Introduction

Imagine we deleted every codebase on earth and asked an AI, restricted to producing only assembly, to rebuild our software stack. What would come back? Which abstractions would re‑emerge, which would disappear, and which would look entirely different?

Humans built layers to optimize for our cognition: readability, portability, team coordination. AI optimizes differently: for verifiability, performance transparency, and search across immense design spaces. This essay explores what an AI‑first stack could look like and, more importantly, the practical lessons we can adopt today: write tighter specs, prefer domain‑specific IRs over monolithic languages, make costs visible, and co‑design software with hardware.

The early days of coding

Early programming sat near the metal. We traded developer time for machine time: assembly gave performance and control, but scaling teams and products demanded higher‑level languages, standard libraries, type systems, and build tools. Abstractions emerged to compress recurring patterns and enable portability across architectures.

The cost of these gains was opacity and overhead. Compilers, runtimes, and frameworks created layers between intent and execution. For humans, this is a net win. For a system with perfect recall and tireless search, the trade might be different.

The early days of AI

Modern AI excels at:

• Pattern search over vast spaces (e.g., instruction sequences, schedules, memory layouts)
• Superoptimization of small, hot paths
• Synthesis from constraints and examples
• Learning cost models from data

But it struggles with:

• Ambiguous specifications and shifting requirements
• Long‑horizon system design without intermediate feedback
• Social context: maintainability, governance, team handoffs

This mismatch suggests AI would invent layers that surface precise intent and stable constraints, then freely re‑optimize execution beneath them.

If we had ChatGPT then: what would AI build first?

1. Formal specs and tests before code

AI benefits from crisp goals. Expect contracts, properties, and invariants to lead the process. Rather than “write a parser,” the spec would define accepted grammars, error conditions, memory and latency budgets, and proof obligations. Code generation becomes a search bounded by verifiable constraints.

2. Domain‑specific IRs and DSLs

Instead of one general‑purpose language for everything, an AI would likely favor a small set of intent‑centric DSLs (for parsing, linear algebra, streaming, consensus) that lower into shared intermediate representations. Separation of intent (what) from schedule (how) becomes explicit.

3. Cost models at every layer

Every transformation would be accompanied by an explicit model of time, memory, bandwidth, and energy. Hot paths become superoptimized; cold paths remain simple but correct. The system learns which schedules win on real hardware and updates policies automatically.

4. Hardware/software co‑design loops

With codegen at the metal, the boundary between ISA, microcode, and runtime blurs. An AI would iterate: propose an instruction, synthesize kernels that use it, evaluate end‑to‑end workload impact, and keep the parts that pay for themselves.

The present day: early signals

We can already see the contours of this world:

• LLVM IR as a lingua franca; eBPF as safe, low‑level programmability
• ML compilers (XLA, TVM, Glow) and auto‑tuned kernels
• Superoptimizers and learned cost models
• Verified toolchains and formal methods moving into production

The shape is consistent: intent lowered into IR, schedules searched and verified, feedback loops tied to measurable costs.

Counterarguments and constraints

There are good reasons not to collapse everything into assembly-level artifacts:

• Humans still read and govern systems; comprehension matters
• Interoperability and standards reduce systemic risk
• Regulation, safety, and auditability require stable interfaces

An AI‑native stack must meet these constraints without forfeiting performance and verifiability.

Practical lessons for today

Adopt the spirit of an AI‑first stack without rewriting the world.

Checklist to pilot this quarter

• Define crisp specifications for one critical subsystem (APIs, invariants, budgets)
• Introduce a tiny DSL or schema for intent; keep the schedule separate
• Add cost visibility: record time, memory, bandwidth for key paths
• Auto‑generate tests and property checks with AI; fail closed
• Superoptimize one hot loop or kernel; lock in measurable wins
• Involve infra/hardware earlier; co‑design interfaces and budgets

A minimal example: intent vs schedule

The point is not to ban high‑level languages; it is to make intent explicit and make schedules swappable under testable constraints.

For example, a matrix multiply “intent” could live in a DSL that compiles to an IR. The “schedule” chooses tiling, vectorization, and memory layout per device. AI explores schedules, verifies correctness, and keeps the fastest within constraints.

The future

Expect toolchains that negotiate abstractions per workload: for each deployment, the system re‑derives schedules, regenerates kernels, and ships binaries proven equivalent to the spec. Runtimes self‑tune; proofs and benchmarks ship alongside artifacts. New roles emerge: spec engineers, abstraction curators, and cost modelers.

Conclusion

If AI only wrote assembly, it would still rebuild abstractions, but around verifiability, cost, and search rather than human ergonomics. We do not need to erase our stack to benefit. Start small: pick one subsystem; write the spec tighter than feels comfortable; separate intent from schedule; make costs visible; and let AI search beneath the glass.