Who Gets Hired In An AI-First World?

Ever since they have been around, technology companies hired for pedigree, technical depth, experience and execution speed. But, with AI now writing code, automating workflows and analysing data in seconds, the rules of the hiring game are being rewritten. The skills that define valuable talent are changing rapidly, forcing startups to rethink recruitment, organisational design and their strategies for attracting top talent in an increasingly competitive market. 

Therefore, companies are not asking whether candidates can merely do the job but are increasingly prioritising employees who can work effectively alongside AI systems and possess the ability to think across functions.

But as AI reshapes workplaces, HR leaders are still contemplating which roles and skills will matter most in AI-first organisations and which roles will lose relevance. And should they be hiring fewer people in the age of AI? Let’s try to answer some of these questions in this edition of The AI Shift. 

Who’s An AI-Native Employee?

One of the most significant shifts in hiring today is the growing demand for what HR leaders call AI-native talent. This does not necessarily mean employees with a deep background in AI research or machine learning expertise. Instead, companies are increasingly looking for people who understand how to work effectively with AI systems, interpret outputs critically and connect those outputs to business outcomes.

According to Poonam Ajgaonkar, the chief people officer at Qure.ai, a health tech company, technical skills alone are no longer enough to stand out.

“AI is fundamentally shifting what startups need from their people. The role that will gain the most ground over the next 3-5 years is the AI-native problem solver, someone who knows not just how to use these tools, but how to ask the right questions, make sense of what comes back, and connect it to what the business actually needs,” Ajgaonkar said.

As automation takes over repetitive execution work, startups are placing greater emphasis on judgment, contextual understanding and problem-solving.

Hiring leaders say candidates who can combine domain expertise with AI fluency are becoming significantly more valuable than specialists who only operate within narrow functional silos.

That trend is also influencing how startups are evaluating talent.

Ganesh Shankar, the CEO and cofounder of Responsive, a global strategic response management software platform, said companies are now prioritising candidates who can adapt to new technologies, learn quickly, and work effectively as AI tools evolve.

Which Jobs Are Fading Away?

As AI tools become better at handling routine workflows, startups are reassessing the relevance of roles built primarily around task execution. This, however, does not necessarily mean mass replacement of employees or a full-fledged AI jobpocalypse.

Let’s take AI infrastructure startup Neysa as an example. The company is actively hiring across high performance computing, cloud, distributed systems, platform engineering and infrastructure roles, but the larger expectation goes beyond technical depth. Employees are expected to think across systems, solve interconnected problems and take ownership beyond their immediate job descriptions and KRAs.

“What is becoming less relevant are roles that are limited to task execution without ownership,” Neysa’s EVP of employee experiences Swapna Uchil said. 

She added that instead of looking for narrowly defined specialists, companies are prioritising people who can scale functions, work across disciplines and operate independently in high-speed environments. The shift is particularly visible in younger AI and infrastructure startups where teams remain lean and product cycles move rapidly. 

Getting Talent Ready For AI

Even as startups rethink the kind of talent they need, competition for strong AI and engineering talent has intensified. Large technology companies and AI-native firms continue to offer aggressive compensation, global exposure and access to frontier AI work. 

But several hiring managers argue that compensation alone is no longer the deciding factor. At Neysa, for example, engineers are attracted by the opportunity to work directly on core AI infrastructure challenges rather than contributing to smaller isolated systems within large organisations. Employees often gain exposure across infrastructure, products, platforms and customer requirements simultaneously.

The same is common in SaaS companies, too. According to LeadSquared CTO Pankaj Gupta, ambitious professionals want opportunities to influence outcomes directly and solve consequential business problems.

“The best want to be challenged, trusted and given the freedom to build. That expectation is also reshaping organisational culture,” he said.

Hiring managers said that the new-gen talent is evaluating companies based on learning opportunities, ownership, speed of execution and exposure to meaningful work rather than traditional corporate stability markers alone.

Finally, as the Indian workforce moves towards the broader shift, organisations are slapped with the challenge of measuring employee productivity and business impact in the age of AI.

To counter this, companies have started tracking whether AI investments are genuinely improving efficiency, productivity and business outcomes rather than becoming an experimental overhead. They are evaluating factors such as output per employee, operational efficiency, customer outcomes, and revenue impact instead of simply tracking AI tool usage.

Going ahead, companies that are likely to attract the strongest talent may not necessarily be the ones deploying the most AI tools, but the ones that successfully combine AI-driven efficiency with meaningful human ownership, learning and business growth.

