Raw Reads is a series of refined, comprehensive transcripts of conversations on topics covered by Ion Genomics.
Illumina made nearly $900 million selling sequencing instruments and consumables to run on them in the third quarter, but apparently that’s no longer the point.
“It’s not about just selling sequencers anymore,” Illumina CEO Jacob Thaysen said on a Oct. 29 conference call with investors following the release of the company’s recent financial results.
In the era of AI, pharma companies (among others) are looking for partners that can generate huge data sets that could be used to train AI models, such as Illumina’s Billion Cell Atlas, he suggested. “They’re much more interested in what those data sets can generate, and obviously the models, how they can perform rather than the sequencing itself,” he said.
Thaysen and other top Illumina execs have been positioning Illumina as a company that can do more than just sequence DNA for a while now, deemphasizing the cost of sequencing per gigabase or human genome in favor of the total cost of the end-to-end workflow and highlighting the acquisitions they’ve made in multiomics, such as single-cell sequencing (Fluidigm) and sequencing-based readout of proteomics (SomaLogic.) That’s certainly convenient for them, now that researchers have elsewhere to go for sequencing technology.
Still, I don’t think Thaysen has ever phrased this sentiment so starkly. It would be like if the CEO of Apple said “It’s not about just selling iPhones anymore.”
Oh wait. Tim Cook did say something like that when he was CEO. More than a decade ago.
“Because we’re not a hardware company,” Cook said in 2013, as reported by Tech Crunch. “There are other things we are doing and could do to have revenue and have profit flow.”
I bring this up not to suggest that Illumina is the next Apple, but to note that it’s smart for Thaysen to frame his company the way Cook did. It highlights additional areas for revenue growth and may even nudge Wall Street analysts to adjust the way they calculate their valuations.
Moreover, it’s both advantageous and necessary to mention AI in these types of conversations in 2026. Illumina execs said a lot more on this topic during the call. Here’s a refined and edited transcript of their comments.
Jacob Thaysen, Illumina CEO (01:41)
Good afternoon, everyone. We had a great third quarter with strong clinical sequencing demand and elevated NovaSeq X placements, driving revenue, margin, and EPS above our guidance range. Our strategy is working, and I’m proud of what our team delivered for our customers and shareholders, our continued disciplined execution and business momentum allow us to raise our fiscal year guidance, and we remain on track to achieve our long-range financial targets.
Before I go into details on our results, I want to highlight a few ways our innovation is helping customers answer new questions about biology. Early in 2026, we introduced the Billion Cell Atlas to address one of the biggest bottlenecks in applying AI to biology: the lack of functional data sets. AI models need large amounts of data to learn from, and in biology, those models only work if the data is accurate, standardized, and reproducible. But these datasets don’t exist at scale. There is no internet of biology.
Our billion-cell atlas was designed to help address these limitations. Now over halfway complete, the atlas is advancing toward an unprecedented AI-ready perturbation dataset that helps biopharma partners connect genetic signals to cellular mechanisms, strengthen target validation, improve disease understanding, and enable accelerated drug discovery by working directly with biopharma. We are integrating their input to ensure the proprietary dataset is relevant and built to maximize value for both our partners and Illumina.
GSK is the latest biopharmaceutical company to join the billion-cell atlas consortium to accelerate drug discovery, and we are seeing growing interest from other companies seeking access to the completed atlas. We have learned a lot from our partners about how to build these datasets for AI, and we see multiple opportunities to address additional bottlenecks across drug discovery and development. We see this as a large long-term opportunity, and look forward to sharing more about our strategy in the coming quarters.
Earlier this month at ASHG (American Society of Human Genetics), we brought together our assays, sequencing platforms, and software to show how customers can benefit using the Illumina BioInsight ecosystem. The most compelling examples came from customers themselves, who showed how combining genomics, transcriptomics, methylation, and proteomics is helping them see biology they could not see before, with work spanning both single-cell and spatial approaches.
What is resonating with customers is that we are looking at their work from start to finish, making it easier to obtain all the components they need to drive high-quality insights while bringing down the total cost of the workflow.
On the analysis side, we launched the BioInsight platform, which brings our analysis application, AI models, and curated biological data together in one place, reducing the need for customers to assemble and manage separate software tools.
Through the platform, customers can also access Splice AI2, our new AI model that predicts how genetic changes affect RNA splicing in rare disease research data sets. It identified 17 percent more disease-relevant splice variants than other splicing models. I’m proud of the innovation our teams continue to deliver.
Our view is that the next era of life sciences innovation will be powered not only by sequencing, but also by the software, AI, and differentiated data that unlock the power of the genome. Through BioInsight, Illumina is building the base for AI biology, enabling more powerful foundational models and virtual cell models with the potential to improve our ability to understand and predict biology. As we move nearer to predicting biology, we see a path to using those insights to move healthcare toward a more proactive future, identifying risks sooner, preventing disease where possible, and intervening earlier.
