Artificial intelligence is reshaping drug discovery in an unexpected way: instead of cutting costs, it's poised to drive pharmaceutical companies to spend more on research and development. Thermo Fisher Scientific's CEO Marc Casper recently explained why AI tools that eliminate failed drug candidates faster and accelerate promising therapies actually encourage larger investments across the industry.

How Is AI Changing Pharmaceutical Spending Patterns?

The conventional wisdom suggests that AI automation reduces the need for expensive laboratory work. But Casper pushed back on that assumption in a recent CNBC interview, arguing that AI creates a fundamentally different dynamic. When drugmakers gain better insights into which compounds might work, they don't stop testing; instead, they validate their findings more thoroughly and pursue more disease indications with the same drug candidate.

"AI is helping kill ineffective medicines faster, so that you don't spend money on things that don't work, and at the same point in time, shorten the time to market for those things that do work, and that gives you a longer return cycle on an improved medicine, and that will spur more investments," said Marc Casper, CEO of Thermo Fisher Scientific.

Marc Casper, CEO at Thermo Fisher Scientific

Casper elaborated further on this counterintuitive trend. When companies have more confidence in a drug's potential, they invest more money exploring additional disease applications. This expanded scope means more validation work, more clinical trials, and ultimately more demand for laboratory services and equipment.

"When you have more insights into what you do, you actually do a lot more validation, because what you'll then do is spend more money on the ultimate number of indications or types of disease you're going after with the medicine. So, AI means more spending, not less spending," Casper added.

Marc Casper, CEO at Thermo Fisher Scientific

What's Driving the Shift in Laboratory Automation?

While computational AI has transformed data analysis and biological modeling, the physical work inside laboratories has remained largely manual and disconnected from digital systems. A Boston-based startup called Dimenso is now addressing this gap with an innovative approach: AI-powered smart glasses that capture laboratory work in real time.

Dimenso raised $650,000 in funding from Draper B1 and Next Tier Ventures to expand its platform across the United States and Europe. The technology uses computer vision, voice interfaces, and contextual understanding of scientific protocols to automatically document experiments, track protocol execution, and identify deviations without interrupting scientists' work.

Unlike traditional laboratory software that requires manual data entry before or after experiments, Dimenso's platform captures scientific work as it happens. This real-time capture transforms physical laboratory execution into structured, traceable, and analysis-ready data that can feed into existing systems like electronic lab notebooks.

Ways AI Is Transforming Laboratory Productivity and Data Quality

• Administrative Burden Reduction: Dimenso's technology reduces the time scientists and operators spend on administrative tasks by up to 65%, freeing researchers to focus on experimental design and analysis rather than paperwork.
• Accelerated Data Analysis: The platform can accelerate the analysis of experimental data by as much as 20 times in supported workflows, compressing weeks of manual review into hours.
• Error Prevention and Reproducibility: By capturing experimental context and identifying deviations in real time, the system helps prevent costly experiment repetitions that can delay drug development by weeks or months.

According to company data, errors or missing information during laboratory execution force teams to repeat experiments, creating delays that extend development timelines and significantly increase research and operational costs. Dimenso's approach aims to preserve information that might otherwise be lost during manual documentation.

What Does This Mean for Pharma Growth Forecasts?

Thermo Fisher's outlook reflects growing confidence in AI's role as a growth accelerator rather than a cost-cutter. The company expects organic growth of approximately 4% in the second half of 2026, with acceleration to 7% by 2028. This projection assumes that AI-driven efficiencies will encourage pharmaceutical and biotechnology companies to expand their research pipelines and pursue more ambitious drug development programs.

Thermo Fisher is already integrating AI into its operations through a collaboration with OpenAI to speed up clinical trials and drug development. The company's previous guidance suggested that AI could improve returns from drug discovery and encourage customers to invest more money into their pipelines, a thesis that Casper reinforced in his recent comments.

The convergence of these trends suggests that the pharmaceutical industry is entering a new phase of AI adoption. Rather than replacing laboratory infrastructure and personnel, AI tools are enhancing their productivity and enabling more ambitious research programs. For companies like Thermo Fisher that supply laboratory equipment, reagents, and services, this shift translates into sustained demand growth driven by expanding R&D budgets across the industry.