Top Stories From India & Around The World

• AI Takes The Director’s Chair: Generative AI is rapidly becoming part of filmmaking workflows, helping creators with scriptwriting, storyboarding, VFX, dubbing, editing, and production planning, while also lowering production costs for ambitious cinematic projects.
• Innefu Labs’ $30 Mn Raise: The AI-powered cybersecurity startup has secured fresh funding from Panthera Growth Partners ahead of its planned IPO. The company will use the capital to expand globally, strengthen R&D and build sovereign AI capabilities, including agentic AI platforms, robotics and specialised language models for high-security environments.
• Together Fund’s Manav Garg Joins Emergent: Together Fund cofounder Manav Garg has joined AI startup Emergent as executive chairman, betting on AI becoming the next operating layer for businesses. Garg said Emergent’s focus on helping companies fundamentally change operations, rather than simply speeding up software development, drove his decision.
• SC Mulls Limiting AI In Judiciary: The Supreme Court has proposed a strict regulatory framework banning AI-driven adjudication in Indian courts. While AI can assist with legal research, transcription, case management, and administrative workflows, the draft makes it clear that judgments, sentencing, and judicial decisions must remain exclusively human-led.
• TrueFan Raises $10Mn For AI Video Push: Gurugram-based startup TrueFan AI has raised funds in a Series A round led by Baring PE India and Z3Partners to expand across Southeast Asia, the Middle east, and the US. Originally launched as a celebrity engagement platform, the startup now builds AI-generated personalised video systems and real-time video agents for customer support, sales, onboarding, and product discovery workflows.
• India Gets Access To Anthropic’s Mythos AI: Anthropic has expanded access to its cybersecurity AI model Mythos under Project Glasswing to organisations across India and 15 other countries. The model is designed to identify critical software vulnerabilities before exploitation, as Indian regulators, banks, and government agencies simultaneously assess the cybersecurity and national security risks posed by advanced AI vulnerability-detection systems.

The Weekly Buzz: Paul Graham On Building ‘AI-Proof’ Startups

As AI rapidly commoditises software creation, startup investors and founders are increasingly asking a bigger question: what businesses remain defensible in an AI-first world? 

This week, Paul Graham, one of Silicon Valley’s most influential thinkers and the cofounder of startup accelerator Y Combinator, sparked debate online with a simple argument: the strongest AI-proof startups may be the same businesses humans have historically struggled to replicate.

Graham pointed to marketplaces as a prime example. According to him, marketplaces are resilient against AI disruption for the same reason they are resilient against human competitors: network effects. Once supply, demand, trust, liquidity, habits and distribution consolidate around a platform, replicating the interface alone does little to recreate the underlying value.

The conversation quickly expanded into a broader debate around what constitutes a moat in the AI era. Several founders and operators noted that AI can now replicate product features, user interfaces and workflows within days, but struggles to reproduce accumulated behavioural data, user trust, operational history and network density built over years.

Others highlighted a growing distinction between “AI wrappers” and deeply embedded platforms. While foundational AI models continuously evolve and commoditise application-layer features, network-driven businesses tend to compound in defensibility as more users participate. 

Graham also argued that patents could remain another layer of AI-era protection. While AI systems may become better at identifying loopholes or workarounds, he suggested companies could equally use AI to predict vulnerabilities and strengthen patent coverage proactively.

The larger takeaway is that the AI era may shift startup defensibility away from pure software execution toward harder-to-replicate assets. In a world where AI can increasingly clone products, the real moat may lie in the ecosystems companies build around them.

Startup In The Spotlight: Molecule AI

As pharmaceutical companies race to reduce the time and cost involved in drug discovery, Bengaluru-based Molecule AI is using generative AI to accelerate how new drug candidates are identified and evaluated.

Founded in 2023 by Saurabh Singal and Neeta Singal, Molecule AI is focused on reimagining the early-stage drug discovery process, which has traditionally relied on expensive and time-consuming trial-and-error experimentation across thousands of chemical compounds.

The startup’s core platform, MoleculeGEN, uses AI to design and simulate new molecules digitally before any physical lab work begins. The platform helps researchers rapidly identify promising drug candidates that can potentially target specific diseases while screening them against multiple pharmacological criteria.

MoleculeGEN evaluates molecules for factors such as target specificity, pharmacokinetics and safety profiles, helping determine whether a compound can effectively bind to disease proteins, remain stable inside the body and potentially be safe for human use.

By accelerating virtual screening and pre-clinical evaluation workflows, Molecule AI aims to significantly reduce the time, cost and uncertainty associated with traditional drug discovery pipelines.

The startup operates within India’s rapidly growing AI-in-genomics market, which is projected to surpass $506.4 Mn by 2030, as pharmaceutical and biotech companies increasingly adopt AI-driven research and drug development tools.

Prompt Of The Week

What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work? 

Here’s the prompt used by Sachin Panicker, chief AI officer at Fulcrum Digital, to challenge strategic assumptions and uncover hidden risks before major business or AI transformation decisions are executed.

“You are a world-class strategic consultant and industry contrarian who has scaled multiple companies and witnessed spectacular failures in market expansions and major pivots.

We are planning to [Insert Strategic Initiative, e.g., expand to the US market/pivot to AI-first/new pricing etc.].

The Context: [Describe current resources, market position, competitive landscape, runway, key assumptions and timeline].

I want you to perform a ‘Pre-Mortem’ analysis:

Imagine it is 12 months from now and this initiative has failed badly. Identify the top 3 ‘Silent Killers’ (hidden or under-appreciated risks) that most likely caused it. Rank them by likelihood × impact.

For each risk, identify 1-2 ‘Canary in the Coal Mine’ metrics we can track starting today. For each risk, suggest 3 ‘Asymmetric Moves’ to de-risk this initiative without doubling the budget.

Keep the tone cynical, sharp, and focused on brutal honesty. Call out our likely blind spots. Use sharp language. Avoid fluff and generic advice.”

Editor’s Note: Some prompts may need to be adjusted by users for best results or may not work as intended for certain users.

[Edited by Shishir Parasher]
[Creatives by Varshita Srivastava]

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