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Ankur Dhingra, Illumina CFO (09:15)
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Sequencing service and other revenue of 158 million dollars was up 8 percent on a rest of world organic basis. As Jacob mentioned in his prepared remarks, we are gaining traction in our billion cell atlas program as we continue to deliver data, converting more of our backlog to revenue. The pipeline and interest in our programs is very strong.
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We have made tremendous progress this year, adoption of sequencing across a wide range of clinical applications continues to grow, and our rapidly growing X install base provides for a strong consumables revenue stream for years to come. Revenue and customer interest in our billion cell atlas are both expanding, and we’re just getting started on the large AI-based predictive biology opportunity that lies ahead, and our strong balance sheet and cash flows give us the flexibility to invest in areas of strategic growth that complement our innovative toolset. [...]
Q&Q Session
Vijay Kumar, Evercore ISI (20:51)
Hi, Jacob and Ankur. Thank you for taking my question. I guess I had a two-parter, if you will. Q4 jump off here. I think it implies something close, like 6 percent for rest of the world. And my question is, with that being the jump off, what’s the right framework for 2027? Are the LRP (long-range plan) targets still intact? And just given the strong instrument placement so far, could instruments become a headwind next year? How do you offset it? Thank you,
Thaysen (21:20)
Vijay. Thanks for that. I can’t believe we had to go into question number two before we had a question about 27 here. But let me share a few points here. I mean, first and foremost, we will come out with a full guide in our q4 earnings call, so you have to wait with the details.
But we are still operating in very healthy market environments, especially the clinical market is strong, and we anticipate that clinical will be a growth driver for the company for the foreseeable future. On top of that, there are now also opportunities in what we are seeing with our multiomics capabilities, especially when the research market is coming back. We’re very well positioned for taking advantage of that.
And on top of that, the AI market is just scratching the surface on that. But there’s no doubt there is a substantial requirement or need for a large data set to really train models. We can talk more about that later also. So that’s the overall framing for how we see the future.
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Dan Arias, Stifel (33:30)
Yeah, hi guys. Thanks for the question here, Jacob. [...] As you guys try to tap into this training model virtual cell AI opportunity. Can you just talk about how you see genome pricing playing into that? I mean, obviously you think that the X is the box to bet on, but there are some pretty cheap genomes that are being talked about out there, pushing down to $50. So, to what extent do you think that you might need to revisit the price elasticity discussion in order to make sure that you really take those large projects. Can you win those with the current pricing scheme, or do you think it takes whatever the new 35B flow cell might bring on on genome cost?
Thaysen (34:14)
Well, first and foremost, we feel really good where we are. I think AI models is way beyond what a whole genome pricing. There’s a lot going into this. So let me just try to frame this a little bit. First and foremost, I think we see a big opportunity in what I would call the [predictive biology]. But if you want to step into that new logical world, which could [...] have a substantial impact on everything we do, both in our space, but if you think about life science as a whole, this could revolutionize our understanding on biology, and it could definitely also have big disruption into overall the healthcare industry — where Illumina is very well positioned, because it’s not only about the price of sequencing here.
It’s about the ability to develop and create large data sets that go across both the genome, the transcriptome, the methylome, and of course also the proteome. Combine that together, look at that from a bulk, the single cell, and also a spatial context.
There’s no other company in this industry that has the broad portfolio and also has the ability to create those data sets. We started with the 1 billion cell [atlas.] It’s a great example of a functional data set that is not only looking at something in time, but when you try to see what is happening to a cell when you change your up or down regulated gene, so I think these are types of the data sets that you want to look for.
So we feel good about where we are. Look, Illumina has been in a world for many years to deal with elasticity. We love elasticity. We can address any opportunity that we need to, and we see this market could be much much bigger. And we have both the capabilities, but also innovation pipeline to to be a very strong player in that.
So both developing the data sets ourselves, but also looking at flywheel, that together with our customers, we create data sets. Our customers can add on to those data sets, and we can create bigger and bigger data sets for training those models. So we are excited about that, and I’m not super concerned about what others are doing right now.
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Kyle Mikson, Cannacord Genuity (40:11)
[...] Congratulations on executing on the strategy day from 2024 numbers and the framework and all that. But I mean, is there upside to the high single-digit growth going forward? I think obviously you know as was discussed before, you got AI, multiomics, maybe a research inflection, clinical. I feel like that could be kind of longer term as well. So, could you approach double-digit growth perhaps while expanding operating margins to the high 20s? And what could that take really?
