An AI R/evolution
This issue frames AI as an evolution in software development. And uses that foundation to construct a framework for how AI startups can implement creative business models to find their edge.
01 | An AI r/evolution
Lately I’ve been hearing variations of this sentiment reverberate across the tech ecosystem:
"AI will fundamentally restructure what we are capable of. Software will move from work efficiency to doing all the work for you."
The implication: AI represents a totally new paradigm, something categorically different than yesterday’s software.
Yes, the capabilities of AI represent a substantial improvement in automation. And yes, adoption is happening at a record pace (thanks ChatGPT!).
But I like to think of AI as a moment in the evolution of our tooling, not something fundamentally novel. After all, "work efficiency" and "doing work for you" are just two different points on the same productivity curve.
Like prior technology shifts, it will take time for the underlying infrastructure and applications to specialize and reach significant adoption. It took enterprise SaaS 20+ years to eclipse 50% penetration.[1] Even at 5x that pace, it will take until the end of the decade for our economy to incorporate AI at scale.
As such, the questions we need to ask to evaluate new, AI-native applications should remain largely the same as traditional SaaS:
What's the “10x” upgrade in user experience? Why should a user care?
What is the theory for how the product will unlock latent or non-obvious demand to grow a new market,[2] or quietly steal share from incumbents?
What are the core, defensible assets, and at what level of scale will those assets materialize?
What's the unique wedge for initial adoption, and how does that wedge drive long-term embeddedness?
Where is there leverage in the growth model, through network effects, a viral coefficient, brand halo, or compounding revenue?
An "AI-first" lens that doesn’t account for these aspects may lead to investments in trendy technology. But it risks backing products that fail to meet real user needs, or are vulnerable to competition from incumbents who integrate LLMs and already have broad distribution and deep network moats.
On that last point, look no further than the velocity of Stripe’s successful roll out of AI tools into its product stack. Within 12-18 months, Stripe has deployed Sigma Assistant (converts natural language into SQL queries), a Copilot integration, neural net-powered fraud detection, LLM-powered merchant risk scoring, and intelligent subscription reactivation. That’s a massive headwind for any AI startup offering similar tooling that has not solved for the above questions.
Technical capability is only one side of the coin. Entrepreneurs must now innovate around their positioning and business model if they wish to build the next iconic, AI-first platform.
02 | A new framework for AI business models
How application-layer business models will adapt in the AI era boils down to two key factors:
1. What makes AI distinct from traditional SaaS is the ability to derive utility from unstructured data.[3] Unstructured data doesn't neatly fit into “rows and columns” for simple retrieval, manipulation, and distribution (what SaaS applications were great at). Extracting value demands complex inferences, translation between formats, and accounting for context - which language models and computer vision can unlock.
2. As machines get better at reading unstructured data, users will move beyond execution-based tasks to operate at a higher level of abstraction. With SaaS, the impact of our keystrokes and clicks were amplified by swift data processing and zero-cost distribution. AI-powered applications will automate the decision-making and judgment underlying those very actions. Users will progress from manual tasks – drafting and distributing a blog post – to more advanced work – an AI agent automatically generates and sends a blog post to a tailored audience, based on a brief outline. (Note - this blog is not quite there yet!)
As a result, software KPIs could shift.
Greater output per user could mean fewer seats. Customers may begin to demand pricing aligned with outcomes and quality — e.g., throughput rate, workload completion, accuracy, business results — rather than feature counts or the number of licenses.
Product love might be gauged by time saved and work delivered, rather than the scale of a retentive, active user base. (Imagine for some products daily active user count could be inversely correlated with ARR!)
Software providers can take on more risk (and upside) around service level agreements.
The below framework outlines how business models can align with shifting user behavior in AI-powered applications:
Of course, these models will take time to proliferate. Buyers will need to adjust to more pricing complexity. Enhanced measurement techniques and reporting need to be integrated into the product GUI, so that customers can trace business outcomes to what they’re paying. Software vendors will need to experiment to mitigate revenue volatility.
But as AI transforms the way customers experience value, business models should inevitably follow suit.
03 | Finding an edge
As companies explore these business models, it’s important to keep in mind:
(1) The success of any new venture hinges largely on the quality of its product offering. Any of these business models – absent a game-changing product – won’t deliver long-term value.
(2) Frameworks like usage-, value-, and risk-based pricing are not new in and of themselves; there's a long history of their application across the economy.
However, marrying these business models with the unique value AI applications provide can help startups build a go-to-market advantage.
Value-based care (VBC) models in healthcare provide an interesting analog.[4]
VBC refers to a payment method whereby providers are reimbursed based on the quality and effectiveness of the care they deliver, rather than the quantity of services provided (what’s called “fee-for-service”).
This (i) shifts the fundamental unit of payment to outcomes, and (ii) provides economic rewards and penalties to hold healthcare providers accountable for delivering those outcomes.
VBC proliferated in the U.S. over the last decade in the wake of the Affordable Care Act of 2010. Transitioning to these arrangements was not easy. Healthcare providers needed to invest in new care coordination workflows, define outcome benchmarks, and establish data infrastructure for tracking results. Introducing risk and variability into the revenue model further complicated the transition from fee-for-service.
However, newer, more innovative providers like Oak Street Health (founded in 2012 and acquired by CVS in 2023 for $10.6 billion) embraced VBC, competing on value from Day 1. In successful cases, implementing VBC contracts and delivering a superior product helped new players achieve structural advantages over their fee-for-service counterparts through longer-term payer contracts, higher margins, and economic incentives to continue to improve their offering over time.
As AI shifts the software performance metric from features and seats to outcomes, I can imagine similar dynamics playing out.
Take a crude example: an AI-powered marketing product that auto-generates and optimizes digital ad campaigns, guaranteeing $100k in net new revenue and taking a 20% cut, might be preferable to a SaaS company charging $500 per user per year, automating work for 40 ad marketers. In both scenarios, ACV starts at $20k. But the former shifts risk onto the software provider, incentivizes ongoing performance, and fosters deeper, longer-term alignment with the buyer.
Startups integrating these models into their DNA can gain an edge over incumbents with static, per-seat pricing who would need to invest in new market positioning, revamp parts of their operational infrastructure, and possibly cannibalize existing business lines in order to transition.
It’s early days, but we’re already seeing signs of outcome- and value-based business models penetrate the technology sector. As AI shifts how we work and what we expect from software, we may see this trend accelerate.
Given the rapid pace of investment from high growth and large-cap companies into AI, technical moats – in and of themselves – are unlikely to be a reliable source of defensibility for new entrants.
That’s why I’m excited to see the creative ways entrepreneurs will marry new, AI-native products with these pricing frameworks in order to craft their own unique edge.
[1] Salesforce launched in 1999. By 2021, 50% of corporate data was stored in the cloud.
[2] See Jensen Huang’s “zero billion dollar markets”
[3] “Unstructured data is information that is not arranged according to a preset data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS. Text and multimedia are two common types of unstructured content. Many business documents are unstructured, as are email messages, videos, photos, webpages, and audio files.” (source)
[4] Value-based care is a complex topic – there is a vast body of literature defining VBC, its implementation, efficacy, policy frameworks, and the various contract models between payers and provider networks. This primer from the University of Pennsylvania provides a good overview. Comparing value-based care to software business models offers a conceptual framework for how to restructure and reprice offerings, in order to optimize incentives and value distribution in a market. I acknowledge the limitations here – it’s not a direct equivalence or one-to-one comparison between the two domains.