As is our usual custom, we are back again with our annual highlight of software trends and predictions.
This iteration of the industry outlook article aims to explore key insights from the software industry for the year ahead, with the goal of highlighting emerging trends in 2025 while trying to predict an outcome for the industry in the coming year.
In our previous industry outlook article, we made a number of claims, including that “The growing demand for interoperable data systems that can handle diverse workloads without being constrained by a specific platform is leading businesses to operate their data layer independently of dedicated storage or computational resources”
And that as such “As companies increasingly seek to unify disparate datasets and accelerate their analytics capabilities, it’s safe to say headless data architecture is bound for a continued rise in 2025.”
Going further, we made the following prediction specifically about quantum: With the pace of innovation in hardware, algorithms, infrastructure, and research in the field, we have reasons to believe that Quantum computing’s future looks brighter than ever and 2025 might just be the tipping point for real-world quantum applications.
As 2025 winds down and we look back on these predictions this month, we again are pleased to say that a few of our observations, including the two listed above, were right on the money.
First, Let’s Talk Computing
For a start, in 2025, researchers made even more headway in their quest to circumvent the fundamental problem with quantum computing.
As seen in the launch of the world’s 1st modular quantum computer, which uses photonic qubits as opposed to traditional ones, making it operable at room temperature, specialist computing infrastructure once needed for quantum computation is about to be cut out, paving the way for broader mainstream adoption.
“Traditional qubits, or superconducting qubits, are the building blocks of quantum computing and hold the key to processing massive amounts of data quickly.
But these qubits use microwave signals to help process data, which creates heat that can damage hardware. Further, current cooling methods, which are used to create a near absolute zero computing environment, also damage hardware and make accessing machines difficult. —-Ibid.
In the same vein, quantum computing as a subset of the larger computing sector also saw a shift of investors’ focus away from research-focused quantum startups to businesses that can prove that they can use quantum to solve real-world problems, and this is leading to applications of quantum in, for instance, industry such as AI for artificial intelligence training.
What both of these mean in essence is that we are at the precipice of widespread quantum computing application, which is coincidentally converging at the rise of the larger computing landscape.
Furthermore, as an example that captures the rise of larger computing, there are recent reports that US chip maker Nvidia is planning to increase the production of its H200 chip due to robust Chinese demand following the lifting of a previous ban on exports to the country.
This, of course, is in addition to the increase in datacenter investment witnessed this year from industry heavyweights across the US, EU, and China.
Fierce AI Supremacy Battle
Naturally, the rise in computing investment as well as the simmering of geopolitical tension between the US, EU, and China could see 2026 filled with a tense battle for AI supremacy in continuation of the presently intensifying struggle for consumer mindshare.
Already, Nvidia CEO, for instance, has started on the course to motivate the need for investment as well as government support for American AI technology branching out to places such as India, Europe, Latin America, and Africa.
Here’s the foremost political commentator, Arnaud Bertrand’s comment on this: [Jensen Huang] says that the chip export controls on China were one of the most self-destructive decisions ever taken by the US government. In a separate interview (linked below) he effectively says that might have lost the US the AI race. Because, as he puts it, «winning» the AI race means that «80% of the world uses the American tech stack» and that, given that China on its own is «50% of AI research» and «30% of the technology market», then them not using the American tech stack means that by definition America is «forfeiting and conceding» the AI race.
When you consider the strategy of Chinese AI companies and the amount of ground they have gained in prime locations in comparison to their American counterparts, it’s almost entirely safe to say that the next year will undoubtedly be packed with suspense as well as interesting surprises for tech consumers.
Generative AI for Software Development
Going further on AI, the 2025 Forrester Developer Survey highlighted the growing desire to incorporate more AI into the software development lifecycle.
According to the survey, using AI and genAI in the software development lifecycle (SDLC) bubbled up as a top priority for developers, CTOs, and founders alike in 2025, alongside using more cloud-native technologies and improving software security.
The research also surveyed adoption rates at various points of SDLC and found that coding and testing were the top use cases for leveraging AI (48% and 47%, respectively), with more and more people interested in using AI for finding development insights, at 33% of respondents.
Vibe Coding and Natural Language Become the Default
If 2025 was the year AI-assisted coding went mainstream, 2026 is shaping up to be the year natural language becomes a first-class programming interface.