Thaysen (40:48)
Yeah. Look, Kyle. I think yeah, this is absolutely not impossible. I mean, there’s there’s definitely upside to a high single-digit growth. But but remember a few things here. When we were laying out our strategy in 2024, we were in a negative growth arena, and now we have stepped ourselves into mid-single-digit growth. I think we want to also show we can do high-single-digit [growth] before we go beyond that. But but I do. I mean, and we you can also see over last few years there’s been a lot of headwinds that this industry and and Illumina specifically had to deal with. You can pick your one you want to talk about, but but there’s been plenty. So we think that we are operating in a in a significant market opportunity space, particularly in clinical, and we think that is going to continue to expand over the years. We are very well positioned when when the research segment is coming back with our multiomics portfolio, where we see a lot of grants being moving in that direction right now, and and then of course the whole AI space where there will be substantial data sets generated and and looked for, and I think that is something that when you with our 1 billion cell atlas shows that we are very well committed. It’s not about just selling sequencers anymore. In that space, our customers and the broad spectrum of customers are looking for companies like Illumina that can actually generate those data sets. They’re much more interested in what those data sets can generate, and obviously the models that can perform rather than the sequencing itself. So we are very well positioned, and there’s certainly opportunities to grow beyond that.
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Dan Leonard, UBS (42:41)
Thank you a bunch, Jacob and Ankur. You talked a bunch about BioInsight. When would Bio Insight become more visible in the P and L? Would that be as soon as next year, or is that a farther out event?
Thaysen (42:58)
Yeah. So I think first of all, we actually established BioInsight that approximately a year ago, which was the combination of our software part, which is actually doing extremely well. Also, and I think for those of you who visited our booth and at our symposiums at ASHG, we really presented how important software is for the ecosystem, so very excited about that. But we combine that with the data because there’s a lot of synergies between the two. You have to generate data, but you also have to develop models on top of that. So we are as we mentioned in 2024, also that the data and of course the multiomics business would start to generate one to 2 percent in the latter part. So here in 2027 of our strategic period, when we look in 2024, and that is playing out very well. But I think you will, depending on how that AI opportunity plays out, which we actually looks very very promising. This could be substantially bigger. But maybe Ankur, you have comments on that.
Dhingra (44:02)
Yeah, I think our view right now is somewhere in 2027 there should be some sort of color we should be able to begin to add the billion cell atlas, and then [...] we’ve talked about it expanding to 5 billion cells, certainly would be one of the first drivers within that. Jacob mentioned we actually have a very broad spectrum of customers in discussion about the cell atlas and several other new bio AI-based applications that they are working on. But we anticipate that somewhere in 2027, it does become more visible. And the whole point around the new new products and bio insight adding one to two points of growth for 2027 would certainly be an area.
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Dan Brennan, TD Cowen (48:00)
Great, thank you. Maybe just one on AI. I know there’s been a lot of discussion there so far, but if we think about the next, say, three plus years or so, is the opportunity more on the service side for Illumina through BioInsight, or is it going to be substantial or opportunity on the placing boxes and traditional flow cells? I’m just wondering how that splits out, and then related to that, you know, you talk about the billion cell atlas. So, is the way to think about it right now, from what you’re seeing in your pipeline and backlog, is it more going to be on single cell and multi omics versus traditional whole genome?
Thaysen (48:39)
Okay, so good, good question, Dan. I think how we think about the AI opportunity, as I mentioned, the industry right now is looking for some huge data set that is both reproducible and standardized. And the problem is when you have individual PIs or individual postdocs or others that are sitting and building small data sets, these are quite noisy and and it’s very difficult for models to actually train well on those.
We have seen many times that they are better in predicting where the data is coming from rather than predicting the biology that is that that what the what the the experiments were actually telling you. So that’s why we’re seeing a big demand and a big ask for customers that would like us to help generate large data sets that can help build those.
That is because we can when we do those large data sets, obviously we also spend time on ensuring they’re reproducible, standardized, and so on. And I think there’s a big demand for that. So there’s certainly a huge opportunity for Illumina in what you can call in a service business format.
At the same time, we are of course in the business of placing instruments. So you could imagine there’s a lot of customers saying, “We can get access to the Illumina data sets. Can we augment that with our own data that we might want to keep to ourselves because of various reasons?
So I think we will see a lot of demand for that also. So it will be a blend. But versus what we traditionally think about it, we also think there are big service opportunities.
This plays extremely well to Illumina where we’re sitting today. The capabilities we build up over the the last few years in this space is probably not fully understood, but we are we have really become experts in how to think about or how to do perturbation for different cell lines and what it takes to build that quality, and and that’s what some of our customers are really seeing a lot of benefits in.
Dhingra (50:39)
Yeah, let me add just a couple more things on the BCA (Billion Cell Atlas) and the way you think about it. Jacob covered it extensively. the The power of our portfolio by having significant part of the the multiomic workflows is that the pathway where we stand today looks like combination of whole genome and proteomics, combination of single cell and transcriptomics, combination of spatial and transcriptomics, all of those combinations based on either specific organs where the the research is or specific disease pathways where the research is happening. So that combination of the different multiomics would vary, but that’s what different types of data sets could be. As we’ve said, our billion cell atlas is a starting point, which is helping prove causality in many areas already. But we do see a pathway for multiple combinations, as I was describing, of additional data sets that can be very meaningful and powerful going forward.
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Moderator (54:14)
Thank you. This concludes the Q and A section of the call.