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TMO · Thermo Fisher Scientific Inc.

Medical - Diagnostics & Research · mkt cap $179.2B · calls: Q1 FY2026 vs Q4 FY2025
48.0 conviction · conf-adj 48

conf –

enthusiasm:27.0 · trend:8 · quantifies:0 · impact:0 · under_radar:5 · credibility:0 · business_impact:8 · disruption:0 · commitment:0 · confirmation:0

Enthusiasm latest 9 / prev 8 (rising)

Thermo Fisher's AI thesis is that AI improves drug discovery and development ROI, raises wet-lab and instrument demand, strengthens clinical research, and improves internal productivity through PPI. Credibility is moderate: management gives specific use cases, named collaborations with OpenAI and NVIDIA, and product embedding, but provides no numeric AI revenue, bookings, cost, margin, or productivity impact.

PAST (realized)
CURRENT (now)
FORWARD (guidance)
TRACK RECORD — PROMISE vs DELIVERY

—/100 (no quantified promises)   no-quantified-promises  6 calls reviewed

Across all six calls, Thermo Fisher discusses AI substantively — generative AI embedded in its PPI Business System, AI-enabled instruments (e.g. Vulcan, Glacios 3 Cryo-TEM), and strategic collaborations with OpenAI and NVIDIA — but every AI reference is qualitative ('aggressively adopting AI', 'deploy AI at scale', 'improve cycle time'). Management never attached a number plus a timeframe/milestone to any AI capability (no AI revenue target, cost-cut percentage, or dated milestone), so there are no quantified AI promises to score on a deliver-vs-miss basis.

PRICED-IN (REFINED)
MEDIUM

Est. revisions falling  ·  Fwd P/E 21.2  ·  EV/Sales 4.8x

AI claim maps to Service, Instruments

Estimate momentum is not rising: ratings are broadly stable to slightly softer since April, while price-target averages are falling with lastMonthAvg below lastQuarterAvg and lastYearAvg. Forward consensus still embeds mid-to-high single digit revenue/EPS growth, but that is growth already in the model rather than evidence of upward revisions. Valuation is fairly rich for a mature diagnostics and research tools company at 21.2x forward EPS and 4.8x EV/Sales, so flat-to-falling revisions plus a premium multiple makes AI upside partly priced in, but not enough for a high verdict.
COVERAGE — ENTHUSIASM TRAJECTORY + CATALYSTS
2Q4 FY20247Q1 FY20257Q2 FY20259Q3 FY20258Q4 FY20258Q1 FY2026

AI enthusiasm across 6 calls — trend ↗ rising

AI evolved from absent to concrete AI-enabled instruments, clinical research automation, OpenAI/NVIDIA partnerships, and productivity/product embedding.

RECENT AI CATALYSTS & NEWS
BUSINESS IMPACT - QUALITATIVE MATERIALITY

7/10 qualitative impact   material  medium-term · mixed evidence

Where AI matters: AI-enabled instruments, clinical research workflows, lab automation, internal productivity

AI is being embedded into instruments and clinical research workflows via OpenAI/NVIDIA collaborations and AI-enabled platforms like Glacios 3 Cryo-TEM, which can improve customer productivity and strengthen Thermo Fisher's product moat. The upside is credible but still mostly qualitative, with no disclosed AI revenue, margin, cost, or productivity quantification.

Caveats: No quantified AI revenue or cost-savings targets; Benefits may accrue more to pharma and biotech customers than to Thermo Fisher directly; Regulated clinical and lab workflows can slow AI commercialization; AI-enabled instrumentation may require sustained R&D and partnership execution

AI DISRUPTION / CANNIBALIZATION RISK  tailwind · 2/10

Thermo Fisher's core model is anchored in instruments, consumables, regulated workflows, lab services, and scale manufacturing, which AI is more likely to augment than commoditize. Some clinical research tasks may be automated, but that should mostly improve throughput and competitiveness rather than deflate the core revenue base.