What began as autocomplete and code suggestions has evolved into something far more profound: developers increasingly describe intent, not implementation.
The rise of what many now call “vibe coding” reflects this shift.
Instead of meticulously specifying every instruction, engineers communicate high-level goals, constraints, and desired outcomes, allowing AI systems to translate those into working code.
And contrary to popular belief, this doesn’t eliminate technical rigor; rather, it relocates it.
Consequently, in 2026, architectural thinking, system design, and correctness constraints may become more important than syntactic fluency in any one language.
We already saw early signs of this in 2025, with tools that could refactor entire codebases, generate tests from specifications, and reason across repositories. In 2026, these capabilities are likely to mature into default workflows.
Natural language specifications, design documents, and even Slack threads will increasingly serve as executable inputs to development systems. Code, in many cases, will become an intermediate artifact rather than the primary source of truth.
This shift has two immediate implications. First, it lowers the barrier to entry for building software, allowing smaller teams and non-traditional builders to create increasingly sophisticated systems.
Second, it raises the premium on clarity of thought. Poorly defined requirements now scale faster than ever, making product sense, domain knowledge, and critical reasoning indispensable skills for modern engineering teams.
The Rise of Agentic Development Workflows
Alongside vibe coding is the rapid normalization of agentic workflows. Rather than relying on a single AI assistant, development teams are beginning to orchestrate multiple specialized agents—each responsible for planning, coding, testing, security review, or performance optimization.
In 2025, this was largely experimental. In 2026, however, it is poised to become standard practice, especially in larger organizations.
Agentic systems reduce context switching, automate routine work, and enable parallel execution across the SDLC, and as such, developers may increasingly act as supervisors and reviewers of AI-driven workstreams rather than sole authors of code.
Furthermore, this trend is forcing companies to rethink governance, accountability, and tooling. As agents gain more autonomy, organizations must answer difficult questions about auditability, trust, and liability.
Expect 2026 to bring a stronger emphasis on AI observability—tracking what agents did, why they did it, and how decisions were made—alongside stricter controls around access and deployment.
Headless Architecture Becomes Operationally Mandatory
Our earlier observation about headless data architectures proved prescient, but the story has evolved.
In 2026, decoupling data, compute, and application layers will no longer be a competitive advantage—it will be an operational necessity.
As AI-driven workloads proliferate, organizations are finding that tightly coupled systems simply cannot scale. Training models, running inference, serving real-time analytics, and supporting traditional applications all place fundamentally different demands on infrastructure.
Headless architectures allow companies to route workloads dynamically, optimize costs, and avoid vendor lock-in at a time when flexibility is paramount.
This architectural shift also aligns with geopolitical realities.
As data sovereignty rules tighten and cross-border data flows become more regulated, organizations need the ability to localize storage while maintaining global intelligence.
We expect 2026 to see further investment in federated data systems, composable analytics stacks, and policy-aware infrastructure layers.
Software Security Moves Left—and Up
Another defining trend for 2026 will be the elevation of software security from a technical concern to a board-level issue.
The combination of AI-generated code, increasingly complex supply chains, and geopolitical cyber risk has made traditional, reactive security models untenable.
In response, security is moving both left—earlier in the development lifecycle—and up, into organizational governance. AI-assisted threat modeling, automated dependency analysis, and continuous compliance checks are becoming embedded in day-to-day development workflows.
At the same time, executives are demanding clearer metrics around risk exposure, code quality, and system resilience.
Notably, AI is both the problem and the solution here. While it accelerates development, it can also propagate vulnerabilities at scale.
Thus, the companies that succeed in 2026 will be those that pair AI-driven productivity with equally robust AI-driven safeguards.
A More Fragmented, More Competitive Software World
Taken together, these trends point to a software industry that is simultaneously more powerful and more fragmented.
The barriers to building software are falling, but the strategic stakes are rising. Geopolitics, infrastructure constraints, and talent shortages are shaping technology choices as much as innovation itself.
In 2026, success in software will depend less on adopting any single tool or platform and more on how well organizations integrate people, agents, data, and infrastructure into coherent systems.
The winners will be those who can navigate this complexity with clarity—embracing AI not as a replacement for human judgment, but as a force multiplier for it.
As the industry heads into another year of rapid change, one thing is clear: software is no longer just about code.
It is about orchestration, intent, and the ability to adapt in a world where technological, economic, and political forces are increasingly intertwined.
