AI is changing how software gets built. Tools like Claude and ChatGPT can scaffold pages, propose components, and even fix small bugs. For quick prototypes and internal tools this is fantastic. But here is the line that matters for any serious platform that handles money, personal data, or regulated workflows: you cannot run a bank on AI software.

AI is great at scaffolding, not at building systems

From my own experience, AI assistants can spin up routing, models, controllers, tests, and basic UI in minutes. I have used Claude to generate boilerplate, write utility functions, and patch straightforward bugs. That saves time and reduces friction.

The cracks appear when the work becomes genuinely complex. Cross system integrations, nuanced business rules, performance tuning under real traffic, and long term maintainability are not solved by prompts. AI does not know your institution specific constraints, risk appetite, data lineage, or compliance obligations. That context is where engineering judgement lives.

The full development lifecycle still needs humans

Software is more than writing code. A real product moves through a lifecycle that requires human decision making at every stage:

1. Discovery and planning: requirements, stakeholder alignment, regulatory mapping, threat modeling.
2. Architecture: data models, service boundaries, secure data flows, choice of frameworks and cloud primitives.
3. Implementation: coding standards, code reviews, patterns that fit the domain. AI can help here but it does not decide what the implementation should be.
4. Testing and QA: unit tests, integration tests, property based tests, user acceptance testing, accessibility checks, regression control.
5. Security engineering: secrets management, least privilege, key rotation, dependency vetting, static and dynamic analysis, penetration testing.
6. Deployment and operations: CI and CD, blue green or canary releases, observability, incident response, rollback strategy.
7. Maintenance and evolution: patching, refactoring, migrations, performance work, deprecations, backlog grooming.

AI can assist within these phases. It cannot own them. A developer is not only a coder. They are an architect, reviewer, tester, and steward of long term reliability.

Security cannot be automated away

This is the non negotiable point. Even the best AI will confidently produce code that compiles but is unsafe. Without a human reviewer you do not know what you are putting into production. Examples of risks that routinely slip through AI generated code:

• Authentication and authorization bugs: missing checks, confused deputies, privilege escalation through poorly scoped endpoints.
• Input and data handling flaws: SQL and NoSQL injection, unsafe deserialization, path traversal, mass assignment, weak validation and sanitization.
• Secrets and configuration: credentials in code, over broad IAM roles, insecure default configs, disabled TLS verification during quick tests that never get fixed.
• Supply chain issues: unvetted dependencies, outdated libraries with known CVEs, transitive packages pulled in by convenience.
• Compliance gaps: POPIA and GDPR data minimization and retention, PCI DSS handling of cardholder data, auditability and logging that actually stands up to review.
• Operational security: missing rate limits, weak anti automation controls, insufficient monitoring and alerting, lack of disaster recovery drills.

Bottom line: never ship AI generated code without human review, tests, and security auditing. Money, identity, and privacy demand a human in the loop.

Real world complexity that AI cannot intuit

• Banking: multi factor auth flows tied to risk scoring, reconciliation with core banking systems, transaction dispute workflows, regulatory reporting, and audit trails that must be tamper evident.
• Healthcare: consent models, data segmentation, clinical safety cases, immutable audit logs, and jurisdiction specific privacy obligations.
• E commerce at scale: tax rules per region, fraud prevention, chargeback handling, inventory synchronization, and logistics integrations that behave under peak traffic.

These are not template problems. They are domain problems. They require conversations with stakeholders, trade offs, and careful engineering.

Prototyping and vision is where AI shines

AI is excellent for turning ideas into something tangible quickly. Many clients now bring a prompt crafted demo, a wireframe, or an AI generated proof of concept. That accelerates alignment and shortens feedback loops.

Our job as developers is to take that vision and make it production ready. We harden the architecture, secure the data paths, validate assumptions with tests, and build the integrations that make the system real. AI helps us move faster. It does not replace the responsibilities that keep users safe.

My experience using AI on real projects

• What works well: scaffolding modules, generating CRUD, writing small utilities, producing first draft tests, explaining unfamiliar library APIs, refactoring repetitive code, and fixing simple bugs.
• Where it struggles: multi service data modeling, performance tuning under realistic load, thorny race conditions, nuanced auth rules, complex migrations, and anything that requires a deep understanding of business constraints.

In other words, AI is a powerful assistant. It is not an autonomous engineer.

Cheaper and faster means more development, not less

As AI trims the cost and time for early stages, demand rises. More teams can afford to prototype, iterate, and ship. The result is not the end of development. It is an expansion of it. Developers become AI fluent builders who deliver better outcomes in less time, while still carrying the accountability for correctness, security, and reliability.

Conclusion

Use AI to move faster. Use it to explore options, draft components, and test ideas. Then rely on experienced developers to design the system, validate the risks, secure the edges, and carry the product through its full lifecycle.

When trust, money, and safety are on the line, the rule stands: AI can help, but you cannot run a bank on AI software.