The economics of building AI companies have changed. For years, SaaS founders could focus almost entirely on building applications while letting infrastructure fade into the background. That playbook no longer works.
In the AI era, costs scale differently. Cloud usage, inference, and compute often grow faster than revenue, leaving startups exposed. Many founders are now learning that top-line growth means little if margins collapse under the weight of infrastructure bills. The reality is simple: the AI value chain has been reshaped, and survival depends on how well startups adapt.
Why the AI Value Chain Looks Different Today
In the SaaS model, value was captured mostly at the application layer, close to the end user. With AI, the stack runs deeper. It includes energy infrastructure, chips, cloud access, models, vertical AI tools, and only then the applications users interact with.
The margins are no longer concentrated at the top. Instead, they sit further down in areas of scarcity—such as high-demand GPUs, compute capacity, and exclusive access to foundation models. This shift forces startups to rethink their strategies if they want to build sustainable businesses.
Three Strategies Founders Can Use to Stay Competitive
Own your data
Founders do not need to create foundation models from scratch. What they do need is differentiated data. Proprietary, structured datasets are now the strongest competitive moat. For startups in sectors like healthcare, finance, legal, or real estate, workflows that capture and refine unique data can provide long-term defensibility. Fine-tuning open models and building lightweight adapters turn that data into real value.
Price for usage, not access
Flat subscription fees worked for SaaS, but AI economics are different. Compute drives costs, so pricing must align with actual usage. Models such as per-token billing, compute-aware pricing tiers, and premium charges for resource-heavy features like image generation or live inference help preserve margins. Tracking profitability by feature, not only by customer, gives founders a clearer picture of their true costs.
Design for flexibility, not lock-in
Relying on one model provider, whether OpenAI, Anthropic, or another, is risky. Latency spikes, price hikes, or sudden policy changes can disrupt product roadmaps overnight. Building with model abstraction in mind allows startups to route tasks across providers, keep open-source backups, and negotiate stronger contracts. Flexibility is not only a technical safeguard but also a business hedge.
The Path Forward for AI Startups
The AI value chain no longer guarantees healthy margins for companies focused only on the application layer. Yet opportunities remain. Startups that own their data, align pricing with usage, and design for flexibility can create defensible positions and thrive in this new landscape.
AI may have reshaped where the value lies, but founders who adapt quickly will still find room to build profitable, sustainable businesses.