OPTIONS / MARKET STRUCTURE

option liquidity: good

ATM IV
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TYPICAL BID-ASK
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OPEN INTEREST
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proxy inputs — dollar-ADV $1.1B · beta 0.883 · px $482.08

source: proxy (no options chain on FMP)
FMP /stable/ exposes no options-chain endpoint on this key, so ATM IV, bid-ask spread and open interest are unavailable. Liquidity below is a PROXY from dollar-ADV, beta and price level (a stand-in for option depth), not measured option-market data.

CONFIRMATION — INSIDERS · 13F · LANGUAGE
Undercutting — insiders selling, institutions flat, management language 6/10 measured.
INSIDERS selling 35 open-market sell(s) vs 0 buy(s) — net distribution
INSTITUTIONS (13F) flat as of 2026-03-31: 168 new / 288 closed positions; 1129 increased / 1095 reduced; institutional ownership -11.95pp; -116 net 13F holders
MGMT LANGUAGE 6/10 measured AI is real but lightly discussed, with product launches and scale language offset by collaboration-oriented, future-commercialization phrasing.
commit “we introduced the Thermo Scientific Glacios 3 Cryo-TEM, a next-generation cryo transmission electron microscope that features AI-enabled workflows.”
commit “deploying AI at scale to run the company better”
hedge “working together towards the commercialization of new workflow solutions that will enhance scientific instrumentation”
VERBATIM AI QUOTES
“In our Analytical Instruments business, we introduced the Thermo Scientific Glacios 3 Cryo-TEM, a next-generation cryo transmission electron microscope that features AI-enabled workflows.”
— Marc Casper, Q1 FY2026
“Earlier in the year, we announced a strategic collaboration with NVIDIA, combining our leadership and laboratory technologies with NVIDIA's advanced AI capabilities.”
— Marc Casper, Q1 FY2026
“The team is making great progress working together towards the commercialization of new workflow solutions that will enhance scientific instrumentation and help customers work faster, improve accuracy and get more value out of each experiment.”
— Marc Casper, Q1 FY2026
“As a reminder, a few areas of focus for the PPI Business System in 2026 are driving an accelerated level of cost productivity, deploying AI at scale to run the company better, and the continued mitigation of tariffs.”
— Marc Casper, Q1 FY2026
“And we're embedding AI into our capabilities per that collaboration we had announced some time ago with OpenAI and customers value that, and that positions us very well.”
— Marc Casper, Q1 FY2026
“In terms of artificial intelligence, super exciting, actually.”
— Marc Casper, Q1 FY2026
“And when I think about the role that AI is playing with our customers, it's accelerating scientific discovery.”
— Marc Casper, Q1 FY2026
“It's deepening understanding and it's ultimately going to accelerate bringing new medicines to patients faster to address significant unmet medical needs.”
— Marc Casper, Q1 FY2026
“And when I think about what it means is we believe that AI is going to improve the returns on investment for the drug development industry.”
— Marc Casper, Q1 FY2026
“We believe that AI is going to accelerate and enhance our durable competitive advantage that we've had.”
— Marc Casper, Q1 FY2026
“they want the instruments to be more automated or more automated-ready, and they want it to be easy to effectively have the data be able to populate their own models, right?”
— Marc Casper, Q1 FY2026
“We also announced a strategic collaboration with OpenAI, aimed at increasing our use of artificial intelligence at the company.”
— Marc Casper, Q4 FY2025
“This will improve productivity across our operations, and also allow us to embed AI capabilities in our products and services to accelerate scientific breakthroughs and advance the drug development process.”
— Marc Casper, Q4 FY2025
“That's why we're so we're also increasing increasingly using artificial intelligence into PPI which will further enhance its impact across the organization.”
— Marc Casper, Q4 FY2025
“The combination is helping us improve how we serve customers, streamline internal processes, and operate the company more effectively.”
— Marc Casper, Q4 FY2025
“When I think about how we are collaborating with OpenAI, really focused on the, you know, clinical research side of the thing the equation.”
— Marc Casper, Q4 FY2025
“Is how do you further shave time and cost out of the process? And have even more insights?”
— Marc Casper, Q4 FY2025
“We're doing a lot of work with customers on the wet lab dry lab combination, meaning that we're actually working with our customers to better link what goes on in their wet labs with their data management and insights from AI.”
— Marc Casper, Q4 FY2025
