Snyk founder's new venture, TESOL, posits that AI will make code disposable. Instead of code being the source of truth, a durable, versioned 'spec' document defining requirements will become the core asset. AI agents will generate the implementation, fundamentally changing software development.
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The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.
In 10 years, AI will generate vast amounts of high-quality code, similar to the leap in image generation. The developer's role will shift from writing code to curation and design, exercising intent and critical judgment to select the best output from a sea of AI-generated options.
The current model of separate design files and codebases is inefficient. Future tools will enable designers to directly manipulate production code through a visual canvas, eliminating the handoff process and creating a single, shared source of truth for the entire team.
Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.
Jack Dorsey champions "vibe coding," using AI to generate code, allowing developers to operate at a higher level of abstraction. This shifts focus from syntax (like semicolons) to orchestration, making software creation more accessible and freeing developers to be more creative.
AI acts as a massive force multiplier for software development. By using AI agents for coding and code review, with humans providing high-level direction and final approval, a two-person team can achieve the output of a much larger engineering organization.
The role of a senior developer is evolving. They now focus on defining outcomes by writing tests that a piece of code must accomplish. The AI then generates the actual implementation, allowing small teams to build complex systems in a fraction of the traditional time.
It's infeasible for humans to manually review thousands of lines of AI-generated code. The abstraction of review is moving up the stack. Instead of checking syntax, developers will validate high-level plans, two-sentence summaries, and behavioral outcomes in a testing environment.
As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.
Historically, developer tools adapted to a company's codebase. The productivity gains from AI agents are so significant that the dynamic has flipped: for the first time, companies are proactively changing their code, logging, and tooling to be more 'agent-friendly,' rather than the other way around.
