The tech industry is undergoing rapid evolution, driven by advancements in artificial intelligence, shifts in global political landscapes, and changes in market dynamics. These forces reshape how companies approach development tools, data infrastructure, regulatory technology, workforce strategy, and pricing models. As businesses adapt, new challenges and opportunities emerge, requiring thoughtful approaches to remain competitive and sustainable in this evolving landscape.AI in dev tools, such as code completion and agents capable of writing entire sections of a codebase, will continue to accelerate. This growth will be fueled by more engineers and managers recognizing the significant productivity gains these tools provide. As a result, engineers will spend marginally less time on repetitive boilerplate code and more time addressing complex problems with higher impact.AI-powered tools will also offer more significant insights into legacy codebases, enabling engineers to tackle complex migrations more effectively. However, the limits of these tools will start to be more widely understood. Engineers have context not just of the task at hand but also of the overall direction of a company and the interpersonal politics of a company that makes very real demands on architecture. Engineering teams will start to discover in which scenarios these tools stop being used and where they generate low-quality, unmaintainable code.For example, an AI agent generating a working web app makes for a great demo, but is it architected flexibly to respond to changes in response to your organization’s commercial pressures over the next 12 months? Or the demands you know your biggest customers are likely to have? Our experience so far is that giving agents this level of context when tackling more significant engineering problems is hard, though the productivity gains when performing more straightforward tasks are a great asset.Data Infrastructure InvestmentsMany companies are now experiencing the frustration of executives demanding AI-driven product improvements, which has been going on for over a year, only to face roadblocks caused by pervasive low data quality. As patience wears thin, we will likely see significant overhauls of data infrastructure receiving approval to address these issues.However, companies that embark on these projects without learning from past experiences — such as the challenges of migrating from large monolithic systems to distributed architectures — risk encountering endless and ultimately failed migrations.As this wave of data infrastructure investment unfolds, traditional architecture and data design skill sets will re-emerge as crucial and highly sought-after. Companies will come to recognize that the legacy replacement skills developed over the last 30 years are just as essential as machine learning expertise in unlocking AI’s full potential and achieving meaningful, sustainable advancements. Engineers with this experience will be in high demand. Companies should look to the Strangler Fig Pattern, avoid big bang migrations, and aim for small, meaningful slices of system improvement to avoid these pitfalls.A More Cautious Approach to Regulatory TechSignificant changes in the global political landscape are prompting companies to adopt a cautious and reactive stance toward implementing regulatory projects across their platforms. With the potential for an era of deregulation on the horizon, businesses are preparing to navigate unpredictable shifts in regulatory requirements.As a result, the justification for large, costly compliance projects is facing increased scrutiny. Companies are wary of committing substantial resources to initiatives that could become sunk costs should sudden regulatory changes render them unnecessary. This cautious approach underscores the need for flexibility and adaptability in planning regulatory tech investments in an uncertain political and regulatory environment.A Comeback for Junior Roles, but They Will Look DifferentSince the pandemic, the market for junior engineering roles has been cold. However, as more prominent tech companies face the reality of five years of senior talent taking retirement, they are likely to revive junior hiring programs. This shift will be a necessary alternative to competing for an increasingly limited pool of experienced professionals.These junior roles, however, will undergo greater scrutiny. With the anticipated workforce changes driven by AI productivity gains, companies will set more stringent expectations for each position. The emphasis will be on ensuring that every new hire aligns closely with evolving business needs.Career switchers may become more attractive candidates in this landscape than recent university graduates. Companies may view individuals with proven aptitude and experience in a commercial environment as a safer investment, prioritizing practical expertise over purely academic credentials. Globally, providers of “boot camps” have often focused on this engineer profile. In 2023, the Pragmatic Engineer newsletter reported Launch School expected their Capstone hiring program to see 15-20% worse than in previous years in terms of salary average and duration to offer. Still, Lighcast data shows that entry-level role postings have grown by 47% in the latter half of 2024.Usage-Based Pricing in SaaS and PaaS ToolsWith low interest rates and a hot IPO market failing to re-emerge, the demand for SaaS and PaaS tools that adopt usage-based pricing rather than seat-based pricing is set to grow.The new generation of SaaS companies offering these tools will be lean by design, focusing on undercutting competitors with competitive pricing. Unlike many established SaaS providers that pursue elaborate and ambitious IPO plans in hopes of becoming the next tech giant, these new entrants will prioritize sustainability and a commodity-like business model from the outset. Customers, increasingly unwilling to fund the high costs associated with grandiose expansion plans, will gravitate toward these pragmatic offerings.Established tools that have embraced usage-based pricing, such as Honeycomb and PostHog, are well-positioned to thrive. Operating in markets where more prominent players traditionally charge by seat, these companies’ transparent pricing positions them for strong adoption.ConclusionCompanies must navigate a complex interplay of innovation, regulation, and workforce evolution as technology advances. AI is transforming development workflows but revealing limits, while the demand for better data infrastructure emphasizes the importance of foundational skill sets. Regulatory tech investments are becoming more cautious, junior roles are being defined to meet modern needs, and usage-based pricing models are reshaping SaaS and PaaS landscapes. By understanding these trends and adapting strategically, businesses can thrive in a rapidly changing environment, positioning themselves for long-term success.The post AI in Dev Tools: Accelerating, but Learning the Limits appeared first on The New Stack.