Q and A with Neil Millard - 14th May 2025 - A.I edition

Session summary: a live Q&A on AI for developers and business — is it hype or does it help, how AI is changing day-to-day development workflows, and where it creates real business value versus where the claims outrun the substance.

It's 12:00, it's Wednesday — that can only be one thing, it's the Tech Answers Club Wednesday Q&A. So what are we talking about this week? AI for developers and business — is it hype, does it help, is it a game-changer? Artificial intelligence is everywhere, but what does it really mean? Today's focus is how AI is changing development workflows and creating real business value, plus what's coming next.

What is AI?

A broad term for machines doing stuff that typically requires human intelligence — problem solving, pattern recognition (they're really good at that), natural language processing, and decision making (I'll reserve judgment on that last one, unless it's really just pattern recognition in disguise). AI isn't magic, though — it's just maths, data, and models.

Types of AI

• Rule-based AI — basic if-this-then-that logic, programmers have been doing that for ages.
• Machine learning — systems that learn from data.
• Deep learning — like machine learning, but more of it.
• Generative AI / large language models — similar to deep learning, but with very specific use cases.

Pattern recognition

Identifying trends, anomalies, and matches in data. I've worked on projects with huge amounts of website usage data — you hover over a product without clicking, then go somewhere else and click something different, and that hover gets logged. Pattern recognition can spot that when 300 people hover over that product, they tend to do a particular thing afterwards. It's a bit like if-then logic, but the machine searches for the pattern instead of a programmer hand-coding it.

Machine learning

Takes that a step further — models are trained on data to improve their performance, so if we feed back what customers actually did after a recommendation, the model refines its future outputs and improves as it goes.

Large language models and neural networks

LLMs rely on advanced neural networks — a simple, much smaller model of a brain, more fly than human. Truth be told, nobody fully knows how they work — we've successfully modelled things as simple as a worm's brain, and we know exactly how that works, but once you get up to something as "advanced" as a fly, it gets murky, yet somehow it still works. A large language model is a bit like that: if you could talk to a fly and it understood you, and had read a vast amount of literature, that's roughly what an LLM is.

Benefits for developers

• Code generation and autocomplete — give it enough code to look at and it can guess the next few lines, a bit like predictive text on your phone. Sometimes it nails exactly what you were going for; sometimes it produces something syntactically valid but logically nonsensical.
• Debugging assistance and static analysis — static tools that check syntax and logic (unused variables, undefined functions) already existed; AI has effectively learned those rules too, and can go further — running tests, adding debug lines, reading error messages, and iterating. It doesn't always get there, and can occasionally spin in circles, but most of the time it does okay.
• Writing test cases — my favourite use. Whether you write tests first (TDD) or after the code, it's easy to miss scenarios (the "customer orders minus three beers" kind of edge case). Ask AI to generate scenarios you haven't thought of, based on your existing tests and code, and it's very good at making up plausible, creative edge cases — which is exactly what you want here.
• Documentation — it can read code and generate an explanation of what it does, which is useful for creating docs and for junior developers getting up to speed.
• Accelerating your workflow — rather than spending ages searching Stack Overflow, an LLM has effectively already been trained on it (right answers and wrong answers included), so it can get you unstuck faster.

Benefits for business

• Automating repetitive tasks — customer support chatbots, data entry, reporting, scraping websites into databases. There's growing interest in AI agents — giving a large language model a very specific role and letting it "stay in character" to carry out a task (which, unfortunately, is also exploited by spammers and scammers).
• Enhanced decision making with predictive analytics — AI has effectively read almost everything humans have written, and is good at spotting patterns across many similar documents and reports, summarizing them into something digestible, with a recommendation of what might happen next.
• Personalization at scale — in marketing and sales, this is why your Instagram or Facebook feed feels tailored to you: it's reinforcing what you already engage with, which is sometimes useful and sometimes just an echo chamber (YouTube even has a "show me something different" button buried on the homepage for exactly this reason).
• Cost savings and increased productivity — the sales pitch, at least. I haven't seen much hard evidence of this yet; the tech is still young, so we'll see.

