AI-Native Software and Software Expansion

Software Expansion

From Alfred Lin, Sequoia Capital partner and co-steward, in an interview with Bloomberg last week:

“Let’s not forget AI is a lot of software,” Lin said in a Bloomberg Television interview on Wednesday. “Legacy software companies of yesteryear like Oracle still exist today.”

Lin added that he believes AI ultimately will improve most companies. “The impacts of AI are real,” he said. “They’re going to allow us to do a lot more than we used to be able to do.”

Lin’s point is hard to argue with. AI clearly allows software companies to “do a lot more than [they] used to be able to do,” whether that’s automating work that once required teams of operators or compressing end-to-end workflows into a single system. If you treat AI-native products as part of the broader software market, it’s easy to see why this isn’t just a feature cycle. AI is actively expanding what software can address, and, in doing so, broadening software’s total addressable market.

That expansion shows up in three main ways: AI can solve previously intractable pain points, it can penetrate strategic positions that software has historically struggled to reach, and it can actually replace human labor TAM. Those three modes of expansion are perhaps onto three AI-native companies, namely Clay, OpenEvidence, and Sierra.

Solving Previously Intractable Pain Points: Clay

Clay is an AI-native GTM platform that was most recently valued at $5 billion in an employee tender offer. If Clay is doing ~$100M in ARR (a milestone it hit in December 2025), that implies a 50x ARR multiple, underpinned by 250% revenue growth and a staggering enterprise net retention rate north of 200%. Clay has also built a deep roster of credible early customers, including OpenAI, Anthropic, and Rippling.

Clay fundamentally operates as an orchestration layer on top of its data-provider integrations, namely tools like Wiza, Apollo.io, and RocketReach. When a user searches for a lead, Clay queries multiple providers in parallel to reliably surface the right contact. If one source comes up empty, another often has the missing record, so coverage and accuracy can improve simply by aggregating across the full set of providers Clay can tap.

However, as powerful as that position is on its own, Clay’s real value proposition is what AI enables on top of it. With AI, Clay can go beyond pulling records from integrations. Clay can automatically search the web, synthesize context across sources, enrich each lead with the details that actually matter, and then generate outreach tailored to the specific person and company at a level of personalization that would be impossible to do manually at scale. For example, a rep can feed Clay a list of 500 target accounts. Clay can then automatically find the right persona from each account, pull and verify contact details across providers, scrape the open web for relevant signals, and draft a tailored first-touch email that connects to a targeted pitch.

This automation of personalization is what ultimately underwrites Clay’s rapid growth and the staggering valuation multiples that come with it. Clay attacks a core GTM bottleneck: the traditional tradeoff between high-effort, customized outreach and high-volume, generic outreach. By making tailored prospecting scalable, Clay lets teams run far more efficient GTM motions than previously possible. Importantly, GTM efficiency ties directly to a company’s upside. Better targeting and messaging translate into a better pipeline and more revenue, which means customers are willing to pay for a tool that meaningfully expands that ceiling. And that jump in efficiency is only possible with AI.

Unlocking New Revenue Pools: OpenEvidence

Another way AI expands software’s TAM is by unlocking previously untapped revenue pools through embedding itself in important strategic positions that legacy software couldn’t reach effectively. There is arguably no better example of this than OpenEvidence. Positioned as an AI copilot for clinicians at the point of care, OpenEvidence effectively functions like a “ChatGPT for doctors,” optimized for clinical questions where accuracy and sourcing matter. OpenEvidence’s most recent funding round was a $250M Series D in January 2026 at a $12B valuation, co-led by Thrive Capital and DST Global. This doubles the $6B valuation it set in its $200M Series C in October 2025, led by Google Ventures.

That meteoric rise is driven by many factors. For example, OpenEvidence has a proprietary data advantage. Unlike general-purpose copilots trained on the open internet, OpenEvidence is trained on licensed, peer-reviewed medical literature. OpenEvidence has inked exclusive deals with publishers and clinical evidence providers such as the JAMA network, the New England Journal of Medicine, and Wiley to access trusted research. As a result, when a doctor uses OpenEvidence, they get a level of confidence that cannot be achieved with a general-purpose copilot. This, paired with OpenEvidence’s first-mover advantage at the point of care, that data moat has helped it build a meaningful lead in clinical Q&A, a lead that becomes harder to close as usage, feedback loops, and workflow lock-in compound over time.

However, OpenEvidence’s biggest value add is commercial. OpenEvidence has opened a new on-ramp into healthcare’s largest marketing spend, pharma. By sitting inside a clinician’s workflow at the point of care, OpenEvidence creates premium real estate for pharmaceutical companies to reach doctors in context, adjacent to the exact clinical questions and treatment decisions that matter.

Pharma has long spent heavily to influence prescribing, but most channels are either indirect, such as advertising through patient-facing media, or expensive and operationally intensive, such as advertising through field reps, conferences, and legacy publications. OpenEvidence offers targeted, measurable exposure to clinicians at the moment of decision, with tighter feedback loops and clearer attribution. In that sense, AI isn’t just improving the product; it’s enabling a strategically advantaged distribution surface that unlocks a revenue pool traditional software couldn’t credibly access.

