The ultimate bio x AI thematic primer for investors
I strongly believe this is the most actionable and informative thematic primer ever published on bio x AI. This primer describes the “universe” of bio x AI from an investment perspective—including the overall vision, subthemes, and individual tickers.
Bio x AI will be one of the most important scientific themes in all of history and is only just beginning the exponential curve seen in other AI-related sectors.
Dario Amodei set an incredibly ambitious goalpost for bio x AI in Machines of Loving Grace — to cure most diseases within 5-10 years.
In October 2024, this sounded absurd. LLMs could barely make it through a conversation without hallucinating. Curing even one disease felt eons away.
But now? It still sounds like an incredible leap but no longer seems quite so ridiculous after two years of exponential progress in AI. Dario now explicitly states that he sees the same exponential trend for biology x AI as we’ve seen in coding, math, and other domains.
“(AI agents) are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.”
— Dario Amodei, September 23, 2026
It’s not only Dario anymore either. The brightest minds in AI have shifted from words to action. Anthropic recently established a wet lab in San Francisco and published a novel biologic result, and continues to ramp investment ahead of IPO. Jeff Dean, co-founder of Google Brain and legendary engineer with too many achievements to count, left Google after 27 years to co-found a biology startup, Discovery Loop. Demis Hassabis stepped down from day-to-day at DeepMind to focus on biology through Isomorphic Labs.
This theme is no longer at the conceptual stage of small-scale pilots. There is a tsunami of interest and investment flooding in.
The core vision is the loop
The core vision of bio x AI is the loop. This shared vision unites all of the subthemes in the dream of automated drug discovery.
The core loop is four steps: design-make-test-learn.
Rather than manually sourcing, developing, and testing drug candidates, the entire process would be condensed into an automated, recursive loop directed by frontier intelligence. This is the explicit vision of Anthropic and Discovery Loop, as the name suggests.
Fully automating and accelerating this recursive loop is how we get 100 years of progress in 1 year and have a shot at achieving the vision of curing most diseases.
Breaking down the subthemes
The bio x AI theme is quite nuanced because there are at least ten intersecting subthemes that come together in support of the discovery loop.
Every subtheme maps onto a step of the design-make-test-learn loop. Designs start in silico, where R&D software and biological foundation models propose molecules with preliminary validation in simulation software. Those designs become physical through DNA & RNA synthesis, then get tested and experimentally verified on the instruments and consumables of life science tools. The results feed biological data generation, which produces the training data that makes the next round of designs better and closes the loop. Autonomous labs & physical AI turn this loop into something that can scale around the clock, unencumbered by human labor.
The companies involved in the loop, either on their own pipelines as clinical-stage biotechs or for partners as discovery platforms and services, produce drug candidates. Every candidate that comes out of the loop passes through CROs & CDMOs for trials and production and scales up through bioprocessing & manufacturing. Personalized medicine, sequencing, and clinical data act as another source of data to augment the source data for drug discovery.
Without further ado, let’s break down the companies that sit at the center of this buildout and vision. Note that many of these companies actually belong in multiple categories and have nuance that is difficult to capture in a few sentences. I simplified out of necessity, but tried to highlight details for each company that I believe the market will perceive as most relevant/torqued to the bio x AI ramp.
1. DNA & RNA synthesis
When an AI model designs a new antibody or gene, the output is just a sequence of letters on a computer. To test whether it works, someone has to physically build that DNA or RNA.
These companies manufacture custom DNA and RNA to order. Their speed and cost determine how fast the design-make-test-learn loop can spin. AI models often propose thousands of variants at once, so cheap, fast, high-volume synthesis matters more than ever.
Revenue is mostly per order, so it scales directly with experiment volume. This explains why these stocks have led the recent surge and rerated so quickly — they have the purest torque to an uplift in experimental volume as the loop becomes a more concrete possibility.
Twist Bioscience (TWST) – Twist writes DNA on silicon chips, making thousands of DNA sequences at once instead of one tube at a time. That makes it cheap and fast to produce large libraries of variants, which is exactly what AI design needs to accelerate the loop. Twist has also moved up the chain into producing the proteins themselves and running antibody discovery work. AI-related orders are inflecting rapidly: $25M in fiscal 2025, and management is guiding for $50M in fiscal 2026 and $100M in fiscal 2027. They are the most obviously torqued company to the loop, and the stock has benefited accordingly.
