Foundries and navigators address different costs in scientific work. A foundry invests in producing experimental measurements at greater scale or speed; a navigator uses AI and tailored software to help teams analyze results, choose experiments and move information through everyday workflows. In a September 24, 2026 guest essay for Latent.Space, Adrian Sanborn describes the distinction as a response to an imbalance: “Thinking got cheap and doing did not.” That is his thesis, not a universal finding established by comparative data.
What “foundry” and “navigator” mean
Sanborn uses foundry for an operating strategy built around industrializing measurement: generating experimental data faster through methods such as next-generation sequencing, multiplexing, high-throughput microscopy and physical automation. The goal is to increase the capacity to do experiments and obtain observations.
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A navigator, by contrast, brings AI and software into ordinary research and company processes. The aim is to improve decisions, speed analysis and tool-building, and help teams decide which questions warrant experiments. It targets the work around experimentation rather than replacing physical measurement.
The labels describe strategies in Sanborn’s framing, not a standardized industry taxonomy. They are most useful as a way to ask where a team’s constraint lies: generating measurements, or deciding what to do with the information it already has.
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Where the two approaches try to lower costs
| Dimension | Foundry | Navigator |
|---|---|---|
| Primary target | Cost, speed and throughput of physical measurement and data generation | Decision-making, analysis and workflow surrounding experiments |
| Typical emphasis in the essay | Specialized experimental methods and physical automation | AI-assisted analysis, internal software and research tools |
| Strategic commitment | Capital investment, years of development and a bet on a particular technology | Can be adopted without proprietary models or a massive dataset, though fit depends on the organization |
| Potential advantage | More experimental data capacity | More adaptable decisions and analysis as protocols and questions change |
| Potential friction | Infrastructure cost and technology-specific commitment | Internal tools may have less polish and no vendor support team; validation, contracts and compliance can add burdens |
This is a comparison of the essay’s concepts, not a standardized scorecard or a claim that one strategy is more effective. The approaches can complement each other: more measurement capacity can produce more data, while better navigation may help a team use its time and information more effectively.
What examples Sanborn gives—and what they do not prove
Sanborn associates Xaira, NewLimit, Octant, Tahoe and Endura with next-generation sequencing and multiplexing; Insitro, Eikon and Noetik with high-throughput microscopy; and Lila and Periodic Labs with physical automation. Those are the essay author’s examples and classifications, not independently verified descriptions of each company’s current operations.
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To illustrate what navigation might change, Sanborn gives several comparisons drawn from his company experience:
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- A software tool built in one day that he says otherwise would have carried a six-figure license.
- Evaluating 500 candidates for disease-program selection, rather than five under what he describes as ordinary team capacity.
- A new protocol evolving “a dozen times in the first year.”
The guest essay supplies no underlying dataset, study design or independent benchmark for these examples. They should be read as Sanborn’s illustrations, not measured industry-wide outcomes or guarantees for another lab.
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How navigation can change research workflows
Keep analysis closer to the experiment
Sanborn describes protocols changing during discovery and analysis code needing updates to match. When coding and analysis tools are easier to adapt, the scientist who ran the experiment may be able to revise and interpret the analysis directly, rather than handing every change to a separate computational analyst. He also describes interactive dashboards as a way to make results more accessible to researchers who understand the experimental context. These are claims based on his company experience, not controlled findings.
Make internal data portals reflect the questions a team asks
A general-purpose vendor system has to serve many laboratories. A purpose-built internal portal can instead reflect one team’s data and preferred comparisons. Sanborn argues that this choice is not merely cosmetic: what an interface displays can influence what researchers notice and compare, so deciding what to show is part of research decision-making. He says implementation can take a day while deciding what the portal should do can take weeks; that timing is another illustrative claim from the essay, not a general delivery estimate.
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When each strategy may fit
A foundry may fit a measurement bottleneck
If the constraint is the rate, cost or capacity of producing physical measurements, a foundry strategy addresses that side of the problem directly. It also makes a substantial commitment: Sanborn characterizes foundry-building as requiring capital, years and a bet on a particular technology. That makes the decision less like adding a small software tool and more like choosing a long-term operating model.
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A navigator may fit a workflow or decision bottleneck
If the drag lies in updating analyses, building internal tools, comparing results or selecting the next experiment, navigation may be relevant without requiring proprietary models or a massive dataset, according to the essay. Sanborn says adoption can be easier for early-stage teams that have fewer legacy systems and entrenched processes. That does not establish that building tools internally is always preferable to buying software.
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Existing obligations can change the calculation
Internal tools can be tailored to local workflows, but they may be less polished and lack a vendor support team. Larger organizations may also face validation requirements, contracts and compliance obligations that make internally built approaches harder to adopt. Those constraints belong in the decision from the outset, not just in an implementation checklist.
How to use the distinction without overclaiming
The foundry-versus-navigator lens helps locate a cost; it does not prove that a particular investment will pay off. A team considering either approach can make the discussion concrete by asking:
- Is the binding constraint physical measurement and data generation, or analysis and decisions around experiments?
- Does the proposed change require specialized infrastructure or a long-term technology commitment?
- How often do protocols and analysis requirements change, and who needs to make those changes?
- Would a tailored internal tool solve a specific workflow problem, and who would maintain and support it?
- What validation, compliance, contract or vendor-support requirements apply?
Sanborn’s essay argues for treating faster knowledge work and experimental capacity as related but distinct problems. It does not establish that every lab should build its own tools, that navigation reliably outperforms purchasing software, or that the cited productivity examples will generalize across research settings.
Read Adrian Sanborn’s September 24, 2026 guest essay in Latent.Space.
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