“And so so we're actually quite optimistic about the intersection between AI and the demand for wet lab research.”
— Marc Casper, Q4 FY2025
ANALYST QUESTIONS ON AI
Q (Q1 FY2026, Jack Meehan): Marc, I wanted to get your thoughts around AI as you -- this is obviously a huge topic for the market. As you look across the business segments, can you talk about how adoption might be influencing your customer spending behavior? And I'm not sure if you're planning an Analyst Day or not, but any color you can share on new offerings you might be able to highlight that leverage your data in Clario?
A: In terms of artificial intelligence, super exciting, actually. And when I think about the role that AI is playing with our customers, it's accelerating scientific discovery. It's deepening understanding and it's ultimately going to accelerate bringing new medicines to patients faster to address significant unmet medical needs. And when I think about what it means is we believe that AI is going to improve the returns on investment for the drug development industry. That means that there'll be more products that will be coming through the pipeline and ultimately will create an enhancement of funding interest in the biotech community.
Q (Q1 FY2026, Matt Larew): Just wanted to follow up on Jack's question on AI, but also the instrument innovation highlights you shared. It seems like there's going to be an enhanced emphasis on scale, automation, connectivity and auditability or proof of work both for large-scale generation of biological data and in autonomous labs. I think the threat of your portfolio alone may be an advantage, but as you think about the way your instrument exists today and what kind of enhancements or changes you might make in the future, how does -- how customers might shift the way they are using your instruments affect the way that you're thinking about developing them?
A: you're seeing experimentation scale up and will scale up in areas that it would never have happened in the past, right, which is just large-scale generation of biologic information to effectively create biology models, right? So as opposed to what people normally do, which is they're looking at their particular area of interest, you're not seeing very wide scale large volume labs that are just trying to build biology models, if you will. And so when you think about what those customers need, they want the instruments to be more automated or more automated-ready, and they want it to be easy to effectively have the data be able to populate their own models, right?
Q (Q4 FY2025, Matt Larew): You know, since you launched Accelerator in in late twenty twenty four, now you've seen a number of pretty big changes in the drug development and manufacturing ecosystem in terms of manufacturing re regionalization, rising use of AI and drug discovery, Marc, you referenced outstanding customer adoption of that solution. Just curious how some of these ecosystems changes are affecting customer preferences for outsourcing in general, and, I guess, more specifically in the accelerator offering?
A: When I think about how we are collaborating with OpenAI, really focused on the, you know, clinical research side of the thing the equation. Is how do you further shave time and cost out of the process? And have even more insights? And you know, that's gonna be a journey because it's a highly regulated industry, and we'll go on that journey with our customers and and look for new opportunities to drive an even more efficient drug development process.
Q (Q4 FY2025, Matt Larew): Okay. Thanks for that. So so encouraged about drug development activities. But thinking about the drug discovery side, think still some debate about whether AI is a headwind or tail lift. Tailwind to the amount of what lab work moving forward. Just would be curious experience has been with customers, be it, you know, AI, first, biotechs and then larger pharma companies that were perhaps more aggressively using AI. And what you've seen about their wet lab activity, their demand for instruments, etcetera.
A: What I would say is on the application of AI, We're doing a lot of work with customers on the wet lab dry lab combination, meaning that we're actually working with our customers to better link what goes on in their wet labs with their data management and insights from AI. Our experience to date and certainly our experience historically is the more confidence you have in the research, you wind up doing actually more wet lab experimentation. You're probably going to work on less things that are just gonna fail. So you there is some waste that comes out of the system. But customers wanna have total confidence in the work they're doing, and that's been our experience.