GenScript (1548.HK) – One of the largest gene synthesis providers globally. It also makes peptides and antibodies. Its pitch versus Twist is breadth: the ability to take a customer from a DNA sequence to a finished protein and experimental data in one workflow. GenScript's AI drug discovery business doubled year over year in H1 2026, with growth driven by AI-native biotechs, model developers and big tech companies entering life sciences for the first time.
TWST has taken some market share from them due to speed and American geography, but GenScript remains a major oligopolistic player and they share the same enormously strong thematic tailwind.
Azenta (AZTA) – Azenta offers GENEWIZ services, which include gene-to-discovery offerings such as antibody production, viral packaging and mRNA synthesis. Compared to Twist, GENEWIZ is the broader, service-heavy version: it builds the construct and can sequence-verify it in the same order, but is a slower process. Additionally, it is not a pure play, with the majority of revenue driven by biorepositories (which store, freeze, and process samples).
Danaher (DHR) via IDT – IDT is a leading supplier of short custom DNA/RNA and CRISPR reagents. Subsidiary of Danaher, not separately listed.
2. Life science tools
These are the “picks and shovels” of biology. They make the instruments, chemicals and consumables that every lab uses, whether it’s a university, a biotech or big pharma. Most earn money two ways: selling instruments, which is lumpy and tied to budgets, and selling the consumables and service contracts those instruments need, which is recurring.
How would AI help these companies? AI-driven discovery runs more experiments, and every experiment uses up consumables. Robotic “autonomous” labs need instruments that software can control. That could push customers to replace older equipment early, as well as provide new high margin software opportunities. Note that the biggest players are diversified conglomerates and not pure plays.
Thermo Fisher (TMO) – The largest and most diversified tools company. It spans several of the subthemes named here, with a footprint in lab instruments, reagents, clinical trial services (PPD), contract manufacturing (Patheon) and diagnostics. It dominates cryo-electron microscopy (cryo-EM), which produces 3D protein structures for models like AlphaFold, and it owns Olink (protein measurement). One knowledgeable investor told me: if you want to own one name in this space to sleep well at night, it is Thermo Fisher.
Danaher (DHR) – Similar to TMO, a very diversified name. Danaher is a holding company of about 15 businesses that together span nearly the whole chain. Some of the most relevant for the AI buildout include:
- Cytiva: bioprocessing equipment for making biologics
- Beckman Coulter: lab instruments and cell imaging
- SCIEX: mass spectrometers
- IDT: custom DNA/RNA synthesis
- Abcam: research antibodies
Segments of DHR will show up throughout this primer in different sections.
Revvity (RVTY) – Lab instruments (including high-content cell imaging), reagents, newborn-screening and immunodiagnostics. In particular, they lead in reagents—used to validate drug candidates against many targets—and mentioned seeing a significant increase in orders due to AI and “lab in the loop” at the recent September Morgan Stanley Healthcare Conference.
Also notable for its software business (discussed in another section).
Bruker (BRKR) – High-end research instruments: timsTOF mass spectrometers used heavily for proteomics, NMR, and spatial biology tools (NanoString’s CosMx/GeoMx, acquired 2024) that map which genes are active where in a tissue. Proteomics and spatial data are both frontier training data for biology AI. Of note, they also have inflecting semiconductor equipment exposure, which led to a stock price jump in June. Their spectrometer tools are most torqued to data generation / model improvement efforts such as those from Periodic Labs.
Waters (WAT) – A leader in quality control, strongest in liquid chromatography and mass spectrometry for pharma. It became much bigger in February by combining with BD’s Biosciences and Diagnostics businesses, which added flow cytometry, single-cell analysis and diagnostics. Every drug candidate must eventually be QC’d on this kind of equipment before it can advance.
