AI is often presented as a shortcut to profitability. For software companies, the promise is easy to understand: faster product development, more automated customer support, better sales productivity, leaner operations and, potentially, fewer people required to do the same amount of work. But the commercial reality is more complicated.
AI can improve margins, but it can also introduce new costs. Inference costs, cloud infrastructure, data quality, compliance, product complexity, customer education and human oversight can all turn AI from an efficiency tool into a new cost centre.
In this article, we separate the AI profitability question into three different issues: whether AI-native startups are more profitable than traditional software businesses, whether adding AI features improves the profitability of existing software companies, and whether internal AI adoption really reduces operating cost.
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Are AI startups more profitable than traditional software businesses?
AI startups can grow incredibly quickly. In some cases, they are reaching revenue milestones faster than previous generations of SaaS companies. But speed of growth does not automatically mean stronger profitability.
Bessemer Venture Partners’ State of AI 2025 report highlights this tension. The firm found that a group of high-growth “AI Supernova” startups reached around $40m ARR in their first year of commercialisation and around $125m ARR in their second year. However, these companies averaged only around 25% gross margins, suggesting that some of the fastest-growing AI businesses are trading profitability for distribution and scale.
That is a very different margin profile from traditional SaaS, where high gross margins have long been one of the attractions of the model. There are signs that AI unit economics may improve over time. ICONIQ’s 2026 State of AI: Bi-Annual Snapshot suggests that AI product gross margins are expected to reach around 52% on average in 2026, helped by companies using multiple model providers and routing simpler tasks to cheaper or fine-tuned models.
For AI companies, the path to profitability depends heavily on model optimisation: choosing when to use frontier models, when to use smaller or fine-tuned models, and how to route different tasks to the most cost-effective infrastructure. The strongest AI businesses will not simply be those with the most impressive product demos, but those that can turn usage growth into attractive unit economics.
Does adding AI features to your software make you more profitable?
For established software companies, the question is slightly different. Many businesses are now adding AI features to existing products. In theory, this should create value. AI can make products easier to use, reduce manual workflows, improve automation, increase customer stickiness and create opportunities for premium pricing.
But adding AI to a product does not automatically improve profitability. An AI feature only improves margins if it creates more economic value than it costs to deliver. That value might come through higher pricing, better retention, stronger expansion revenue or lower support and implementation costs. But if customers do not pay more, usage costs increase, and the product becomes more complex to support, AI can reduce margins rather than improve them. This is where software companies need to be careful. There is a difference between an AI feature that looks impressive in a demo and an AI feature that improves the commercial performance of the business.
Traditional SaaS products often had relatively predictable delivery costs once the product was built. AI products can be more usage-sensitive. The more customers use an AI feature, the more the company may pay in model, infrastructure or processing costs. This inference cost is one of the key differences between traditional software and AI-enabled software: usage can create a variable cost that needs to be managed carefully.
Does bringing AI into internal work processes really save cost?
The third question is about internal operations. Many companies are using AI to improve productivity across functions such as engineering, marketing, sales, customer support, finance, HR and operations. The logic is straightforward: if people can complete work faster, the company should become more efficient.
But individual productivity gains do not always translate neatly into company-level cost savings. McKinsey’s 2025 research shows that while AI adoption is widespread, companies are still working out how to capture measurable value at scale. Its global survey found that organisations are using AI across multiple functions, but that scaling and financial impact remain uneven. McKinsey has also noted that, among companies using generative AI in specific business functions, only a minority reported cost reductions from its use, with HR being one of the clearer exceptions.
AI may help an employee write faster, analyse information more quickly or automate repetitive tasks. But the business only becomes more profitable if that productivity is converted into lower cost, higher output, faster growth or better customer outcomes. In practice, AI can create new work as well as remove old work. Companies may need people to check AI outputs, manage exceptions, improve data quality, redesign workflows, train teams, monitor compliance and explain AI-enabled products to customers. This is why the headcount story is more nuanced than “AI means fewer people”.