The Importance of Shipping Early, Often and Incrementally in Software Products Supporting Deep Tech
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Deep tech software teams often fall into a trap: because the underlying technology has long cycles and high uncertainty, the software “waits” for clarity. The result is predictable: big-bang releases, long feedback loops, and systems that are hard to change when reality finally arrives.
I’ve learned (sometimes the hard way) that shipping early, often, and incrementally is even more important when the environment is uncertain.
What “shipping early” actually means in deep tech
In deep tech, “shipping” does not always mean “launching a polished product to the public”.
It can mean:
- a thin end-to-end workflow that touches the real systems,
- a stable API surface that other teams can integrate against,
- a simulator/emulator path that mirrors the real hardware path,
- or a minimal UI that lets real users validate assumptions.
The key is that something real moves from “ideas” to “usage”, and you learn from it.
Why deep tech makes this harder (and why that’s exactly why you must do it)
Three forces push deep tech teams away from incremental releases:
- Evolving requirements. The research roadmap changes, hardware constraints change, and the “right” abstractions move under your feet.
- Research creep. Engineering work quietly turns into open-ended exploration because “we’re not sure yet”.
- Integration uncertainty. The failure modes show up at interfaces: hardware/software, data pipelines, user workflows, and deployment environments.
If you respond to these forces by delaying releases, you reduce the amount of information you get. That makes the uncertainty worse.
Feedback loops are the point
Incremental shipping creates feedback loops with:
- end users (“does this solve anything?”),
- domain experts (“is this aligned with physics/biology/etc?”),
- and stakeholders (“what progress is real, and what is wishful?”).
It also forces the team to build the scaffolding that deep tech projects need anyway: integration tests, build/release discipline, and operating procedures.
A concrete example: emulators that “didn’t matter” until they did
One pattern I’ve seen is a feature that looks like a nice-to-have (e.g. a cloud emulator path) that gets deprioritized because usage is low.
Then a new hardware milestone lands, and suddenly that feature becomes the main on-ramp for users who want fast iteration before paying the cost (time, queueing, money) of running on real hardware. If you kept shipping incremental improvements, you’re ready. If you paused, you end up firefighting at the worst possible time.
How to ship incrementally without lying to yourself
The common failure mode is “demo-ware”: a release that looks impressive but isn’t built to survive real usage.
What helped me avoid this is to explicitly choose one “hard” thing to include in every increment:
- real data, or
- real deployment (not just a notebook or mocked/pre-computed data), or
- real performance constraints, or
- run in the final execution environment (not pre-chosen data).
If every increment contains one non-negotiable constraint, you build something you can extend, not just something you can show.
A small checklist I’d use today
- Define a thin slice that runs end-to-end and can be repeated.
- Write down “done” in measurable terms (latency, cost, correctness, usability).
- Ship to a small set of real users earlier than you’re comfortable with.
- Make it easy to change the system (tests, clear boundaries, docs that match reality).
- Keep a tight boundary between “research” and “product” work, even if the same people do both.
If you want a companion post to this, I think this pairs well with Architecting new projects because it’s the same story at a different zoom level: uncertainty is normal, and the process has to assume it.