RCA
Elite Member
- Joined
- Jun 27, 2017
- Professional Status
- Certified General Appraiser
- State
- California
For those working with AI Agents through Claude Code, it is become so clear: AI is going to change the business models:
1. Want to write code and sell it? The problem is that smart software engineers can turn out near perfect code pretty damn fast, with the ability to replace MAJOR software systems with full tested and testable code relatively fast. And unless they can keep their code and designs perfectly secret, which is nearly impossible to do, it can be replicated by other smart software engineers.
2. So selling code does not make sense. Ideally, if you write a killer app, you are better off to either sell it fast or make a company around it, and put a high security wall around your code and data.
3. But another thing, SAAS typically creates a fixed package of complex services that require training to use. Well, that means it has limitations when it comes to fitting specific requirements of corporations.
4. Ideally corporations should write their own code to give them a strong competitive advantage. AI makes that nearly possible - they just need to find a smart enough software engineer to do the work. That is the bottleneck.
5. Here's a new business model. The smart software engineers find a handful of companies or professionals, no more than one in each major market area and provides optimal software to make them #1. He won't get rich, necessarily, because he has to limit his client base.
6. But he might do reasonably well.
GROK:
"AI—particularly agentic AI (autonomous systems that can reason, plan, decide, and act across tools)—is poised to dramatically reshape or even displace many traditional SaaS (Software as a Service) business models. While SaaS isn't vanishing overnight, the consensus from industry analysts, tech leaders, and reports in late 2025/early 2026 points to a fundamental shift away from seat-based subscriptions, rigid dashboards, and human-driven workflows toward more dynamic, outcome-oriented, and AI-orchestrated alternatives.
1. Want to write code and sell it? The problem is that smart software engineers can turn out near perfect code pretty damn fast, with the ability to replace MAJOR software systems with full tested and testable code relatively fast. And unless they can keep their code and designs perfectly secret, which is nearly impossible to do, it can be replicated by other smart software engineers.
2. So selling code does not make sense. Ideally, if you write a killer app, you are better off to either sell it fast or make a company around it, and put a high security wall around your code and data.
3. But another thing, SAAS typically creates a fixed package of complex services that require training to use. Well, that means it has limitations when it comes to fitting specific requirements of corporations.
4. Ideally corporations should write their own code to give them a strong competitive advantage. AI makes that nearly possible - they just need to find a smart enough software engineer to do the work. That is the bottleneck.
5. Here's a new business model. The smart software engineers find a handful of companies or professionals, no more than one in each major market area and provides optimal software to make them #1. He won't get rich, necessarily, because he has to limit his client base.
6. But he might do reasonably well.
GROK:
"AI—particularly agentic AI (autonomous systems that can reason, plan, decide, and act across tools)—is poised to dramatically reshape or even displace many traditional SaaS (Software as a Service) business models. While SaaS isn't vanishing overnight, the consensus from industry analysts, tech leaders, and reports in late 2025/early 2026 points to a fundamental shift away from seat-based subscriptions, rigid dashboards, and human-driven workflows toward more dynamic, outcome-oriented, and AI-orchestrated alternatives.
Key Ways AI Is Disrupting SaaS
Traditional SaaS relies on per-user/per-seat pricing, where companies pay for access to tools (e.g., CRM like Salesforce, productivity suites like Microsoft 365, or project management like Asana). Users manually navigate interfaces to input data, run reports, or execute tasks. AI changes this in several profound ways:- AI Agents as the New Interface Layer Instead of logging into multiple apps, users (or entire organizations) interact via conversational AI agents that handle complex workflows autonomously. These agents pull from underlying data sources/APIs, perform actions across systems, and deliver results without requiring users to touch the original SaaS UI. Microsoft’s Satya Nadella and executives like Charles Lamanna have predicted that by 2030, traditional business apps could become "mainframes of the 2030s"—legacy systems still running but largely obsolete as AI agents take over logic and orchestration.
- Shift from Tools to Outcomes/Performance SaaS sells features and access; AI-native models sell results (e.g., "generate leads," "process invoices," or "optimize supply chain"). Pricing moves toward consumption-based (pay per task/outcome), usage-aligned, or even agent-based models, rather than flat subscriptions. IDC predicts that by 2028, pure seat-based pricing will be obsolete for 70% of vendors, replaced by metrics tied to value delivered (consumption, outcomes, or capability).
- Scenarios for SaaS Disruption(from Bain, Glean, and others) AI agents can:
- Enhance existing SaaS (e.g., adding GenAI copilots).
- Compress spending (fewer seats needed as automation rises).
- Outshine or cannibalize tools (agents fully replace workflows, making the SaaS redundant). Many predict 35%+ of point-product SaaS tools could be replaced or absorbed into larger AI ecosystems by 2030 (Gartner/Deloitte estimates).
- Emerging Models Replacing Pure SaaS
- Networked SaaS → AI-powered platforms that connect ecosystems (e.g., vertical industries) with network effects, moving beyond per-seat to ecosystem value.
- AI-Native Agents/Services → Companies sell autonomous agents that perform work directly, often at lower cost and higher adaptability than traditional tools.
- Headless/Backend SaaS + AI Layer → Core data/logic stays in SaaS, but AI overlays handle interaction and execution.
Timeline and Caveats (as of January 2026)
- Short-term (2026): Rapid integration of AI agents into existing SaaS (e.g., major vendors building federations of agents). Pricing experiments accelerate; many tools become "augmented" rather than fully replaced.
- Medium-term (2027–2030): Widespread agent adoption could make much of today's SaaS feel outdated. Predictions vary—some (e.g., aggressive VC/LinkedIn takes) say 50%+ replacement by end-2026; others (Deloitte, Bain) see it taking 5+ years for full disruption.
- Not Total Replacement: SaaS often provides essential systems of record (data storage, compliance, security). AI agents frequently rely on them as backends. The smartest incumbents are evolving into AI-powered platforms rather than resisting.