The Gen AI PLG Renaissance
Gen AI winners are landing with PLG, then compounding with enduring moats.
Introduction
I recently came across a set of mind-boggling stats:
In a recent fireside chat, OpenAI shared ChatGPT reached 700M WAUs, with ~4M enterprise seats.
Anthropic CFO Krishna Rao shared that Claude Code has reached $500M of ARR. This is a product that went GA in May 2025.
At another event, OpenEvidence shared that they have reached almost 50% US doctor penetration, just two years after their launch. This is unheard of speed in healthcare.
There is still plenty to debate in gen AI, but one thing is clear: Gen AI is driving a renaissance in product led growth (PLG). This piece unpacks why gen AI unlocks a new age of bottoms-up adoption, and where PLG native companies could go from here.
Background: Why SaaS PLG Stalled
For our purposes, I am defining PLG as an enterprise GTM strategy where the product itself is the primary driver of customer adoption, not marketing or sales. Think of Dropbox – you get an invite from a colleague to check out a file they’ve saved in Dropbox. Accessing this file was the easiest process you’ve ever experienced. You like it so much that you then started to save and share your files there too. At some point, now your entire company is using Dropbox. That is PLG in a nutshell.
The early innings of PLG for Slack, Zoom, Dropbox, thrived when the enterprise software landscape was sparser. But by the late 2010s / early 2020s software PLG had hit a plateau. Customers now had hundreds of different software solutions and the market was now predominantly brownfield. Per Okta, the average number of SaaS apps per company reached 101 in 2025. New products couldn’t just rely on “it’s a good product” to win – they had to show they were incrementally better than incumbents, and this was better done via account execs.
At the same time, customers’ compliance teams started to ask more questions about integrations and security. This meant more stakeholders got involved in the selling process, putting in more barriers to adoption. This forced vendors to “professionalize” with fulsome enterprise sales teams. It turned out that CIOs still like to be sold software by people wearing Patagonia vests (not just by their employees forwarding them memes from Slack).
Furthermore, many traditional software products are just not good fits for PLG. A Salesforce product isn’t particularly useful unless it is already integrated and configured the right way, with full-to-wall deployment. Straight out of the box, the product’s functionality depends on the success of the implementation. But this is different for many gen AI companies.
What Makes PLG in Gen AI Possible
Gen AI resets the PLG game because it puts companies and users in “experiment mode”:
Gen AI products deliver step-change better experiences. Overworked doctors and deadline constrained developers can use new AI products to save hours per day. Very few new SaaS tools clear this bar.
Many gen AI products are free to start. More performance sits behind paid tiers, but as far as making it easy for new customers to try, nothing beats free.
Many gen AI products don’t require systems access to demonstrate value. Users can “bring their own data” – just upload a file or type a prompt and let the LLM figure out how to make use of it. This means speed to the aha moment is minutes, not days.
A 10x better experience, plus free product, plus easy onboarding? The virality of these products is inevitable. So, as CEOs put together their AI strategies and companies reach all-time highs for live pilots, the purse strings are open. Developers are testing Claude Code vs Cursor vs Windsurf. Knowledge workers are testing multiple search tools. And once enough employees adopt a product bottoms-up, the C-suite is incentivized to standardize on an enterprise plan.
Examples of Successful Gen AI PLG
Gen AI chatbot application ChatGPT from OpenAI has had undeniable PLG successes. But let’s walk through two other examples in the creative and healthcare industries that are less talked about.
ElevenLabs: ElevenLabs is the leading platform for voice models, and they have nailed the PLG playbook. When they first launched in 2023, their ultra-realistic voices had materially superior quality vs what customers had access to at the time from incumbents like Amazon. Today, ElevenLabs continues to be a top text-to-speech (TTS) model provider, with top pricing vs competitors.
Additionally, users can get started for free – you could just paste in a script and press a single button to generate audio. the Free plan gives you 10,000 credits per month, or about 10 minutes of high-quality TTS, with API access out of the box. And the outputs become inherently viral – clips with ElevenLabs voices travel on YouTube, TikTok, and other social platforms, which means the product’s artifacts do the marketing.
Over time, ElevenLabs has successfully expanded their PLG and virality to true enterprise sales. They built creative editing applications on top of their models to keep users on their platform once they come. They added enterprise grade controls, watermarking capabilities, and other product features. They expanded pricing to include multiple tiers to accommodate customers of different scales. As one of the early winners in voice AI apps, they have now passed $200M of ARR, with almost 50/50 revenue split between self-serve and enterprise segments. In their recent September 2025 employee tender, the company was valued at $6.6B.
OpenEvidence: OpenEvidence is a pinnacle example of PLG in the healthcare industry. A physician downloads the OpenEvidence app, asks a question during clinic or prep, and gets cited answers from reputable medical literature. This is a 10x better way for doctors to conduct search because OpenEvidence’s post-training efforts and proprietary data sources make their responses usable to doctors in a way that generic AI search does not. Especially as the half-life of medical knowledge increasingly shortens, OpenEvidence helps physicians get to the right diagnosis and treatment – saving patients and the healthcare system time, money, and stress.
Additionally, the core chat product is completely free to use, with no paid option. Instead, OpenEvidence monetizes through ads and new products. Furthermore, there is no complex integration needed with EHR systems – doctors can just use their personal phones. All of these factors have led to rapid physician uptake. Although the product only launched in 2023, OpenEvidence has already reached nearly 50% of doctors at more than 10,000 locations in the US. The strong engagement on OpenEvidence puts the company in a highly strategic position with multiple paths to monetization and additional product development over time. In their most recent July 2025 funding round, the company was valued at $3.5B.
What’s Next?
While PLG is a fantastic GTM wedge, it is rarely enough on its own. Products that are very easy to adopt are oftentimes also very easy to cut. So how can PLG companies build durability? There is no secret sauce here – durability still comes down to the same 7 Powers, but I want to highlight a few points that are particularly relevant for PLG winners:
Lean into your customer love – Customers use you not because they have to, but because they want to. Your North Star metrics should include time spent per user, free à paid conversion rates, and other engagement metrics.
Cross-sell additional products – Find ways to own more workflows. The more time a customer spends in your environment, the more embedded you can become in their day-to-day. Even better if you can cross sell products to different departments and personas in an organization over time. Your North Star metrics should include revenue retention rates.
Build out the enterprise level buy in – Yes, even if you started with PLG, I believe it is still important to build out enterprise sales capabilities over time. As your product scope expands to multiple departments and personas, your strategic value to customers should grow, so C-suite decision makers will become increasingly involved. Building out enterprise capabilities will help you build C-suite buy in from the start, and help you land larger, multi-year deals. Your North Star metrics should include landing ACV size and sales timeline length.
Conclusion
Gen AI has reset the bar: when a product saves you hours on day one and is free, the virality is imminent. This puts companies and users into experiment mode, and kickstarts a new PLG wave. But PLG is just the tip of the iceberg. The best companies use this bottom-up love as an entry point to eventually building long-term moats. If you are a builder looking to chat more about PLG for your product, please reach out to selina@altimeter.com.
Sources: Artificial Analysis, Okta Businesses at Work, Openevidence.com, Elevenlabs.io, TechCrunch
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Selina, it was insightful to read about eleven labs' revenue mix and enterprise adoption. Which other AI products have been successful with PLG --> enterprise sales?