Nigeria built credible payment infrastructure by taking reliability seriously. As AI-generated code spreads through Nigerian and UK businesses, that lesson matters more than the tooling itself.

In a recent interview with Vanguard, software engineer Adeoye Remi was asked a question that most technology teams are now quietly asking themselves: will artificial intelligence replace software engineers? His answer was more useful than a yes or a no. AI will automate a great deal of what engineers do today, he argued, but it does not remove the need for engineering judgement.

Someone still has to understand the architecture, evaluate what the tool produced, weigh security and reliability, and take responsibility for the system when it comes under pressure. For most businesses, the practical version of that question is narrower and more urgent: once AI generated code is running in production, who owns it?

Remi has spent more than a decade in Nigerian fintech, across Flutterwave, Korapay, and now Paystack, building the kind of payment infrastructure that fails loudly and publicly when it fails at all. That background is worth noting, because the distinction he draws is easy to agree with and difficult to put into practice.

In our work with businesses in Nigeria and the United Kingdom, it is also the most expensive thing a growing company gets wrong when it starts adopting AI tooling.

Nigeria’s payment story was never about possibility

Remi makes a second point in the same interview that deserves more attention than it will get. Nigeria, he says, has moved past the phase of simply making digital payments possible. The live conversations now are about cross-border flows, settlement, interoperability and infrastructure that can carry businesses across several markets at once.

The numbers back him up. NIBSS data published in early 2026 put annual volumes on the NIBSS Instant Payments platform at 11.2 billion transactions, roughly 120 per cent above 2022 levels, with value crossing the one quadrillion naira mark.

In August 2026, NIBSS began rolling out the National Payment Stack, an ISO 20022 compliant rail intended to replace the fifteen year old NIP system, reporting 26.55 million transactions worth 1.4 trillion naira across 48 participating institutions in its early phase.

None of that is a story about clever features. It is a story about reliability. Settlement windows, failed transaction handling, reconciliation, sanctions screening, real-time risk scoring: unglamorous engineering decisions, made deliberately, by people who could explain why they made them.

“Reliability is particularly important because you are dealing with people’s money” – Adeoye Remi, speaking to Vanguard, September 2026

Replace the word money with customers, inventory, patient records or payroll and the sentence holds for almost every business we work with. It is also the standard against which AI generated code should be measured, because a system that cannot be explained cannot be defended when it fails.

What AI-generated code removes, and what it quietly leaves behind

AI coding tools are genuinely fast, and the productivity gain is real, not marketing. The problem is what scales alongside it. Research published by the Cloud Security Alliance in April 2026 found that AI-assisted developers in large enterprises produced commits three to four times faster than their peers, while introducing security findings at roughly ten times the rate. Veracode’s testing of more than one hundred large language models found that around 45 per cent of AI-generated code samples introduced weaknesses from the OWASP Top 10, a pass rate that had not meaningfully improved across successive testing cycles into 2026.

Gartner puts the longer trend more bluntly, predicting that prompt-to-application development by non-specialists will drive a 2,500 per cent increase in software defects by 2028, with the remediation cost consuming budgets that were meant to fund innovation. In its second quarter emerging risk survey for 2026, Gartner also found that senior executives now rank AI-driven discovery of vulnerabilities as the leading emerging risk facing their organisations.

Read together, those findings say something narrow and important. The speed of production has increased sharply. The rate at which sound, defensible decisions are made has not moved at all.

The judgement gap shows up as business risk, not as a technical report

For a small or medium-sized business, AI-generated code never fails with a label attached. It fails as an incident.

A payment integration passes every test in staging and then double-charges customers when the network retries a request. A customer database is exposed because generated authentication code omitted a permissions check that nobody was assigned to review. A month-end job fails, and no one on the team can say what it was meant to do, because the person who prompted it into existence is no longer with the company and never drew the architecture.

In each case, the code was not the failure. The absence of an owner was. Nobody had been made accountable for how the system behaves when something goes wrong, which is precisely the work Remi describes as irreducible.

Four questions to ask before AI generated code reaches your customers

• Who owns the failure modes? Not the vendor, not the tool, not “the team”. If a transaction fails at two in the morning, one named person should know it has failed and know what happens next.
• What happens on the second attempt? Retries, duplicate submissions and partial failures are where generated code is weakest, because they are the cases no prompt describes and no demo exercises.
• Who reviewed the security-relevant paths? Authentication, payments and anything touching personal data under the Nigeria Data Protection Act deserve a human review by someone qualified to say no.
• Can you explain the architecture without opening the tool? If nobody in the business can draw how the system fits together on a whiteboard, the business does not own the system. It is renting an outcome it cannot maintain.

These are not sophisticated questions. They are the questions that separate a system a business can grow on from one it will have to rebuild in eighteen months.

The next phase belongs to the people who understand systems

Remi’s advice to young Nigerians entering technology is to stop chasing programming languages and learn to solve problems, understand how systems work and communicate clearly, because tools change and fundamentals do not. The same advice, translated for a business, reads roughly like this: do not buy output, buy judgement.

Nigeria’s payment infrastructure earned its credibility because a generation of engineers treated reliability as a requirement rather than a feature. Businesses adopting AI now face the same choice on a smaller scale and a shorter timeline. The organisations that come out of this period well will not be the ones that shipped the most AI-generated code. They will be the ones that could still explain, defend and repair what they shipped.

Running AI assisted software in production and want an engineering review before it reaches your customers? Cloud Technology Hub works with businesses across Nigeria and the United Kingdom on cloud, software and managed IT delivery.

Speak to our team at info@technohub.cloud. TALK

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