The opportunity and the bottleneck. Where talent really matters in AI driven drug discovery

19 January 2026

By James Trott, Founder, Aspire Life Sciences Search

Over the past twelve to eighteen months, the narrative in biotech and pharma has shifted quickly. A series of high-profile partnerships and data platform initiatives between major pharmaceutical companies and AI-enabled discovery platforms has made something very clear. Artificial intelligence in drug discovery is no longer an experiment. It is becoming core infrastructure.

For example, leading genome technology companies are building datasets aimed at dramatically improving AI performance in discovery workflows, and major pharma are partnering with them to train models at scale. These efforts are intended to help uncover disease mechanisms and improve predictive power in early research stages.

But as these companies move from research prototypes into mission-critical systems, another reality is emerging just as fast. The real bottleneck is not models or computing. It is people.

Capital is no longer the constraint. Talent is.

The industry is no longer short of funding for AI in drug discovery. What it is short of is the right kind of talent. Across the sector, the conversation has shifted from whether AI will work to whether organisations can actually build teams capable of using it properly.

Companies can find software engineers. They can find machine learning engineers. What they cannot easily find are people who combine:

Deep understanding of biology or chemistry
Fluency in machine learning and data systems
The ability to work with messy experimental data
The judgment to translate model output into real scientific decisions

This is not a credentials problem. It is an interpretation problem. It is about knowing what a model result actually means in the context of a biological system and a drug development programme.

Industry analysis shows that a large proportion of pharma hiring managers are struggling to find candidates who combine domain expertise with AI capability. A recent survey noted that almost half of life sciences organisations see digital skills shortages as a barrier to transformation, and about seventy per cent report difficulties finding professionals who have both deep pharmaceutical knowledge and AI skills.

Where most of the hiring volume will go

As these companies scale, a large part of their hiring will look like any other serious technology business. They will need people to build, maintain, secure and scale the systems that underpin AI driven discovery.

This includes:

Data infrastructure engineers
Machine learning engineers
Platform engineers
Reliability and systems engineering roles
Security and compliance engineering

There is nothing wrong with this. It is inevitable. These are production systems now.

This is also where the broader technology talent market will move in force. Founders and hiring managers will be inundated with candidates from major technology companies and from across the broader AI hiring boom. Salaries will rise. CVs will showcase scale and pedigree.

This trend in the competition for AI talent extends beyond biotech. For example, global technology firms are already reporting that specialised AI and machine learning operations roles are extremely difficult to fill, with companies having to involve senior executives directly in recruitment to stand out.

This will create a lot of activity. It will also create a lot of noise.

The real bottleneck is not generic AI talent

What remains genuinely scarce is a much smaller and much more critical group of people. The hybrid layer.

These are the people who can:

Frame biological problems in a way machines can learn from
Understand why a model fails in scientific terms not just technical ones
Translate predictions into experiments
Sit between data, models, wet lab teams and decision making

These profiles include truly hybrid scientists, computational biologists who can write production code, research engineers with domain depth, and scientific engineers who know both biology and ML workflows.

This talent pool is limited, and it is the difference between a model that accelerates insight and a model that sits unused. Even though these individuals will represent a minority of total hires, they carry a disproportionate amount of execution risk. This observation matches broader industry commentary linking AI adoption in pharma and biotech to workforce capability and organisational readiness, not just tools and platforms.

The danger of over-correcting toward pure tech hiring

There is a real risk over the next few years.

Because generic AI and software talent is more visible and easier to hire at scale, companies will be tempted to over hire in that direction early. Especially when under pressure to show progress or quarterly results.

This can lead to:

Engineering heavy teams without enough scientific steering
Expensive churn as people follow the next trend
Platforms that are technically impressive but scientifically misaligned
Bottlenecks appearing later that are much harder to fix

This is not a theoretical risk. Across science and technology fields, highly specialised roles take significantly longer to fill and become strategic constraints when neglected early.

Why BioTech has a cultural advantage if it protects it

One of the quiet strengths of life sciences has always been retention and commitment. People stay because they care about the science and the impact. They know that meaningful discovery takes time.

Pure technology markets behave differently. Movement is fast. Loyalty is lower. The next interesting problem or the next compensation jump often wins.

There is nothing inherently wrong with that. But if an AI driven BioTech ends up staffed mostly by people who see it as just another interesting job, it will struggle to build durable discovery engines.

The companies that win will be the ones that balance serious engineering talent with deep scientific ownership and continuity.

The real strategic question for founders

The future is not biology or technology. It is biology with technology fluency.

The companies that win will be the ones that:

Invest early in the hybrid layer
Protect scientific context as they scale engineering
Avoid mistaking hiring volume for progress
Build teams that understand what they are trying to discover, not just how to compute

The real competitive advantage in AI-driven drug discovery is not just better infrastructure. It is faster and more reliable translation from model output to biological insight.

A practical hiring truth

Over the next few years:

Most hiring volume will be pure engineering and infrastructure
Most differentiation will come from a much smaller group of hybrid scientific talent

Many firms in the market will chase volume. That is logical.

But the companies that win will be the ones who obsess over the bottlenecks.

Why this matters now

This is not a theoretical discussion. Over the last twelve months, the industry has clearly entered a new phase.

We have seen a wave of significant funding into AI native discovery companies, foundation model biology platforms, and large scale biological reasoning systems. At the same time, we have seen an increasing number of deep, strategic partnerships between these companies and major pharmaceutical organisations.

Taken together, this signals a shift. The industry is moving from a phase of proving that the technology works to a phase of trying to operationalise it inside real discovery organisations.

The first phase was mostly about models, data, and architecture. The next phase is about turning those capabilities into repeatable scientific output, inside complex organisations, under real delivery pressure.

That transition is not primarily a technology problem. It is an organisational and talent problem.

The companies that navigate this phase well will not be the ones with the most impressive demos. They will be the ones that get the composition, sequencing, and culture of their teams right.

Final thought

We are entering a phase where AI becomes part of the fabric of drug discovery. Technology will continue to improve. Computing will get cheaper. Tools will become more standard.

The differentiator will not be who has the nicest models.

It will be who has the teams that actually know how to use them on real biology.

And those teams will not be built overnight.