JEOL (6951.T) - Japanese scientific instrument maker. The primary alternative to Thermo Fisher for cryo-EM, and also makes NMR spectrometers. Both produce experimental 3D protein structures, the ground-truth data that structure-prediction models like AlphaFold and Boltz are trained on and validated against. However, JEOL isn’t a pure play as much of their revenue comes from semiconductor equipment, especially electron-beam mask writers. Consider JEOL a semiconductor-cycle stock with bio-AI optionality if cryo-EM becomes a major bottleneck.
OmniAb (OAB) - Picks and shovels for antibody discovery, run as a licensing business. The core technology is a set of genetically engineered animals (rats, chickens, mice and others) that produce antibodies with human sequences. The AI angle is xPloration, a high-throughput single B-cell screening instrument that uses machine learning and lab automation. OmniAb sold two instruments in Q2, and management pitches xPloration as a way to generate the large datasets the field increasingly needs. Investor day is October 6th.
3. Autonomous labs & lab services
Autonomous labs are the vision for robotic facilities where AI agents design experiments, robots run them around the clock, and results feed straight back into the AI with minimal human involvement.
This turns out to be a surprisingly difficult process. Almost all tools companies above have a significant degree of instrument automation, but end-to-end, programmable automation—the kind that gets dropped into a recursive loop—is a completely different story that is just getting started. The “lab-in-the-loop” model was pioneered by Genentech, a subsidiary of Roche (ROG.SW/RHBBY).
Anthropic’s Model Hardware Standard and Astra have been a model intelligence leap that puts this theme more squarely in the spotlight. One of my strongest convictions is that we are crossing the threshold of model capability where lab automation truly makes sense and will deliver enormous value to the end users.
Ginkgo Bioworks (DNA) – I have written extensively about Ginkgo as my highest conviction play. They are a failed syn bio company that has pivoted and gone all-in on lab automation. They offer two services: building the customer an autonomous lab using their proprietary RACs (Reconfigurable Automation Carts) and Catalyst software, and providing cloud lab services particularly ADME-tox testing.
Tecan (TECN.SW) – Market share leader in liquid-handling robots, the arms that pipette samples in automated labs. These are among the most essential pieces of equipment for every wet lab. Named an early partner of Anthropic’s Model Hardware Standard. It also builds automation that other instrument makers sell under their own brands.
Agilent (A) – A leader in analytical instruments (chromatography, mass spec, spectroscopy), the machines that confirm what a molecule is and whether it’s pure. It also sells cell-analysis tools and cancer companion diagnostics. They struck an enterprise-wide AI collaboration with OpenAI and BCG in June and formed a dedicated autonomous-lab group.
XtalPi (2228.HK) – XtalPi is aiming for the niche of large-scale robotic chemistry and quantum physics-based modeling, with a focus on automated experiment solutions for tasks such as chiral chemistry and material characterization. The robotics and “AI for Science” side is now the growth engine. AI4S Intelligent Solutions revenue grew 136.4% in H1 2026.
4. Clinical-stage biotechs
These companies use AI to invent their own drugs and then run the human trials themselves. They make money only if a drug gets approved and sold, or if a big pharma company pays to license it. These tend to have more binary results as their share prices move sharply on trial readouts. The dream is to become a platform company, capable of repeatedly using proprietary AI-based approaches to source and design new drugs.
Absci (ABSI) – They have an internal foundation model called Origin-1, a generative platform for designing antibodies from scratch. Their key drug is ABS-201 (anti-prolactin receptor) for hair loss and endometriosis. Interim hair-loss proof-of-concept data is due 2H26, with full 26-week data in early 2027. A Phase II in endometriosis starts Q4 2026, with data in 2H27.
AbCellera (ABCL) – Antibody discovery engine now running its own pipeline. Its edge is years of high-throughput single-cell antibody screening data, the kind of proprietary labeled data a model would need. They recently reported positive Phase II drug results in August for ABCL635 (NK3R antibody for hot flashes) with very good clinical data. Their sheer quantity of other pipeline antibody drugs, including one for atopic dermatitis, hints that they’re moving toward realizing the dream of becoming an “AI drug factory.”
