What are the odds of making it?

Executive summary

The startup world is borderline obsessed with celebrating outlier companies. These are the ones that break growth milestones, are run by charismatic founders, and aren’t shy about self-promotion (they might prefer to call it building-in-public). To be fair, I’ve featured many of these stories in this newsletter, too.

What gets lost in the discussion: it’s extremely difficult to make it as a new startup. You don’t have enough resources. Your buyers aren’t paying attention. Your product isn’t as polished as you’d like. And a new competitor comes out of the woodwork each week.

I wanted to shine a light on exactly how difficult this is. For help I teamed up with my friends at ChartMogul, the CRM and SaaS metrics platform where I’m now an Analyst-in-Residence. ChartMogul let me explore their incredible dataset covering 6,525 software companies with historical records going back more than a decade.

Keep reading for brand new data about your odds of making it to $1 million ARR and beyond, how long it really takes to hit your first million, and whether you need to move to the Bay Area to strike gold.

The odds of making it to $1M ARR and beyond

It’s easier to get into Harvard or Stanford than it is to reach $25 million ARR within 10 years. Let that sink in.

The first thing I investigated was what percentage of software startups ever reach ARR milestones like $1 million, $5 million, and $25 million. This is trickier to measure than it sounds given factors like survivorship bias. I made a few simplifying assumptions including starting the clock at initial monetization and using a 10 year measurement window. Since most startups haven’t been around for 10 years, I ran the numbers on a cohort-basis to confirm the slope of the survivor curve across the full historical dataset of 6,525 companies.

What I found: almost half of software startups do eventually make it to $1 million ARR after they’ve started monetizing – if they’re willing to stick around 10 years.

This number is higher than I would have predicted. I suspect it could be influenced by some selection bias; companies interested in subscription metrics might be more likely to be serious businesses rather than hobby projects. And this is showing companies that started monetizing in 2016 or earlier when, frankly, it was much more challenging to build and launch a SaaS product. Later in the report we’ll unpack how these figures look for more recent cohorts in their early years of monetization.

Even still, the odds of graduating beyond $1 million ARR are quite stark. One-in-ten makes it to $10 million ARR. And just one out of every fifty startups make it to $25 million ARR within 10 years. The odds of doing this are lower than getting accepted into Harvard or Stanford (3.6% acceptance rate).

If you’ve done it, I hope you take a moment to celebrate. 97.7% of your peers didn’t get this far.

How long it really takes to hit your first million

When you listen to VCs, you get the impression that revenue growth happens at warp speed. The classic “triple-triple-double-double-double” (T2D3) path to $100 million in five years is no longer good enough for some VCs when they see the likes of Lovable, Cursor and Wiz doing it in two years or less.

What I found: for startups that reach $1 million ARR, it usually takes between two and five years from initial monetization. A mere 3.3% of startups reach $1 million in under a year. 13.4% reach $1 million in under 3 years. And 25.1% reach that figure within 5 years. You can map yourself onto the curve below to find the figures that are most relevant to you.

I think this is healthy! It takes real time to find product-market fit (PMF) and move from founder-led sales to repeatable growth. Premature scaling can kill an otherwise great business. Many of the best companies go slow to go fast.

It’s worth mentioning that today’s most-celebrated unicorn startups didn’t reach $1 million overnight either. They patiently iterated on both product and distribution, then went all-in once they knew it worked.

• Lovable spent 18 months building before their breakthrough success. Prior iterations of the product, then called GPT Engineer, fizzled out after initial launch.
• Clay, the sales intelligence platform, grinded for six years before seeing revenue traction. Growth eventually exploded over the past few years.
• StackBlitz, the maker of vibe-coding app Bolt.new, spent seven years clawing their way to $700k in ARR. Then their new product Bolt.new exploded to $4 million ARR in its first four weeks.

It’s easier to build software, it’s harder to stand out

In the last ten years we’ve turned go-to-market into a science. We’ve invented dozens of three letter acronyms (TLAs) from AEO to PLG to UBP. What we haven’t done is get materially better at breaking through.

I split software startups into cohorts based on the year they started monetizing. Then I compared the likelihood of reaching $1 million ARR for each of these cohorts.

What I found: the startups that launched between 2016-2019 were consistently more likely to get to $1 million ARR within three years compared to startups born later on. The differences aren’t massive – a 15% success rate compared to 12% – but they’re noticeable. Many from the 2016-2019 batch likely benefited from the zero interest rate period (ZIRP) and the COVID-fueled software boom.

2016 in particular was the single best vintage for launching a startup. That year had top quartile success rates across the board. This vintage includes many of the OG product-led growth (PLG) pioneers, too, who introduced new ways of buying to legacy product categories. Notion, Figma, and Loom all launched in 2016.

