But these three themes, capability, context, and communication, are cropping up everywhere.
So if you're building AI into your product, don’t just bolt on a chat window. Step back and ask:
What should users know about what AI can do here?
How will they know where the answer came from?
And can we give them more than just a wall of text?
The bar is rising fast. Early builders who solve these problems elegantly will shape the next generation of usable, trusted AI tools.
2. How to prove your startup idea is worth backing.
Most founders care deeply about the problem they’re solving. But here’s the truth:
Investors don’t always know your problem exists.
That’s why one of your most important jobs in a pitch is to prove, not just explain, that the problem is real.
You can’t just say:
“Marketers struggle with this.”
“Parents don’t want to carry car seats.”
That’s not enough. You need proof. You need to show them:
This problem is real
It affects a large group of people
Those people are looking for a solution
Your product is the answer
Let’s break this down.
1. What kind of proof works?
Customer discovery is your secret weapon. It’s not about guessing what people want, it’s about asking them.
Here’s the kind of proof that works with investors:
Real conversations with real people. Tell investors how many people you spoke to. Be specific. For example:
“We talked to 1,000 families with kids under 5. 90% said they’d use this service weekly.”
Clear data, not vague statements.
Don’t say, “Most people liked it.” Say, “87% said they would pay $10/month for this.”
Specific insights you’ve learned.
Maybe you discovered that grandparents are an overlooked group. Or that people care more about trust than speed. These insights show you understand the problem better than anyone else.
Quotes from your users.
Use 1–2 short quotes that capture the pain.
“I avoid taking cabs because I don’t want to carry a car seat.”
Real words stick better than summaries.
Patterns you noticed.
Did you hear the same issue across different income levels? From people in different cities? Investors love it when you show that this isn’t just your circle, it’s a wide pain point.
2. How to present that proof in an investor call.
You only get a few minutes to make your point. Here’s how to make it land:
Start with the story, not the data. Bring the problem to life first. Share a quick, real example.
“One mom we spoke to said she avoids rideshares completely because of the car seat hassle. We heard this again and again.”
Then drop the numbers. Follow the story with the proof.
“We interviewed 1,000 parents. 90% said they’d use our solution weekly.”
Keep it short and sharp. Don’t list everything you learned. Just highlight the 2–3 biggest takeaways and say you’ve got more in the appendix or memo.
Let the insight guide the product. Connect what you learned to what you built.
“After dozens of conversations, we realized this wasn’t just about transportation, it was about trust and reliability. That’s why we focused on background-checked drivers with childcare experience.”
Show you’re a missionary founder. Don’t just dump data. Speak with care. Show that you want to solve this, not just because it’s a big market, but because it matters to you.
3. What investors hear when you bring proof.
When you walk in with this kind of evidence, here’s what you’re telling investors:
I’ve done the work
I know who my users are
I’m building something people already want
I’m not guessing, I’ve validated it
I’m committed to the long haul
That’s what separates a pitch deck from a real business.
You’re not just trying to prove your idea is smart. You’re showing that you understand the problem inside and out. You’ve listened. You’ve learned. You’ve built something that makes sense, and you’ve got the numbers and insight to prove it.
That’s what gets investors to lean in.
Also, that’s not enough; investors will ask some smart and weird questions that many first-time founders get confused about, so we created an all-in-one guide to help you answer questions smartly. You can download our guide here.
3. How to choose the right North Star metrics for your startup?
“Your North Star Metric is your strategy, and your strategy is your North Star Metric.” Many first-time founders make mistakes in choosing the right metrics for their startups. Even some don’t have a clear idea about it and have picked the wrong North Star metrics. Remember, with your north star metrics, investors predict how you are thinking about the future. So it’s important to choose the right one.
It’s a single metric which can align the entire company around an overarching mission whilst simultaneously reflecting the value being delivered to customers.
Look at the mission statements of two of the world’s best-known companies, Google and LinkedIn:
Google – “Google’s mission is to organise the world’s information and make it universally accessible and useful”
LinkedIn – “Connect the world’s professionals to make them more productive and successful.”
The goal of the North Star Metric is to quantify this mission and express it in a way that can be measured and tracked. It is often used as a guiding light for the entire company to rally around – a sort of special key performance indicator (KPI).
What is a good North Star Metric?
Which metric, if it were to increase today would accelerate business flywheel? - Lenny Rachitsky
Here are some best practices for deciding on a good North Star metric:
Is it measurable?
It is simple, memorable and easily understood?
Is it a leading (not lagging) indicator of success?
Does it help your customers reach their end goal?
Does it apply to all customers, and does it add value to all customers?
What is a bad North Star Metric?
There are also some poor metrics to use as a north star, the most common of which is revenue. Below are some characteristics you should aim to avoid when deciding on a north star metric:
Lagging indicators that show past performance, not future potential.
Metrics are hard for employees to influence or understand their impact on.
Metrics do not reflect customer value delivery, like vanity metrics.
Generic metrics that don't represent your unique business strategy.
Your north star should be unique to your business (or at the least your business niche) and should be an indicator of the problem you solve for customers, and the value provided to them.
Below we have provided examples of North Star metrics from many well-known brands. Notice how they are not all perfect, but generally, all reflect value delivered to the market.
So while choosing metrics for your startup, remember - “North Star Metrics - if this metric grows, all other metrics will be growing too. Such as engagement, acquisition, and sales.” - Adam Wright.
THIS WEEK’S NEWS RECAP
🗞️ Major News In Tech, VC, & Startup Funding
New In VC
Wisdom Ventures, a San Francisco, CA-based early-stage VC firm investing in tech-enabled wellbeing, launched its Fund II with its first close of $16m. (Read)
Sorenson Capital, a venture capital firm with offices in Palo Alto and Salt Lake City, announced the launch of its third early-stage fund, Ventures III, with $150M in capital commitments. (Read)
Lakestar, a leading European VC firm, is reportedly raising $300 million for a new defence-focused fund. (Read)
Major Tech Updates
CoreWeave CEO Michael Intrator’s net worth has soared to $10B post-IPO, despite the company raising only $1.5B—far below its $4B target. (Read)
Trump Mobile scrubbed its website of the prominent "MADE IN THE USA" banner for its T1 Phone 8002, a key original selling point. (Read)
Google launched Doppl, an experimental app that lets users visualise how clothes would look on their own body using a full-body photo and outfit images. (Read)
After Meta hired eight OpenAI researchers, Chief Research Officer Mark Chen told staff the leadership is “recalibrating” compensation and finding new ways to retain talent. (Read)
Meta has hired four additional researchers from OpenAI, following earlier poaching of high-profile talent, including Trapit Bansal. (Read)
Donald Trump said a group of “very wealthy people” is set to buy TikTok but didn’t reveal their identities. (Read)
New Startup Deals
Foresight, a New York City-based software company on a mission to connect the private market through data, closed a $5.5m seed funding round. (Read)
Clearspeed, a San Diego, CA-based voice-based risk assessment technology company, raised $60M in Series D funding. (Read)
Gensmo, a NYC-based AI-native company developing an AI-powered fashion agent service, raised $60M+ in Angel funding. (Read)
Wispr, a San Francisco, CA-based provider of an AI voice app for iOS and desktop, raised $30M in Series A funding. (Read)
Niural, a NYC-based AI-native global PEO (Professional Employment Organisation) for fast-growing companies, raised $31m in Series A funding. (Read)
Mandolin, a San Francisco, CA-based AI automation platform for speciality drug access, raised $40m in funding. (Read)
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