AI-Powered Coding Tools Are Here to Help—Not Harm—Your Job, Insist IT Experts
Even as code development tools get more sophisticated, thanks to generative AI, vendors and other tech pros say nothing replaces the human touch
3 min
Generative artificial intelligence tools designed to automate the process of writing computer code are unlikely to offset a shortage of software engineers—let alone put them out of a job, enterprise-technology leaders said.
Demand for software engineers, developers and programmers has long outpaced supply. In recent months, generative AI has given chief information officers and other corporate IT managers the ability to automate some tasks in the software-engineering life cycle, said Rafee Tarafdar, chief technology officer at Infosys, a business consulting, information technology and outsourcing services firm.
But AI tools aren’t yet sophisticated enough to build working business apps, Tarafdar said. While certain tasks may become outdated as AI-powered coding assistants take them over, “skilled coders will be needed to oversee generated code and documentation,” among other roles, he said.
Tarafdar said he is currently training the company’s engineers and developers to use automated coding tools, including Infosys’ internally built AI coding assistant.
Sparked in part by the popularity of OpenAI’s ChatGPT chatbot, released in November, veteran business software companies, as well as a growing number of tech startups, have been rolling out software applications over the past few months that leverage generative AI technology to write blocks of code from scratch. Trained on massive amounts of data, generative AI tools are designed to produce text, images and code based on users’ natural-language prompts.
Generative AI sales are expected to reach $3.7 billion this year, expanding by an annual growth rate of 58% to an estimated $36 billion by 2028, according to market researcher S&P Global Market Intelligence. Tools designed to generate code are the fastest-growing category, said S&P managing analyst Nick Patience. That growth reflects a dearth of software engineers, which can hinder business growth, Patience said.
Until recently, low- and no-code software development platforms, which are designed to require minimal input to develop apps, were among the few ways employers could bridge that gap, said Jithin Bhasker, a general manager and vice president at a cloud-based enterprise software firm ServiceNow. “Generative AI will empower every employee to build and deploy automation at scale,” he said.
Despite growing interest for the tools among companies across industries, it is still early days for adoption, with many use cases still in pilot. CIOs have voiced concern that tools designed to lower the bar for code creation could lead to increased technical debt and orphan code. Technical debt refers to imperfect technology deployed to meet immediate needs with the knowledge that its imperfections will require redress in the future.
Still, tech companies are moving fast to capture a share of the market. Databricks, a data-storage and management vendor, on Wednesday released a generative AI tool designed to enable employees to use natural-language prompts, rather than code, to mine a company’s data for business insights—handling a task typically left to data scientists and programmers.
But it isn’t meant to replace them outright, said Databricks Chief Executive Ali Ghodsi. By handling code, the tool allows developers to focus on more innovative, proactive projects, while employees outside of tech hubs still have access to business data without the need for special training or coding skills, Ghodsi said.
Similarly, an AI-powered coding assistant launched in March by software firm Sourcegraph is designed to answer users’ technical questions, fix bugs in existing code and generate new code. It is meant to enhance the work of engineers and developers, said CEO Quinn Slack, adding that developers will be freed up to perform higher-level projects, rather than get bogged down by endless lines of basic code.
Thomas Dohmke, CEO of Microsoft-owned coding-collaboration platform GitHub, said that more than 20,000 organisations are currently using GitHub Copilot, a code-generating tool created in partnership with OpenAI and launched last year. In March, GitHub released a ChatGPT-like version of the tool, designed to enable users to interact with the tool through natural-language prompts.
Dohmke said companies are using the new tool for everything from explaining blocks of code to proposing fixes for bugs. “Technology that is not sentient cannot replace human creativity, it can only help deliver it,” Dohmke said. “Right now, AI is really just a probability machine, a co-pilot that is symbiotically dependent on its human pilot to build the world’s software.”
Vlad Magdalin, co-founder and CEO at Webflow, which sells cloud-based software for building and hosting websites, said he has embraced the new automated coding tools. Speaking this week at Collision, a technology conference in Toronto, Magdalin said simplifying the task of writing code saves time and raises expectations of productivity for developers. “It doesn’t mean that a developer is working 30 hours fewer,” he said.
“It’s not a magical tool that removes the need for a human,” Magdalin said.
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From making unpopular decisions to learning when to listen, leaders from five businesses reveal what nobody prepared them for — and how running a company has challenged their expectations.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
4 minA sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.
The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.
The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.
The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.
This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.
The debt behind the AI build-out
Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.
Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.
JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.
The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.
That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.
A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.
When interest rates rise, those assumptions become less forgiving.
Inflation changes the calculation
September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.
The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.
CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.
Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.
Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.
The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.
China adds another source of pressure
Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.
Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.
The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.
That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.
A reset rather than the end of the boom
The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.
What has changed is the market’s willingness to overlook the price and financing structure of that growth.
The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.
More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.
For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.
Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.
The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.
Fact box
Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026
Philadelphia Semiconductor Index: Down approximately 11.4 per cent
Micron Technology: Down approximately 14.6 per cent
Samsung Electronics: Down approximately 18 per cent
SK Hynix: Down approximately 24 per cent
US September inflation: 3.7 per cent annually
Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion
Technology and semiconductor shares fell sharply as investors questioned the debt financing the AI infrastructure boom and confronted higher rates and Chinese competition.
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