The headline is built to look like a paradox, and the word doing the work is “despite”. A platform says machines write three quarters of its code, and in the same breath says it is hiring more engineers in Singapore. If you believe code volume is what engineers are for, that cannot be true. I have stopped believing that, and the four changes at the end of this piece are what happened to our own hiring plan as a result.
What was reported, and when
On 19 October 2026, Fintech Singapore published “iCapital to Hire in Singapore Despite AI Writing 75% of Code”, reporting an interview that chief executive Lawrence Calcano gave to The Business Times. The substance, as reported:
- iCapital, a New York headquartered alternative investments platform, plans to hire more product and engineering staff in Singapore even as AI generates 75 per cent of its code.
- The company uses AI for code generation, testing and quality assurance, not generation alone.
- Calcano on the rationale: “I think it is a significant opportunity for us to be more efficient and deliver more capabilities and user friendliness to clients.”
- iCapital has invested more than US$1 billion in technology since it was founded.
- Brooks Entwistle, newly appointed head of international, described Singapore as an important hub for the company global operations, with the office covering several business functions including sales.
- Its 2026 Asia Advisor Survey found risk analytics to be the top technology priority for Asian advisers, with compliance the fastest growing challenge.
That last point is not decoration. It explains the hiring. Risk analytics and compliance work is where correctness is expensive and the cost of being wrong is external. Those are not domains where you reduce the number of people who understand the system.
Our expert take 1: the constraint moved, it did not disappear
Think of a feature as passing through four stages: deciding what to build, producing an implementation, judging whether that implementation is right, and living with it afterwards. A generation tool compresses exactly one of those four. The other three are unchanged, and the third gets harder, because more candidate code now arrives per engineer per week and every line of it still needs somebody who can approve it.
This is why the arithmetic that says “75 per cent generated means a quarter of the engineers” fails. It assumes the bottleneck was typing. In practice the queue just relocates to review, where it is much harder to see: there is no obvious dashboard for “diffs waiting for someone who understands this subsystem”, and the symptom appears weeks later as a rise in defects that passed review quickly rather than as a visible backlog.
The failure is invisible for weeks: it shows up as defects that passed review quickly, not as a visible backlog.
Our expert take 2: the stated use of AI for testing is the detail worth copying
Almost every account of this subject stops at generation. The report says iCapital uses AI for “code generation, testing and quality assurance”, and the ordering of those three is the interesting part, because applying the same tooling to verification is what keeps a high generation share from becoming a liability.
The asymmetry is worth stating plainly: generating an implementation and verifying one are different tasks with different failure characteristics. Generation fails by producing something plausible and wrong. Verification fails by passing something plausible and wrong. If you accelerate the first without the second, you have built a machine for shipping plausible wrongness at speed, and in a domain where the published priority is risk analytics and compliance, that is the expensive kind of mistake.
A team that automates generation but not verification has not increased its output. It has increased the rate at which unverified work reaches the people downstream.
Which is why a hiring plan that follows this pattern raises test and quality headcount rather than cutting it. If you are reassessing where your own quality capacity sits, the structure we use is in our guide to building a dedicated QA engineering team in Singapore.
Let us discuss it: where is your queue actually sitting?
Tell us your current team shape and how much of your code is machine generated. We will tell you which of the four stages is now your constraint, and what the next two hires should be. Platform engineers | QA engineers
Get 3 Free Developer ProposalsOur expert take 3: do not benchmark against the 75 per cent
The number will travel further than the article, and it will be quoted in planning meetings as a target. It should not be used that way, for three reasons.
First, the definition is not fixed. A share like this can mean any line a tool suggested and a human accepted, or only code merged without modification, and it may or may not include tests, configuration and scaffolding. Two companies reporting the same figure may be measuring quite different things.
Second, it is self reported, and there is no external audit of it. That is not an accusation, it is simply what the figure is: a company statement about its own practice.
Third, and most importantly, the achievable share depends on what you build. A platform assembling large amounts of standard application surface area will reach a high share honestly. A team working on unusual systems problems, tight performance constraints or heavily regulated edge cases will not, and that difference says nothing about the quality of either engineering organisation. A team that chases someone else percentage ends up generating code in the places where generation is least appropriate, which is a reliable way to raise your defect rate while reporting an improvement.
No definition here is dishonest. That is exactly why the figure is a poor benchmark and a reasonable interview question.
What the figure does usefully establish is that this is mainstream practice at a serious platform, which makes it a fair subject to raise in an interview. For measuring whether any of it has actually made your own delivery faster, the only numbers worth trusting are your own: we use the approach in our note on engineering productivity metrics.
