In this exclusive interview for DT, Rajeev Ranjan, Editor, speaks with Adnan Masood, PhD, Chief AI Architect at UST, to explore the implications of Anthropic’s latest AI model releases, the evolving role of engineers in an AI-driven world, emerging workforce concerns, and practical advice for the next generation of technologists. The discussion spans market reactions, enterprise adoption, skill transformation, and the growing importance of trust, verification, and domain expertise in the age of intelligent systems.
Q. What is your perspective on the launch of Anthropic’s latest AI model and the market reaction?
From my vantage point as a practitioner and someone who closely collaborates with academia and industry, Anthropic’s newest Claude release, Claude Sonnet 4.6 (with Claude Opus 4.6 earlier in February), is less about a single benchmark leap and more about operationalization. Sonnet 4.6 upgrades coding and review, long-context reasoning, and “computer use,” introduces a beta 1M-token context window, and is positioned as the default model for broad users at unchanged Sonnet pricing.
The market reaction has been split. Enterprise demand and cloud distribution are strengthening (including rapid productization of these models across major clouds), but investors have also repriced parts of the application layer. Reuters reported a rapid drawdown approaching $1T in software/services market value as markets debate whether agentic models could substitute portions of SaaS and professional knowledge products.
Q. In the evolving AI era, how do engineering and technical roles transform, and what skills matter most?
A . I see engineering shifting from “writing code” to “designing, validating, and operating systems that produce correct outcomes with AI in the loop.” Studies of coding assistants show sizable reductions in task-completion time, and developer adoption is now mainstream. But surveys also show trust is uneven, so verification and quality engineering are becoming differentiators.
The engineers who stay relevant will pair fundamentals (systems thinking, architecture, data structures) with AI fluency (prompting, tool orchestration, evaluation, cost/latency tradeoffs) and production discipline (testing strategy, observability, security, and privacy-by-design). Employer signals point the same way: analytical thinking remains the most-cited core skill, with AI, data, and cybersecurity skills rising alongside resilience and flexibility.
3) In light of rapid AI innovation and rising automation, what fears or concerns are you observing?
The biggest workforce fear I see is uneven disruption. The IMF estimates AI will affect about 40% of global jobs, and the World Economic Forum expects meaningful core-skill change by 2030, pressuring entry-level pathways and routine knowledge work. At the same time, ILO research suggests the dominant near-term effect is often task transformation/augmentation rather than full automation, which means reskilling and job redesign are essential.
On the technology side, the anxiety is trust at scale: hallucinations, bias, privacy/IP leakage, and security issues (including prompt injection and tool misuse) become higher-stakes as models gain agentic “computer use.” That is why structured practices like the NIST AI Risk Management Framework are increasingly table stakes for enterprise adoption.
Q. What will be your advice to young engineers entering the field now?
A. My advice is to use AI daily, be AI fluent while building a verification reflex: test, review, and measure quality in your context—because even developers report significant distrust in AI output accuracy today. Invest in fundamentals, then add domain depth (finance, healthcare, telecom, manufacturing, security) so you bring constraints AI doesn’t. Optimize for outcomes: turn real problems into clear written specs, orchestrate tools, and ship reliably. Finally, invest in human skills employers still prioritize—analytical thinking, clear communication, curiosity, and resilience—because the biggest career advantage will come from combining domain judgment with strong engineering execution.
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