Real-world developer use cases

• Refactoring legacy code and translating codebases — genuinely useful, e.g. upgrading old React code to a newer version. The catch is training data isn't always current, so it might default to an older API version it saw more of, rather than the very latest — and you know what happens when it just makes something up instead.
• ML for CI/CD monitoring and anomaly detection — feed pipeline data into a model and it can flag anomalies. For example, a Terraform pipeline that produces a plan — if a run suddenly shows costs jumping or dropping a long way from the norm, that's worth an alarm, since it might mean a load of expensive resources are about to be created, or a load of resources are about to be deleted.
• AI-assisted API generation, unit tests, and infrastructure templates — genuinely useful for making sure code has decent coverage, including scenarios nobody thought to write manually, catching regressions in CI before anything reaches production.

Real-world business use cases

• Retail — inventory forecasting and pricing, using pattern matching and prediction, even simulating customer personas to test messaging.
• Finance — the UK government uses a fair bit of machine learning to cross-reference large volumes of tax data for anomalies (sudden jumps, VAT/corporation tax mismatches), because it can scan millions of records far faster than people can.
• HR — CV screening and candidate ranking, including spotting CVs that look AI-generated or inconsistent against a candidate's LinkedIn profile.
• Manufacturing — predictive maintenance from sensor data, spotting patterns that predict a fault will occur down the line, across huge fleets of equipment.

Risks and challenges

• Bias and fairness — a model only knows what it's trained on; it's much stronger in English and Spanish than less-represented languages, which is a little ironic given LLMs descend from machine translation ("transformer") research.
• Hallucinations and accuracy — a model essentially never says "I don't know, let me find out" — it always produces an answer, and there's no reliable way to tell fact from confident fabrication from the output alone, because the model doesn't actually know facts, it knows how language works.
• Privacy and data governance — including live legal questions around AI training on copyrighted material, and whether that counts as fair use.
• Over-reliance on automation — if you lean on the machine to do something for you, you risk losing the underlying skill yourself, which is fine until the machine breaks and you're stuck.
• Ethics and governance / explainability — because we don't fully understand how these models work internally, sometimes challenging an answer gets you a correction, and sometimes the model just doubles down on something wrong.
• GDPR, data compliance, and responsible model training — where does the training data come from, and is the model learning things it shouldn't (personal data like emails and phone numbers)? This is why organizations are writing AI usage policies — should AI be allowed anywhere near production, for instance? Probably not.

Tools to get started

ChatGPT (LLM), Midjourney (image generation), Hugging Face (a huge repository of trained models), and OpenAI's APIs. For developers: TensorFlow, PyTorch, and LangChain — libraries for building your own models and neural networks. For business: Azure Machine Learning Studio, Google Vertex AI, and Microsoft Copilot.

What's next

• Image generation with genuinely legible embedded text — still a weak spot today.
• Voice response — getting close, though still a bit rough compared to text-based LLMs; voice cloning is already surprisingly easy now that the underlying pattern analysis exists.
• Real-time AI agents — give an agent a task, some boundaries, and access to tools, and let it act — still fairly limited by the tools available to it today.
• Vertical-specific models — trained on narrow domains (legal, medical) so they can be smaller and faster, without needing to carry knowledge of everything else.
• No-code/low-code and "vibe coding" tools — letting people with no coding background produce working code, which is powerful but can bite you hard if you don't understand what it produced. It's a tool, not infallible — a bit like the humans who built it. For developers, think co-pilot, not replacement — it's there to make you faster, not replace you, and the same goes for business: AI can make things faster and easier, not replace the judgment behind them.

Any private questions, find me on LinkedIn — that's all for today, back again in September after the summer break.