The Winds of Change: How AI Will Reshape Software Engineering

We are at a pivotal moment in the evolution of software engineering, an inflection point in terms of how software engineering is practiced. Using AI to automate code development is just the next step on the journey of abstraction we have been on since Fortran & Algol arrived in the early 1950's. AI coding assistants - tools that can generate code, spot bugs, and even suggest design improvements with human-like intuition - are poised to fundamentally transform how we build software. Many experts predict that within this decade, AI will automate a substantial portion of coding work.

This rapid progression will undoubtedly impact software teams. In this post, we will explore how AI coding assistants will reshape workflows, skillsets, costs, and team dynamics in the years ahead.

The Productivity Multiplier?

The most immediate advantage of AI coding assistants is a boost in developer productivity. These tools can reduce repetitive, mechanical work - freeing up precious time for higher-value tasks.?

Mundane responsibilities like tracing down simple bugs, translating specs into basic code, resolving merge conflicts, code reviews for style violations, and documentation are prime candidates for automation. Developers spend a non-trivial portion of their day on such rote work. A smart AI assistant could lift that burden. ?

And the time savings stack up quickly. If a developer gets back even 2 hours from an 8-hour workday, she now has 25% more capacity for creative, strategic work. The multiplier effect on overall productivity and output for engineering teams is immense.?

As AI coding matures, these productivity gains will only grow. Virtually any coding task with clear rules and patterns is fertile ground for automation. And cutting-edge AI tech like Codex and GitHub Copilot already showcase how advanced algorithms can generate entire functional components on their own.

The Democratisation of Coding?

AI will also make coding more accessible to non-developers.?

Low-code and no-code tools already enable non-technical people to assemble apps from ready-made building blocks. AI assistants will now let them simply describe what they want in plain English. The AI will translate requirements into working skeleton code.

Citizen development platforms like Airtable and Retool couple such intuitive interfaces with modular code components. This allows team members with zero coding skills to build custom business apps.?

AI supercharges this democratisation - expanding the possibilities drastically. Domain experts like business analysts, support reps, and digital marketers could gain the power to prototype and iterate on apps tailored to their unique needs. Coding skills may no longer remain a bottleneck.

This has crucial ramifications for software teams. Instead of explicit hand-coded instructions, specifications will increasingly come as abstract user stories refined through rapid iterations. Cross-functional collaboration will become more integrated into the development process itself.

The new age software team is likely to consist of polymaths combining both tech and business expertise.

The Impact on Costs? ?

The boost in productivity and accessibility has clear cost benefits too.?

AI coding assistants allow faster development with smaller teams. Legacy workflows typically have delays while specs get translated to code, developers wait on reviews, and technical debt accumulates. AI eliminates the bulk of this waste.

We may no longer need armies of developers, quality assurance staff, technical writers and interaction designers to get the same output. Forrester forecasts the global loss of 9% of software jobs by 2025 due to AI.?

The time-to-market for the average application could drop significantly too. Together with the cloud infrastructure for rapid deployment, quick experimentation with minimal commitment becomes possible.?

The reduced costs change the economics for software teams. We may see budget shifts from pure development to business analytics - since rapid prototypes provide the opportunity to quickly validate ideas in the market and double down on what users want.

Staffing ratios could change, with more product experts and fewer pure coders. Executives may fund a portfolio of "shots on goal" instead of multi-year enterprise software projects. Risk-taking may become more acceptable.

Adaptability is Key

However, developers cannot afford to be complacent. As mundane work gets automated, new human skills become mandatory.

Software architecture, complex logic, safety and security analysis, product conceptualisation - these responsibilities do not easily lend themselves to automation. Developers need a solid grasp of computer science fundamentals rather than just familiarity with tools and languages.

Understanding context and user needs is crucial when specifications are abstract instead of detailed technical docs. Developers must collaborate more tightly with business teams - perhaps even transitioning into hybrid product manager roles.

In essence, future developers will operate at higher levels of conceptual thinking and complexity. They have to guide the AI tools - not get replaced by them. The key is adaptability and a hunger for creative challenges rather than rote work.

The need for advanced software engineering skills and experience will likely grow rather than shrink over the next decade. The shortage for senior talent will only get more severe. Juniors and mid-level developers not upskilling risk losing relevance.?

Final Thoughts ?

AI-powered coding assistants surely seem like something out of science fiction for legacy enterprises where change happens gradually. But agile digital natives like startups already encode product requirements as automated tests and deploy new builds daily. For them, automated coding is a natural next step.

The benefits are too substantial to ignore - improved productivity, democratized innovation, faster experimentation. Software teams that don't at least start experimenting with the technology risk being left behind.

Of course, concerns around security, data privacy, bias and job losses warrant careful consideration too. But as with past industrial revolutions, we must adapt to changing landscapes.

Rather than react with fear at losing jobs, software teams should proactively lead the charge in responsibly adopting AI coding. Whatever challenges this transformation brings, technology progress as a whole rarely flows backwards. AI has earned its place as an indispensable partner in building the future of software.