AI Is Unlocking Millions Of New Builders
Quick Read
Summary
Takeaways
- ❖Emergent, a YC-backed company, has seen 7 million apps built on its platform in just 8 months since launch.
- ❖The platform enables non-technical users (80% of its base) to build and ship production-ready, full-stack software using AI agents.
- ❖Emergent's core innovation lies in its multi-agent architecture, long-term memory system, and custom Kubernetes infrastructure, which ensures seamless build-to-deploy processes.
- ❖The founders believe AI is unlocking a vast market of 'new builders' who are domain experts but lack coding skills, leading to a 'Cambrian explosion' of niche applications.
- ❖Traditional SaaS models are under pressure as users increasingly demand customized, agent-first software or build their own internal tools.
- ❖Emergent's team operates globally with a strong presence in India, maintaining high standards and customer empathy through mandatory customer interactions for all employees.
Insights
1Emergent's Rapid Growth and Strategic Pivot
Emergent achieved remarkable growth, with 7 million apps built in 8 months. Initially, the company focused on building advanced coding agents for engineers and even applied to YC with an idea for automating software testing. However, they pivoted from an enterprise-focused, technical user base to empowering non-technical users, who now constitute 80% of their platform's activity. This shift was driven by observing the rapid growth of front-end prototyping tools and realizing the unmet need for production-ready software for non-developers.
7 million apps have been built with Emergent in 8 months since launch (). They applied to YC with an idea of automating software testing (). They pivoted to general coding agents (). 80% of users are non-technical with zero programming knowledge ().
2Architectural Advantage for Production-Ready Software
Unlike competitors focused on front-end prototyping, Emergent was designed from the ground up to ship production-ready applications. This involved building their own Kubernetes-based cloud infrastructure, ensuring agents use the same environment for both build and deployment. This approach minimizes issues during the deployment phase and allows for rapid feedback to the agents. Their chosen tech stack (Python backend, React frontend) supports complex features like background jobs and asynchronous processing, anticipating users' growing ambitions.
To automate all of software engineering, you need a platform that replicates best engineering practices like automated testing, debugging, deployment, security, and hosting (). They built their own Kubernetes tech stack from the ground up (). Providing agents the same infra during build and deploy time reduces problems (). They use a Python backend and React frontend to support advanced features like background jobs ().
3Multi-Agent System with Continual Learning and Memory
Emergent's platform is built on an early multi-agent architecture that manages context frugally and enables agent-to-agent communication. A key innovation is its long-term memory system, where agents learn from aggregated trajectories across all user sessions. This allows the agent to auto-generate 'skills' based on past successes (e.g., a calendar integration that struggled weeks ago now works seamlessly), effectively implementing a form of continual learning and outperforming agents without such capabilities.
They invented the multi-agent system, memory, and agent-to-agent communication (). The agent learns not just from your own session but across sessions, a variant of continual learning (). Skills get auto-generated based on previous trajectories and added to long-term memory ().
4Empowering Domain Experts and Solopreneurs
Emergent's platform unlocks the ability for domain experts and solopreneurs to build custom software without technical barriers. Examples include a clinical psychologist building an app marrying psychology and horse riding ('Equine'), a business developer creating a CRM for lawyers, and small business owners automating processes previously handled by spreadsheets or dev shops. This reduces the 'translation loss' that occurs when non-technical founders try to explain their vision to developers, enabling them to build exactly what they envision.
People who were close to a problem domain expert but blocked by technology are now using Emergent (). Users are small/medium business owners running businesses on email, WhatsApp, spreadsheets (). A clinical psychologist built an app called 'Equine' marrying psychology and horse riding (). Users say 'I know what I want to build if I could just say it out loud myself I would do a better job' ().
Bottom Line
The traditional SaaS model faces existential threats from AI agents consuming workflows and the rising demand for highly customized, personalized software.
Generic SaaS solutions will struggle as AI agents automate tasks and users opt to build bespoke applications that perfectly fit their unique workflows, often at a fraction of the cost. Companies must pivot to an 'agent-first' approach or risk being replaced by internal tools built on platforms like Emergent.
Entrepreneurs can build platforms that enable rapid, customized software development for niche markets, or develop 'agent-first' SaaS solutions. Existing SaaS companies must integrate AI agents deeply into their offerings and allow for extreme customization to remain competitive.
