Will AI agents eat SaaS?

It's eat or be eaten in the age of AI agents and time for SaaS companies to place their big bets.

Note: This is the first post in a series on how AI agents will transform SaaS products

If you missed it over the holidays, CEOs Satya Nadella and Marc Benioff had a fun “exchange.” Satya, on a podcast, postulated that the business logic for most applications would just be captured by an AI agent leveraging multi-repo CRUD databases - collapsing the value of SaaS companies. Marc clapped back on a different pod, effectively calling this a “Her” fantasy. He followed up with vague but passionate comments on how successful their recent Agentforce launch had already been in comparison to Microsoft’s Copilot.

As LLMs keep getting more reliable (actually following instructions is key) and better at doing math, mimicing reasoning, navigating UIs and writing code - true agentic systems will become production grade for many use cases. Just over the past 24 hours, we saw both Deepseek-R1 and Gemini 2.0 Flash top the charts with new, breakthrough scores just weeks after releasing previous versions of their SoTA models. AI researchers theorize that this progress is coming from model distillation and a new recursive loop that’s finally making scalable RL (reinforcement learning) effective at improving model capability and reliability. We’ve already come a long long way from agentic projects like babyagi and Auto-GPT (remember those?), released as recently as Mar 2023. I’ll save debates on the definition of agents and AGI for future posts. (Something tells me it might come up ;))

There’s no doubt today that AI agents will go mainstream and expand technology markets. The unanswered question is - who will capture the most value? To date, it has been the hardware layer, led by Nvidia. The hyperscaler platforms together invested over $220B in capex in 2024, which is all revenue for the semiconductor supply chain. Yesterday saw the announcement of project Stargate with $500B of further capex planned over the next few years just for OpenAI. On the application side (albeit vertically integrated), the breakout success is their prosumer SaaS business, which brought in ~$3B in traditional, seat-based revenue in 2024. (Stop saying but what about ROIC please, “AGI is coming”.. in 1-10,000 days.)

All CEOs talk their book. So far, Microsoft has benefited from its AI bets by adding $10B of AI inference revenue to Azure in 2024. It would love to decrease its dependence on Nvidia GPUs and further expand its TAM to include business applications.

This is the why the AI agent turf will see the biggest fight in software for the foreseeable future. What will determine the outcomes? How does SaaS win this? Let’s dig in. <rubbing hands emoji>

The reports of SaaS death are greatly exaggerated

In 2022, public market SaaS companies saw the most severe correction since the 2008 financial crisis. Since then, the declining growth rates have troughed and the category leaders are back at/near all time high valuations. In the world of startups, many new AI SaaS companies (like Eleven Labs, Clay, Glean, Writer, Perplexity, Heygen, Cursor, etc) are breaking out and have charged towards/past the $100M ARR milestone faster than ever before. Patrick Collison recently shared that AI-native SaaS companies were getting to $2.5M in MRR 5X faster than their antecedents. Many are creating new categories and raising early growth rounds at > 100x ARR.

There is valid reason for the hate punditry around SaaS though. The business model has had a great run over the past past 20 years. With 80-90% gross margins and high cost of switching, successful SaaS companies generate 30% FCFs at scale and often leave customers feeling overcharged. Add to that the proliferation of point solutions and many large companies end up with literally 50-100 SaaS vendors billing them more every year for each function in their company. Doesn’t feel great.

Dude, where’s my moat?

This is a fabulous topic that deserves its own series but let’s start with the obvious, most relevant issue at hand - there is legitimate fear that AI will kill existing SaaS moats, starting with two that were already starting to vanish - integrations (faster to build now) and data (easier to connect to or acquire now).

While the reality of organization-wide vendor change management is still daunting to enterprise buyers, middle market and tech-forward customers feel much less locked in today. This is a great environment for startups. As the cost of prototyping falls, it should take less and less capital to get to early product-market fit. The large legacy teams and businesses are an anchor to shipping product for incumbents. In fact, they have only one true advantage left - enterprise distribution.

However the customers that are recipients of this distribution are hyper-aware of the platform shift and largely bought into AI. This opportunity hence comes with immense downward pressure on pricing as alternatives abound for each solution.

Your SaaS margin is my opportunity

There is a single source of downward pressure on SaaS products - alternatives. This comes in different forms but the two that matter most are tools and pricing models.

1. Alternative tools: no matter how much real value you create, your pricing can only go as high as reasonable alternatives. AI has already given every SaaS vendor more competition and we are just on ... Season 3, Episode 1 of the AGI show. Buckle in.

2. Alternative pricing models: competition creates more choice for buyers and pricing flexibility will be a competitive advantage. Do you charge for seats, for utilization, or for outcomes? Can you let buyers dictate pricing models that work best for them? Buyer flexibility will lead to lower ACVs for the same functionality.

While there are companies charging *more* for add-on AI features, I strongly believe this is temporary. As the technology matures and its value becomes more clear, ZERO growing companies will be offering and pricing AI features as add-ons. It will become a signal that your company isn’t, in fact, AI-native. The decay function on pricing for your existing features is exponential.

