AI Agents: A $4.6T opportunity to transform the software industry(foundationcapital.com)
foundationcapital.com
AI Agents: A $4.6T opportunity to transform the software industry
https://foundationcapital.com/system-of-agents/
3 comments
> The shift has other business model implications too. Take the observability sector. The industry still grapples with data overload, skyrocketing costs, and a critical shortage of skilled personnel. Incumbent company pricing models (think Datadog or Splunk) are wildly mismatched to customer needs, charging per GB for log management, for example, or ratcheting up costs with infrastructure size and data volume.
We have no reason to believe that AI costs won’t skyrocket long term either. It feels like the early days of streaming right now, but how long will it last?
Foundation models are pretty reasonably priced depending on the workload, but having a plurality or majority of companies all hooking up to the same two or three model APIs is gonna get really interesting when prices start to increase, because your exposure to the price increase scales with the number of times that same provider is used by your vendors, your vendor’s vendors, etc.
If Altman wakes up one day and decides to double the token price, it could be Armageddon in the SaaS space.
We have no reason to believe that AI costs won’t skyrocket long term either. It feels like the early days of streaming right now, but how long will it last?
Foundation models are pretty reasonably priced depending on the workload, but having a plurality or majority of companies all hooking up to the same two or three model APIs is gonna get really interesting when prices start to increase, because your exposure to the price increase scales with the number of times that same provider is used by your vendors, your vendor’s vendors, etc.
If Altman wakes up one day and decides to double the token price, it could be Armageddon in the SaaS space.
Have anyone tried this new AI Agent from Seeedstudio yet? It's called SenseCAP Watcher
The key insight is that to achieve truly automated development, we need AI agents across the entire development lifecycle - not just in isolated tasks.
Our system implements a "Retrieval Augmented - Complex Coding Task accomplishment loop" with: - Multiple LLM-based agents handling different aspects of development - A Task and State Management System for agent coordination - Dual knowledge retrieval combining self-repository learning and GitHub public knowledge - Integrated development tools library (editing, testing, deployment) orchestrated through multi-agent collaboration
The key differentiator is the holistic integration - every step from initial development to deployment is agent-aware and interconnected. This creates a true "Service-as-Software" development environment where AI doesn't just assist, but actively drives the development process.
We're seeing promising results in automated code generation, self-healing test suites, and intelligent CI/CD pipelines that learn from deployment patterns.
Would love to hear thoughts from the community, especially from those working on similar full-lifecycle automation approaches.