It’s also worth noting that OpenEvidence only unlocks this revenue pool because it first solves a previously intractable pain point. Before AI, there was no scalable way for physicians to retrieve and synthesize the right medical evidence quickly enough to be useful in the exam room. OpenEvidence uses AI to make that kind of point-of-care clinical lookup fast and practical, and the advertising surface is downstream of that utility. In other words, the ads work because the product first earned its place at the moment decisions get made, and OpenEvidence was only able to earn that position due to AI.

Replacing Human Labor: Sierra

It is important to note that for both Clay and OpenEvidence, AI is augmenting human labor. Clay makes GTM teams much more efficient, and OpenEvidence helps clinicians become faster and more accurate at the point of care. But the third, and arguably most TAM-expansive, mode is when AI replaces human labor wholesale. A good example of this is Sierra.

Sierra is an AI-native customer experience agent platform that was last valued at $10B after raising $350M in September 2025. If you benchmark that against Sierra’s reported $100M ARR milestone, the headline valuation implies an eye-popping ~100x ARR multiple. Sierra has already signed numerous blue-chip customers, including Discord, Ramp, Rivian, SoFi, and SiriusXM.

Sierra’s core value proposition is simple: it can replace customer service labor end-to-end, not just assist it. Sierra’s AgentOS platform can handle the customer interaction, execute the downstream action, and then write back to the systems of record. For example, if a customer initiates a conversation asking for a refund, the agent can resolve the conversation, issue the refund, and log the outcome in the relevant back-office ERP system, all without a human in the loop. And that no human in the loop promise shows up in Sierra’s monetization. Sierra prices on outcomes, meaning Sierra only gets paid when the interaction is completed without a human from start to finish.

This is where the TAM expansion becomes obvious. With AI, Sierra isn’t just selling a better CX software; it’s selling into the entire customer service labor spend. And that’s a big reason investors are comfortable backing multiple top players in the category. You can see the same investor conviction in other agentic CRM solutions like Parloa (which raised a $350M Series D at a $3B valuation) and Decagon (which announced a $250M raise at a $4.5B valuation). The prize for AI-native CRM solutions is not a marginal slice of SaaS budget, but a direct claim on a massive labor pool.

AI Limitations

However, even if AI can expand software’s TAM in meaningful ways, it still has real limitations, especially around reliability. A common thread across Clay, OpenEvidence, and Sierra is that each operates in a domain with a built-in tolerance for failure. With Clay, the downside of an imperfect output is usually limited. If the agent misses a lead or gets a detail wrong, the worst case is a lost outreach attempt. It’s frustrating, but rarely catastrophic. OpenEvidence has a different safety valve. A trained clinician remains the ultimate decision-maker. If the model is wrong, a knowledgeable doctor can often catch it. And if they can’t immediately validate the answer, the doctor can still flag uncertainty rather than treat the output as ground truth. Sierra, meanwhile, can handle failure by simply escalating edge cases. If a request is too complex, sensitive, or ambiguous, the system can hand off to a human agent before anything irreversible happens. In other words, these are AI products designed around bounded risk where errors are either non-fatal, human-auditable, or recoverable via escalation.

But there are domains where the downside of failure is catastrophic. Take Jeppesen ForeFlight, an aviation software company that Thoma Bravo spun out of Boeing in a $10.55 billion deal. Jeppesen ForeFlight sits directly on the critical path of flight operations, from flight planning and weather to aeronautical charts and navigation data. In that environment, an error is much larger than a slightly worse conversion rate or an easily corrected suggestion. It can translate into a wrong route, a missed constraint, or a compliance and safety incident with real consequences.

Fundamentally, today’s AI is a poor match for domains like aviation, where the tolerance for error is effectively zero. Because AI systems are probabilistic rather than deterministic, they can produce rare but unpredictable failures, and in safety-critical environments, “rare” is still unacceptable. The problem is compounded by the fact that a human-in-the-loop often can’t reliably catch or correct an AI’s mistake in real time, especially under operational time pressure. In those settings, AI may still be useful at the margins, but broad autonomy is unlikely if reliability, verifiability, and strict guardrails matter more than raw capability.

Overall, AI’s most important macro effect is that it makes the software market bigger. AI is stretching the boundary of what can be productized, capturing new distribution surfaces that weren’t economically reachable before, and converting entire labor pools into addressable “software spend.” However, the tolerance for error in a workflow is still a constraint that will ultimately shape where AI wins. In high-variance, recoverable workflows, probabilistic systems can compound into real leverage; in zero-failure domains, they’ll remain tightly bounded behind verification, controls, and humans.

Ultimately, the AI-winners are the companies that can earn the right strategic position in a workflow by providing new capabilities. But no matter who wins, there’s no denying that the new, AI-enabled capabilities are expanding the overall pie that is the software market.