More recent cohorts, and in particular software companies founded in 2021, haven’t fared as well. The 2021 cohort has the lowest likelihood of reaching $1 million ARR within three years (10.1%). This batch has the bad luck of being late to the ZIRP boom and in the prime of the 2022-2023 funding slowdown. Most were too early to ride the AI wave.

The silver lining: the 2023 vintage is performing exceptionally well out of the gate. They’re 50% more likely to reach $1 million ARR within six months of launch. This batch was still early to the AI party; the founders had been thinking about how to build AI products before doing so became mainstream. They were in the right place at the right time with the right product.

AI-native startups are in a league of their own

As a rule of thumb, newer cohorts of software companies haven’t gotten better at finding traction. But there is a (big) exception to this rule. You might be able to guess what it is.

The ChartMogul team used AI to group the 6,000+ companies into product categories. These include categories like PLG (have a self-serve offering on their website), vertical SaaS (software designed for a specific industry or niche), and enterprise SaaS (average ACV of $10,000+).

I then looked at how the product category impacted a startup’s chances of being an outlier, which I defined as reaching $1 million ARR within six months or $10 million ARR within 12 months. (The categories weren’t mutually exclusive; a company could fall into multiple buckets.)

What I found: AI-native startups are 3x more likely to reach $1 million ARR in 6 months. And they are 8x more likely to reach $10 million ARR in 12 months.

To be clear, these milestones are still exceedingly rare. Fewer than 1% of AI-native startups reach $10 million ARR within 12 months of first monetization. But this is occurring at dramatically higher rates than we’ve seen historically.

No other product category even remotely compared to being AI-native:

• Enterprise SaaS companies are 40% more likely to reach $1 million ARR in 6 months, although they’re just average at reaching $10 million ARR in 12 months.
• Contrary to what folks might expect, PLG and consumer SaaS are both less likely to explode out of the gate; many of these companies have a slower ramp as they prioritize user acquisition before monetization.
• Vertical SaaS has fewer outliers, too. I suspect this reflects the challenge of selling to buyers who aren’t early adopters of tech.

I’ll caveat that exploding out of the gate doesn’t necessarily predict longevity. Being quick to acquire customers can coincide with being quick to churn those customers to another product.

You don’t need to be in the Bay Area (but it helps)

Conventional wisdom suggests you’ve got to move to the Bay Area if you want to make it big. It turns out this isn’t necessarily true, although being in the Bay Area certainly doesn’t hurt.

I analyzed the dataset based on a company’s current headquarters location, then grouped companies into the ten biggest regional hubs represented by ChartMogul customers. These groups include large metro areas (like the Bay Area and NYC), countries (like France, Canada, UK) and in some cases regions (like Central and Eastern Europe, Scandinavia).

What I found: Bay Area startups are indeed more likely to break out. But the differences are not as profound as you might expect given all the advantages (and added costs) of being in the Bay Area. Bay Area companies are:

• 70% more likely to reach $1 million ARR within 3 years.
• 150% more likely to reach $5 million ARR within four years.
• 40% more likely to reach $10 million ARR within five years.

France and Canada are surprisingly good at producing software companies that reach $1 million ARR within three years (although Canada in particular rarely produces companies that reach $10 million within five years).

New York City and the rest of the US produce companies with the most longevity. Companies from these locations beat out the Bay Area in getting to $10 million ARR within five years. Perhaps those building outside the Bay Area have more staying power; they persevere when their peers might pivot, shut down, or pursue an early exit.

What it takes to become an outlier

If you’ve made it past $1 million ARR, well done. You’ve beat the odds.

So what is it that propels these startups and not others – besides, of course, being AI-native and headquartered in San Francisco? I’ll be exploring that question further over the coming weeks. Subscribe to Growth Unhinged and connect with ChartMogul to get notified when these drop.

Methodology and glossary

Data sources and analysis framework: To create this report, we analyzed anonymized and aggregated data from ChartMogul.

Data sources: ChartMogul MRR movements, enriched account data with AI segmentation, and geographic/industry classifications. Excludes sandbox accounts.

ARR milestone analysis: Examines how quickly companies reach key ARR milestones ($1M, $5M, $10M, $15M, $20M, $25M) across time horizons (1-120 months). Companies are eligible only if sufficient time has passed since their first revenue date.

Fastest decile analysis: Focuses on the top 10% fastest companies to reach each milestone, analyzing their retention and growth characteristics during the ramp period.

Retention metrics: Gross Revenue Retention (GRR) and Net Revenue Retention (NRR) calculated during ramp windows.

Growth metrics: Average Revenue Per Account (ARPA), ARPA growth, and revenue composition (new business, expansion, reactivation).

Revenue movements: New Business, Expansion, Contraction, Churn, and Reactivation MRR categories.

Statistical approach: Cohort analysis by year, geography, and business model; monthly aggregation with forward-filled data; percentile-based performance benchmarking.