Insilico Medicine (3696.HK) – Insilico is one of the most commercially validated AI biotechs. Its Pharma.AI software/model suite covers the whole front half of the loop: PandaOmics for target discovery and Chemistry42 for generative chemistry. Their landmark drug, rentosertib, is touted as the first drug where AI found both the target and the molecule. Preliminary data supports it use for pulmonary fibrosis and suggest that it can favorably shift the proteomic aging clock. Insilico claims that Pharma.AI has produced more than 20 assets at the clinical or IND-enabling stage. They recently disclosed a co-development deal with an unnamed “frontier foundation model lab,” valued at up to tens of millions of dollars.
Recursion (RXRX) – Phenomics + chemistry AI platform with a tumultuous history, but now rebounding with a focus on AI drug discovery. They co-developed Boltz-2, an open-source model that predicts both protein structure and binding affinity, with MIT. It also has Phenom (a cell-imaging model) and licenses its TxFM RNA model to Tempus. Notably, they also own a lot of compute, more than any other pharma except for Eli Lilly. The key question now is upcoming clinical readouts. An update on the FDA path for REC-4881 (MEK1/2 inhibitor for FAP) is due in 2H26. REC-1245 dose-escalation data is also due 2H26, with more readouts in 2027.
Generate Biomedicines (GENB) – Generative protein design company that IPO’d in February 2026. Chroma, its generative protein model, generates new protein structures and can be steered toward chosen shapes, motifs or functions. GB-0895 / golukibart (anti-TSLP) goes after the same target in severe asthma as AstraZeneca/Amgen's Tezspire. The pitch is dosing every six months instead of every month. The AI was used to raise binding affinity and extend half-life while keeping the antibody specific. Other notable drugs are GB-4362, an MMAE neutralizer meant to blunt ADC side effects, which has its first Phase I cohort enrolled, and GB-5267 (MUC16 armored CAR-T).
Relay Therapeutics (RLAY) – Relay’s Dynamo platform approaches drug discovery from a computational/simulation angle rather than generative AI, which most other companies in this category use. Relay solved the full-length cryo-EM structure of PI3Kα, the most mutated kinase in cancer, and ran long simulations of how the protein moves. This helped inform the design of zovegalisib, which better selects for only the mutant form of the kinase and thus reduces toxicity. It received FDA Breakthrough Therapy designation in second-line HR+/HER2- breast cancer, with a frontline Phase III planned for early 2027.
5. Integrated platforms
Integrated platforms stitch together multiple pieces—putting biology models, the scientist’s own data, AI agents and a hand-off to physical labs in one environment. The scientist designs a candidate, sends it out to be made and tested, and gets the results back into the same system to improve the next round. Note that these are megacap companies, so the exposure is diluted, but their partnerships are a strong signal of which other players sit at the center of the buildout.
Lilly TuneLab (LLY) — Arguably deserves its own separate article. Lilly opened up AI models based on its decades of proprietary historical data for free to startups and other companies, in exchange for data inputs from the users. The platform itself aims to comprehensively guide users through the discovery loop. They are partnering with many of the companies mentioned in this piece for software and wet lab services, including TWST/DNA/SDGR/RVTY/CRL/GenScript. TuneLab had onboarded more than 70 partners within about five months and is targeting 150 by the end of 2026.
Amazon Bio Discovery (AMZN) — Launched in April. It gives scientists a catalog of biology foundation models from partners such as Apheris and Boltz. Scientists can fine-tune models on their own lab data, and teams that built their own models can host them there. Top candidates can go straight to an integrated network of lab partners, including Twist Bioscience and Ginkgo Bioworks, with A-Alpha Bio expected to join. Early adopters include MSK, Bayer, the Broad Institute and Voyager Therapeutics.
NVIDIA (NVDA) — NVIDIA offers BioNeMo, a collection of models and a full open development platform for lab-in-the-loop biology. NVIDIA and Lilly also announced an AI co-innovation lab in South San Francisco, whose first technical priority is a continuous learning system that runs data between robotic lab equipment and AI models around the clock. NVDA is also making its own private investments in numerous companies to support the buildout.
6. Biological data generation (-omics)
Our understanding of biology is still fairly rudimentary. In order to understand how cells actually work under different conditions, a giant library of data has to be generated experimentally. These genomics/proteomics/transcriptomics companies make instruments that turn biology into that data.