The future of AI agents involves 'agent swarms' collaborating on long-horizon tasks, potentially running for 24+ hours.
This evolution will unlock new levels of automation and complexity in software development and other fields. However, it necessitates sophisticated 'overseeing agents' and robust verification loops to ensure trajectories don't derail and tasks are completed accurately.
Invest in research and development for advanced AI verification systems, multi-agent orchestration, and long-term memory solutions. Develop tools and platforms that facilitate the creation and management of agent swarms for complex, multi-day projects.
Opportunities
AI-powered CRM for Niche Professional Services
Develop a customizable CRM platform specifically tailored for niche professional services (e.g., lawyers, consultants, specialized therapists) where generic solutions fall short. Leverage AI agents to automate data entry, client communication, and workflow management, allowing domain experts to build and adapt the CRM to their exact needs without coding.
Customizable Business Automation for SMBs
Offer a service or platform that helps small and medium businesses (SMBs) automate manual processes (e.g., intake forms, scheduling, inventory management) currently handled via spreadsheets, email, or WhatsApp. Provide AI-powered tools that allow business owners to 'prompt' their desired automation, resulting in a custom, full-stack application.
Personalized 'Passion Project' App Development Platform
Create a platform specifically for solopreneurs and domain experts to build highly specialized applications that combine seemingly disparate fields (e.g., clinical psychology and equestrian coaching). Focus on abstracting all technical complexity, allowing users to bring their unique insights to life as functional apps.
AI-driven Internal Tool Replacement Service
Provide a service for companies to replace expensive, generic SaaS subscriptions with custom-built internal tools using AI agents. This allows businesses to save costs and create perfectly tailored software that aligns with their specific workflows, potentially managed by non-technical team members closest to the problem.
Key Concepts
Jevons Paradox
Increased efficiency in resource use (like AI in software development) leads to increased consumption of that resource. The hosts apply this to software engineering, noting that more powerful AI tools lead to more ideas, more work, and a higher expected rate of software shipment, rather than a reduction in jobs.
Pareto Principle (80/20 Rule) Inverted
The last 20% of a task often requires 80% of the effort. The founders highlight that getting software to be 'production-ready' (the 'last mile') is often neglected but is where significant engineering finesse and effort are required, a problem Emergent specifically addresses.
Lessons
- Prioritize building robust verification loops for AI agents, as this is fundamental for enabling long-term autonomy and preventing task derailment in complex operations.
- Cultivate deep customer empathy across your entire team by requiring all employees, including engineers, to regularly engage with customer support and feedback. This bridges product-market gaps, especially for global teams serving diverse user bases.
- When developing AI products, consider an 'engineering-maximalist' approach first (building powerful core capabilities) and then focus on simplifying the user experience, rather than starting with a simple UI and trying to add power later, which can lead to architectural limitations.
- Leverage influencer networks and targeted messaging to rapidly scale distribution, especially when entering a competitive market as a 'second mover' with a superior product.
- Embrace a global talent pool, particularly in regions with strong technical expertise, and foster a culture of high ownership and problem-solving by giving significant responsibility to small, focused teams.
Notable Moments
Emergent's QA engineer cloned Jira as his first prompt, leading to an internal Asana clone that saved the company thousands monthly and perfectly fit their workflow.
This demonstrates the platform's power to replace commercial SaaS with highly customized, cost-effective internal tools, even built by non-developers, validating the 'personalized software' trend.
A user from Alaska, a clinical psychologist and equestrian coach, built an app called 'Equine' to marry her two fields, an app that would never have been built in a world of limited software.
This exemplifies the 'Cambrian explosion' of niche ideas and the unlocking of human creativity that AI-powered building platforms enable, empowering domain experts to bring unique visions to life.
Quotes
"No one's really talking about the fact that actually like if you have like some agency of interest, you want to start your own business and have autonomy over your life, like you are empowering that at scale."
"To automate all of software engineering you will have to build a platform that replicates what best engineering team do like code reviews automated testing debugging deployment security hosting."
"The last mile that you mentioned, right, is is always what people neglect that hey, you need to make sure that not not only app gets built, it also gets deployed."
"Unless your SAS company pivots into like an agent first company, you know, I think that's going to be hard to sort of survive."
"In a world of limited software that app would never have been built. But in a world of unlimited software you can build that and 7 million other apps that like nobody would have ever gotten to build."
Q&A
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