BuildNot yet confirmed elsewhere1 publisher2 min readPublished
Platform teams building mostly from scratch are 3.5x more likely to lack a strategy
Survey responses from 379 platform professionals indicate that teams that mostly build from scratch are 3.5x more likely to lack a clear strategy. The report's authors point to ownership: agents cut the effort of writing code, and the work of owning it remains.
The Engineer · Build desk
17.5% of respondents mainly build from scratch. The report gives a high-performer figure of 33.3% for scratch builds and 58.8% for tools. David Tuite puts upgrades and overhead on custom interfaces at 6 to 18 months. Cost-benefit analysis led build-or-buy logic at 29.6%.
- 17.5 % of respondents Primary approach is building from scratch, claim 4
- 33.3% Report's high-performer figure for scratch builds, claim 13 the report gives 58.8% for tools
- 6 months, at least Months on upgrades and overhead, per David Tuite, claim 15 up to 18 months
- 29.6 % of responses Cost-benefit analysis, claim 8
| Figure | Value | Note | Claim |
|---|---|---|---|
| Primary approach is building from scratch | 17.5 % of respondents | 4 | |
| Report's high-performer figure for scratch builds | 33.3% | the report gives 58.8% for tools | 13 |
| Months on upgrades and overhead, per David Tuite | 6 months, at least | up to 18 months | 15 |
| Cost-benefit analysis | 29.6 % of responses | 8 |
What happened
- Teams that predominantly build from scratch also meet fewer of their platform goals, according to the report.
- Using an underlying tool is the most common primary approach at 51.3% of respondents, ahead of building on a template at 31.2% and building from scratch at 17.5%.
- The report gives high-performer figures of 58.8% for tools, 47.8% for templates and 33.3% for scratch builds.
- Among teams reporting an extremely positive effect from AI, more than three-quarters had ephemeral environments.
- Asked to explain their build-or-buy logic, respondents most often cited cost-benefit analysis, at 29.6%.
Why it matters
- cost Upkeep is paid in platform-team time. David Tuite, as the report cites him, puts it at 6 to 18 months of upgrades and operational overhead for teams maintaining custom interfaces.
- exposure The platform sets how much agents help. DORA's finding, as the authors cite it, is that strong platforms amplify the organizational gains from AI and weaker ones dampen them.
- decision The authors call build-versus-buy a sharper strategic question than ever, so each scratch-built feature needs a keep-or-replace call on its own merits.
The excerpt reports the 3.5x in its headline but not the two rates behind it, the cutoff for "predominantly", or any comparison with surveys from before coding agents. That last gap matters because the explanation on offer is about agents. A single 2026 snapshot cannot show the gap widened as building got cheaper.
Scratch-builders are also the smallest of the three approach groups [4]. If all 379 respondents [1] answered the sourcing question, that group is about 66 teams [11]. A multiple drawn from 66 teams can move a long way on a handful of answers.
The authors' mechanism is volume. Maintenance, upgrades and governance on a scratch-built feature remain, and more change moves through the pipeline [17]. Their reported results are narrower: sourcing, strategy and goals [4][2][3]. They also caution against the simple reading. The association is strong, they say, but it does not simply follow that building from scratch means a team has no plan [12].
In our view a snapshot also cannot say which way the link runs. A team with no strategy has no standard to buy against, so it may default to building.
The high-performer figures need care. The three percentages sum to 139.9% [18], so they are not slices of one population. We read them as the share of each approach group that counts as high performing. That is an inference from the sum.
On ephemeral environments, the authors put the explanation on the platform around the feature. They say the pattern likely reflects the platform: teams that have them tend to run on broad platforms with a range of automated delivery capabilities [6].
Differentiation was the least-cited of the five build-or-buy reasons listed, at 16.3% [9]. One respondent summarized the rule this way: "Build what makes us unique, buy or adopt what's standard, and always consider long-term cost and lock-in." [10]
What to watch
- Whether the full 2026 Future of Platform Engineering report publishes the two rates behind the 3.5x and its cutoff for 'predominantly'.
- Whether a later survey wave shows the scratch-builder gap moving as agent use grows.
Clarity's read
What the record supports and how the coverage leans. The claims behind it follow.
Reality
- Evidence50
- Adoption
- Insufficient
- Hype gap+20
- Incentives
- Insufficient
- Confidence55
Claim ledger
Ranked by verification strength, evidence, and original report placement.
- [1]
The 2026 Future of Platform Engineering report draws on responses from 379 platform professionals.
- [2]
Platform teams that predominantly build from scratch are 3.5x more likely to lack a clear platform strategy, according to the headline of the authors' article.
- [3]
Teams predominantly building from scratch meet fewer of their platform goals.
- [4]
Using an underlying tool is the most common primary approach to supporting a platform feature, at 51.3% of respondents, ahead of building on an existing template (31.2%) and building from scratch (17.5%).
- [5]
Among teams reporting an extremely positive effect of AI, more than three-quarters had ephemeral environments.
- [6]
The authors say the ephemeral-environment association likely reflects the platform: teams with ephemeral environments tend to run on broad platforms with a range of automated delivery capabilities around them.
- [7]
DORA's research found that organizational gains from AI depend on platform quality: strong platforms amplify the effects, while weaker ones dampen them.
- [8]
Asked to explain their build-or-buy logic, the most common response was a cost-benefit analysis, at 29.6%.
- [9]
Building for core differentiation was cited by 16.3% of respondents, the last of five build-or-buy reasons listed in the excerpt.
- [10]
One respondent summarized the logic: "Build what makes us unique, buy or adopt what's standard, and always consider long-term cost and lock-in."
- [11]
If all 379 respondents answered the sourcing question, the 17.5% building from scratch is about 66 respondents.
- [12]
The authors wrote: "The association is strong, but it is not as simple as 'build from scratch means you don't have a plan.'"
- [13]
The report gives high-performer figures of 58.8% for tools, 47.8% for building from a template and 33.3% for building from scratch.
- [14]
Coding agents remove the effort of writing code, but not the work of owning it.
- [15]
David Tuite has described a "build-versus-buy trap", in which teams stuck maintaining custom interfaces sink 6 to 18 months into upgrades and operational overhead instead of shipping value.
- [16]
The authors say AI makes build-versus-buy a sharper strategic question than ever.
- [17]
The maintenance, upgrades and governance that come with a scratch-built feature remain, while the volume of change moving through the pipeline increases.
- [18]
The three high-performer figures sum to 139.9%, so they cannot be shares of a single population.
Sources
1 independent publisher whose own reporting we read for this story.
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