So this seems to be a lot of bad news at first. Here’s my take. First, the pricing battle in SaaS is just that - one single battle. You could try hard to “win” it and lose the big war. The war is on for new TAM that has all tech CEOs salivating - digital labor that spawn Services-as-Software products, what we are very imaginatively starting to call SaaS 2.0. (Of course.)

SaaS CEOs must focus on how to build and price digital labor to keep their ACVs.

Second, this is going to force consolidation in SaaS. Between the hundreds of zombie software unicorns that can’t go public and buyers wanting fewer vendors, AI has stepped into a perfect storm. By 2030, we will see more giant platforms in SaaS and many startups will work towards getting acquired as their primary exit strategy.

The end of software

25 years ago, Marc Benioff launched the now infamous “no software” campaign, proclaiming the death of on-premise packaged applications. Of course, nothing dies in tech, everything is reborn to then eat its parent. In 1999, the disruption was about cloud based delivery models for software, now it’s about value models - what software actually does for customers.

The SaaS 1.0 wave was great for systems of record. SoR companies (Salesforce, Servicenow, Intuit, Adobe, Workday, Atlassian..) built massive value by moving a functional source of truth to the cloud. However, implementing and using these tools required a lot of labour. They are also famous for feeling over-engineered and clunky to individual customers because they need to serve all customers with differing requirements. Until 2022, we thought the primary threat to them was startups with better UX - 10X easier to set up and use. More interoperable. That turned out to be entirely wrong in hindsight. The core threat to them doesn’t come from startups with better UX, it comes from companies that will automate the primary work their users do - the AI agents.

Agents could make the underlying system of record an upsold feature.

The addressable market for agentic software might be 10X that for systems of record and workflow tools. (Of course this assumes that the value of human labour represents the prices businesses will pay for infinitely scalable agents.) Incumbents need to either innovate themselves or acquire for velocity and talent. If I’m the CEO of Atlassian, I’m definitely thinking about how to acquire an agentic code generation startup (and not Linear) without breaking the bank. It’s either that or wait for Github to eat your juicy, big project management lunch.

So what does all this mean for SaaS startups? Is it pointless to start an agentic SaaS company right now? Not at all. There are real structural advantages to the SaaS model (and talent) that continue to persist in an agentic world and will drive the next decade of innovation in software. Here’s how.

Homo homini lupus, but software

SaaS 2.0 won’t have the crutches of integrations and data gravity to lean on. But the galaxy brains dissing SaaS are getting one thing completely wrong - UX. I say that AI agents are not going to commoditize UX, they are going to massively raise the bar on customer expectations. Similarly, the domain expertise needed to build agents that properly represent and adhere to a specific-customer’s-unique-function’s-business-logic is going to be a more scarce and valuable resource.

There are more reasons SaaS folks have an edge. Below are five hypotheses on how great SaaS 2.0 products will win the AI agent war. I’ll dive into each of them in a detailed post over the next few weeks.

1. Fluid UX: If you think shipping good UX for point and click SaaS was hard, imagine a world where your users demand to toggle seamlessly between voice, text, APIs and clicks. Or want their UI personalized to their current objectives. Deeply understanding how humans want to interact with tech has been a mainstay of good SaaS tools this past decade. That expertise and obsession with detail will pay dividends. Your BYO agent will not be as fast or good or easy to use as the one shipped by a company that wakes up everyday focused on making it better.

2. 1% Domain Expertise: Here’s the secret. Great SaaS products are built by replicating what the best 1% of your customers are doing for the remaining 99%. If you give the remaining 99% a general purpose agent, they are not going to be able to recreate the business logic they want. Hell, even in the best 1%, no one person knows all the business processes their company exactly needs to follow for any function. So, yes, some of the logic will move to the agentic layer but SaaS companies will still be well placed to distill the right approach and invent good orchestration.

3. Configurable Determinism: The first new frontier SaaS needs to tackle is helping customers decide where they want deterministic outcomes and where they want a high-risk but potentially higher-return output. Most big companies today are starting risk OFF but that will change as we all get used to working with a new kind of software.

4. Diverse Data Structures: While LLMs are great with unstructured data, reliable business applications need both. Historically SaaS companies have been good at working with structured datasets but will now need to stretch in new directions, including incorporating freshness and relevance as considerations. Either way, this introduces complexity they can solve for on top of data platforms and thus monetize.

5. Customizable Agency: Not all “agents” are the same. Be prepared for every LLM app to be named an agent for the foreseeable future. Since we can’t prevent that (freedom of speech, duh) we will use frameworks around levels of agency. Exactly how many autonomous decisions is your app making while that FOR loop runs? What tools is it using? What is it allowed to do/not without human feedback? SaaS 2.0 products will implement frameworks to help enterprise customers craft the right levels of agency for themselves.

These are very much hypotheses but I’m excited to dig into examples of how some startups are already building in these directions in future posts. DM me if you want to work together on them!