The writer, Holden Spaht, is a managing partner at Thoma Bravo.
Broader, integrated software platforms for business are positioned to win out against those selling specific services
The explosive rise of AI start-ups follows a pattern we’ve seen before: small companies racing to apply new technology to specific business problems, promising huge efficiency gains in their narrow slice of the market.
These start-ups typically use standard AI models (like ChatGPT or Claude) and distinguish themselves by writing software on top in order to target specific tasks. Their value proposition is that specialised solutions can compete without worrying much about how businesses function as complete systems.
But this is a vulnerable strategy. Like dotcom companies that burned cash without sustainable business models, many of today’s AI start-ups burn money paying for access to AI models while lacking advantages that competitors can’t copy. Their core bet is that AI can solve specific business problems so efficiently that companies will willingly go beyond their integrated systems to get superior performance in one area — in short, “best-in-class” beats “good-enough-but-all-in-one-place”.
But connecting to full business processes to find a sustainable growth path over time is a very challenging task for companies built to do just one thing. While some might be seeing sharp initial revenue acceleration, they also see substantive costs. One study by Kruze Consulting suggests they spend double what traditional software-as-a-service companies spend on compute and infrastructure, while also paying for specialised talents that command high pay packages.
These structural challenges show why we believe established software platforms such as Salesforce, SAP, Microsoft — and portfolio companies like Anaplan and Coupa that we invest in at Thoma Bravo — will be more durable despite the risks of AI disruption to their businesses.
While AI start-ups struggle to defend narrow territory, broader platforms build advantages that often grow stronger over time. Rather than just making one specific area better, they work on strengthening the broader business of a customer. This model produces intelligence for both the company and the provider, building capabilities. And SaaS companies have years of making improvements.
For example, the software platforms can model cause and effect across business functions. A narrow AI tool might optimise inventory, but if it doesn’t consider cash flow, supplier relationships or supply chain risks, that “optimisation” on a single variable could actually stand to hurt the business.
Established software companies are thus also better positioned to address business concerns on AI integration: competition, implementation challenges, cyber security, legal issues, ethical questions, costs and reputation risks. This is because digital transformation has always meant balancing efficiency gains with risk management. And platforms also spread the cost of staying compliant with regulations across all their customers.
So for established enterprise software, the next horizon for competition is more likely to come from the so-called foundation model companies themselves, such as OpenAI and Anthropic. These firms are in a position to try to build their own comprehensive business applications while keeping their best AI models for themselves. Imagine, for example, OpenAI offering a complete enterprise risk management suite powered by GPT-6 while software companies get stuck with GPT-5; or less capable open weight models that they are forced to choose as an alternative.
The idea would be to force a battle of commoditisation: the foundation model companies want to commoditise software and the software companies want to commoditise the AI ingredients of the software platforms. But this competition story, too, reveals why I believe established platforms will ultimately win.
Companies like OpenAI trying to build business software face the same challenge as every tech giant before them: creating entire business systems from scratch. Code is now relatively easy. What’s hard is the decades of industry knowledge, thousands of existing connections to other software, deep understanding of industry-specific regulations and the built-up trust of large enterprises. These are areas that established software platforms spent decades working on.
Simply put, the maths is against AI start-ups: they need to deliver gains large enough to justify the work and risk of managing a separate tool. And established software companies are positioned to win by integrating innovation rather than fragmenting it.