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CONTRIBUTORS

Jonathan Curtis

Portfolio Manager
Franklin Equity

Matt Cioppa, CFA

Portfolio Manager, Research Analyst
Franklin Equity

Executive summary

Enterprise software is undergoing its most significant reset in a generation. Artificial intelligence (AI) is reallocating value within software—creating clear winners and exposing vulnerabilities in business models that have worked well for the past two decades. We believe investors who treat software as a uniform asset class will make costly mistakes.

What AI has changed

  1. The cost of writing software has collapsed. AI coding tools now perform at the level of elite human programmers, meaning more software will be created and deployed than ever before.
  2. AI is automating knowledge work at scale. Enterprises can now do more with fewer people, putting direct pressure on software companies whose revenue depends on human headcount (seats) growing predictably.
  3. The “software as a service” model is fracturing. The industry is splitting into distinct camps, and this past earnings season made that division even clearer.
     

Implications for the industry

  • Seat-based software incumbents that have not yet adapted their business models may face increasing pressure.
  • Companies that benefit from AI-driven usage expansion—infrastructure, security, orchestration—and systems of record deeply embedded in enterprise workflows will be better positioned.
  • Analysis requires a disciplined framework: pricing power, gross margin stability, consumption-based monetization and evidence that AI is accelerating the business.
     

The two disruptions

Two separate, but related, forces are reshaping enterprise software.

The first is the collapse in the cost of creating software. Since 2022, AI has moved from a novelty to a genuine engineering peer. By late 2025, leading AI coding tools had nearly matched the output of skilled human developers and are continuing to improve. The consequence is straightforward: The volume of software written and deployed globally will increase dramatically. The barrier to creation has effectively fallen, forcing enterprise software investors to re-evaluate how these companies operate.

The second disruption is broader. The same AI systems that write code can now draft documents, synthesize research, manage workflows and automate complex analysis, as well as engage in other agentic knowledge work. Enterprises are realizing that AI doesn't just make developers more productive; it can make entire organizations leaner. That realization raises an uncomfortable question for many software vendors: If AI can perform the work, how many human software seats does an enterprise customer need?

Together, these forces are not destroying software, they are shifting value around and repricing it.

Exhibit 1: Recent Model Advances Rapidly Shifted Coding from Assistance to Autonomy

AI Coding Has Reached an Inflection Point

Source: Franklin Equity. This chart represents a framework for illustrating the qualitative shift in coding autonomy since 2021. In 2021, Codex, the first Large Language Model (LLM) focused on content generation and completions. In 2022, GiftHub and Copilot, early real-world developer adoption at scale. In 2023, GPT-4 large rea-soning models with coding support. In 2024, Next-gen multi-modal code models, such as GPT-4o, Gemini 1.5, and Claude 3, which represented step-ups in context length, planning and code-understanding. In 2025, Advanced agentic and coding augmented systems, such as Claude Opus 4.6, integrated agentic tools, which represented the shift toward models that can autonomously plan, execute and manage code workflow through tools, not just generate code.

Discernment: Three camps

We believe the most important insight from this past earnings season is that "software" is no longer a single investment narrative. We think the sector has split into three distinct camps:

  1. AI workload beneficiaries. As enterprises deploy AI at scale, the underlying systems become more complex. More needs to be monitored, secured, governed and orchestrated. Companies that manage this complexity have started to experience accelerating demand. AI is the engine driving their growth, not a threat to it.
  2. Seat-model incumbents under pressure. Companies built on the assumption that enterprise headcount would grow predictably (and that each employee would need a license) are facing change. They are not all in decline, but they face a painful transition: reinventing how they capture value at the precise moment customers are questioning how much value they need.
  3. AI operating platforms. The most compelling long-term opportunity sits here. These are companies whose products serve as the connective tissue of enterprise AI deployment—workflow orchestration, process automation and systems of record with critical enterprise context on how work is done. Their value proposition is strengthening as AI complexity grows and the best of them are growing faster than their pre-AI trajectory with pricing power intact.
     

The new investment question

The old question was: How fast can this company add seats and upsell value?

The new question is: Where does AI create incremental usage, pricing power and workflow control—and where does it erode them?

This distinction is not subtle. Usage-based expansion driven by AI agents and machine-generated workloads can scale up non-linearly, while headcount-driven seat expansion is capped by the size of the human workforce. The companies best positioned today tend to sit closer to machine activity than to human activity—their products are consumed by systems and workflows, not just by employees logging in each morning.
 

Our investment framework

We believe companies well-positioned in this environment are aligned with the following criteria:

Pricing power through deep integration. Systems of record and systems of engagement that are genuinely difficult to displace, where switching costs are high and the cost of removal outweighs the cost of staying.

AI-driven revenue acceleration. Companies where we can trace the revenue impact through actual usage growth and expanding customer relationships.

Gross margin stability. The acid test of real pricing power: Can a company add AI capability and get paid for it, or does it simply absorb higher costs? Stable or improving gross margins are a strong signal.

Internal AI leverage. Companies using AI effectively in their own operations, particularly in product development, tend to ship better products faster. This compounds over time.

Consumption or outcome-based monetization. The direction of enterprise software pricing is clear. Companies that have already made this transition, or have a credible path to it, are better positioned for the decade ahead.

The bottom line

AI is not replacing software. It is repricing software by reallocating value away from human-seat models and toward companies embedded in AI-driven infrastructure, data and enterprise orchestration. The total volume of software consumed globally will almost certainly grow. But we believe the distribution of that value will look very different from the past decade.

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