Key Takeaways

  • Software teams that built heavy wrapper code around foundation models in 2023 are actively deleting it as baseline model capabilities advance.
  • AlphaSense SVP of Product Chris Ackerson points to the company's early generative AI features, which required heavy engineering layers to summarize earnings calls with near 100% accuracy.
  • Product teams carry scar tissue from underestimating model velocity, creating technical debt by solving temporary model weaknesses with permanent application code.
  • Defensibility in vertical software has shifted away from prompt wrappers and retrieval heuristics toward proprietary domain data and workflow integration.

The Trap of Permanent Scaffolding

When enterprise software vendors rushed to adopt large language models in 2023, the baseline models could not handle rigorous financial tasks out of the box. Engineers had to build heavy scaffolding around them: prompt chains, validation guardrails, and parsing logic. The goal was simple: make an imperfect model reliable enough for institutional investors.

Ackerson saw this firsthand at AlphaSense. Reflecting on the shift, he observed: “Everyone working on AI has a lot of scar tissue from underestimating the pace at which these models would improve.” In 2023, AlphaSense focused on document summarization, specifically trying to summarize an earnings call with near 100% accuracy. Getting an LLM to hit that threshold required extensive engineering effort and strict guardrails.

The mistake many teams make is treating that scaffolding as an enduring asset. Six months later, the underlying model improves, rendering thousands of lines of custom code obsolete. Ackerson noted that product teams grapple daily with how much scaffolding to build to make a product usable today versus where the model will sit in six to twelve months. Building too much creates dead weight that slows down future product releases.

Unsentimental Deletion as an Operating Principle

The companies winning the vertical AI race are not the ones with the largest proprietary wrapper codebases. They are the ones willing to tear their own work down. Ackerson explained that AlphaSense had to adopt an aggressive posture toward its own codebase: “We are now incredibly unscentimental about deleting scaffolding and moving towards the future.”

In practical terms, this means throwing away custom logic the moment a base model absorbs the capability. Ackerson pointed out that the engineering work required in 2023 to summarize an earnings call accurately is simply not necessary today. Retaining those custom checks adds latency, increases maintenance costs, and prevents the software from taking advantage of newer capabilities like autonomous reasoning and end-to-end task delegation.

This requires product teams to adopt a beginner's mind on every release. If an engineer falls in love with their custom retrieval pipeline or error-correction loop, they will defend technical debt instead of upgrading the user experience.

Why It Matters

For private equity deal teams and software buyers, this dynamic exposes a major valuation trap in vertical SaaS. Startups that raised capital based on early generative AI features often hold balance sheets full of temporary scaffolding that frontier labs commoditize in the next model cycle. Real enterprise value will not accrue to software that patches foundation model defects, but to platforms that own proprietary datasets and embed directly into institutional execution workflows.