The four things we changed in our own hiring plan
| Change | What it replaced | Why |
|---|---|---|
| 1. Screen on judging code, not producing it | A blank-file exercise under time pressure | Everyone has a generation tool on the job. Reading a diff and finding what is wrong with it is the work now, and it is far harder to fake. |
| 2. Treat specification as a hire, not a task | Assuming product requirements arrive complete | When implementation compresses, vague specifications become the binding constraint within a quarter. Somebody has to own that. |
| 3. Raise test and quality capacity alongside generation | Cutting QA because “the tool writes tests” | Verification and generation fail differently. Accelerating one without the other ships plausible wrongness faster. |
| 4. Protect subsystem depth in the seniority mix | Hiring for breadth on the assumption tools cover depth | Review capacity is not generic. It requires someone who knows the subsystem well enough to reject a confident diff. |
The fourth is the one we underestimated. Review capacity sounds fungible and is not: a reviewer who does not know a subsystem deeply tends to approve work that looks competent, which is exactly the failure mode generated code produces. If you are rebuilding a technical interview around judgment rather than production, the panel side of that change is in our guide to training an engineering interview panel.
Two caveats
This is one company, in one segment, reported through an interview. An alternative investments platform with over a billion dollars of technology investment behind it has a code profile most teams do not share, and nothing in the report quantifies how many people will be hired or into which roles beyond product and engineering.
And the second: a stated intention to hire is not hiring. It is a public commitment made by an executive expanding into a market, which is useful signal about direction and weak evidence about volume. Treat it as a confirmation that the generation-plus-hiring combination is a real strategy rather than a contradiction, and keep measuring your own funnel.
FAQ: AI code generation and engineering headcount
What did iCapital actually say about AI and hiring in Singapore?
According to Fintech Singapore on 19 October 2026, reporting an interview chief executive Lawrence Calcano gave to The Business Times, the New York headquartered alternative investments platform plans to hire more product and engineering staff in Singapore even though AI generates 75 per cent of its code. The company uses AI for code generation, testing and quality assurance. Calcano framed it as an efficiency and capability gain rather than a headcount reduction, saying it is a significant opportunity to be more efficient and deliver more capabilities and user friendliness to clients. The report also notes iCapital has invested more than one billion US dollars in technology since it was founded, and that Brooks Entwistle, its newly appointed head of international, described Singapore as an important hub for global operations.
If AI writes most of the code, why would a company hire more engineers?
Because generating code was never the scarce part. When the cost of producing a plausible implementation falls close to zero, the work that remains is deciding what should be built, judging whether what was produced is correct, and carrying the consequences when it is not. Those activities do not get cheaper with a higher generation share, and several of them get more expensive, because there is now more code arriving per engineer per week that somebody has to understand well enough to approve. A team that raises its generation share without raising its review and testing capacity has not become faster. It has moved its queue from writing to reviewing, where the queue is harder to see.
Is a 75 per cent code generation figure comparable between companies?
No, and it is worth resisting the temptation to benchmark against it. Published shares of this kind are self reported and company specific, and the underlying definition varies enormously: some count any line a tool suggested and a human accepted, some count only code merged without modification, some include tests and configuration while others exclude them. A company whose product is largely standard application surface area will naturally report a higher share than one working on unusual systems problems, with no difference in engineering quality between them. Treat a figure like this as evidence that the practice is mainstream in that organisation, not as a target your own team should match.
Does this change what we should screen for when hiring developers in Singapore?
Yes, in one specific way. The ability to produce a working implementation from a blank file is now a weaker signal, because the candidate will have a tool for that on the job and so will everyone else. What has become more informative is judgment applied to code the candidate did not write: can they read a generated diff and say what is wrong with it, do they notice an unrequested behaviour change, can they tell you which of two plausible implementations will be cheaper to maintain. Those are testable in an interview, they are difficult to fake, and they map directly onto the work the role now involves. The practical change is usually to the format of the technical stage rather than to the bar itself.
Discuss it with us this quarter
Send us the two roles you are about to open and the share of your code that is machine generated. We will come back with the shape of the pool and a technical stage that tests judgment rather than typing. React developers | Data engineers
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Source: Fintech Singapore, “iCapital to Hire in Singapore Despite AI Writing 75% of Code”, 19 October 2026, reporting an interview given by chief executive Lawrence Calcano to The Business Times. The 75 per cent figure, the quoted sentence, the technology investment figure, the survey findings and the remarks attributed to Brooks Entwistle all come from that report. The four-stage model of the delivery pipeline, the four changes to our hiring plan and the argument about verification failing differently from generation are our own analysis. No study is cited and none is implied.