More specifically, sequencers read DNA and RNA. Single-cell tools measure what’s happening inside individual cells. Spatial tools show where each gene is active within a piece of tissue. The ultimate goal is to build toward a “virtual cell”: a model that can predict how a real cell will react to a drug before anyone tests it in the lab.
Because these companies are paid mostly on consumables, more AI-driven experiments mean more revenue. A few, like Illumina, are also starting to sell the data itself.
10x Genomics (TXG) – One of the big thematic winners YTD, 10x makes the tools that measure individual cells. Its Chromium platform reads what’s happening inside thousands to millions of single cells, and its spatial platforms (Xenium and the new Atera) show where genes are active within a slice of tissue. This opens up enormous realms for biological insight that were not previously possible and has contributed to rapid growth.
Illumina (ILMN) – Illumina is the dominant DNA sequencing market leader, making the machines that read genetic code at massive scale. More recently, they announced the Billion Cell Atlas, the world's largest dataset of what happens inside cells when individual genes are switched off, opening up a new revenue line of selling data.
Oxford Nanopore (ONT.L) – Oxford Nanopore differentiates on real-time sequencing pulling DNA/RNA through a tiny pore and reading the electrical signal. The new CEO has focused less on offering AI training data and more on commercial execution and margins, seeking out new biopharma and clinical customers.
Pacific Biosciences (PACB) – Most sequencers read DNA in short fragments and stitch them back together. PacBio reads long stretches in one go, which can catch additional markers like methylation. PacBio signed its first significant AI-related project in 2026: sequencing about 100,000 samples and providing data for Basecamp Research's Trillion Gene Atlas. Basecamp Research trains frontier AI models for therapeutic design and just closed a 140M Series C on September 23 to accelerate their efforts, sending PACB surging in unison.
Qiagen (QGEN) – Qiagen is upstream of the sequencers discussed in this section, and leads in informatics software + sample preparation: the kits and instruments that extract DNA and RNA from blood, tissue and other samples before any sequencing or testing happens. They benefit from an upward inflection in sequencing use. They are leaning into curated data for AI as well: building graph-based AI on a 25-year-old database with NVIDIA and planning to offer at least 14 AI software products by 2028.
7. R&D software & simulation
This category covers several angles. First, simulation as validation. Simulation software, usually applying physics + computational modeling, tightens the loop by allowing for some degree of testing to happen before the molecule needs to be physically made.
Second, biosimulation is an important subtrend. Regulators are starting to accept computer models in place of some lab, animal and even human studies. The FDA has announced plans to phase out animal-testing requirements for antibody drugs, and Certara’s simulations were accepted in place of human studies for a leukemia drug’s approval.
Finally, there is an emerging layer of AI co-scientists — AI agents that act as research assistants. They read the literature, propose hypotheses, choose experiments and analyze results, closer to a junior scientist than a prediction tool. Several companies have launched agentic "co-scientist" products or strategies in the past year, with Revvity and Schrodinger getting the most traction.
Schrödinger (SDGR) – Physics + ML simulation software. They play a central role in the computational / predictive modeling of molecule design. The most exciting AI-related development is their AI co-scientist, Bunsen. At the recent Morgan Stanley Healthcare Conference, the CEO said Bunsen drew more customer interest than any product in his 20 years at the company, with lines at their conference booths. It solves a talent bottleneck. Instead of hiring hard-to-find experts, agents run through the night and manage tasks/restart failed jobs.
Revvity (RVTY) – Their Signals software acts as an integrated laboratory notebook and data platform. They launched Signals AI in June, which lets scientists work with research data in natural language rather than complicated code/data processing. They are billing this as ERP-like, the scientist’s system of record, and aiming to double Signals adoption in the next 4-5 years.
Certara (CERT) – A major emerging player in biosimulation; FDA accepted its models in place of clinical studies. They also offer an AI-powered pharmacology modeling platform. In July 2026 it partnered with NVIDIA to add the BioNeMo Agent Toolkit to its platform.
8. CROs & CDMOs
These are the outsourced labor of drug development. CROs (contract research organizations) run studies for pharma and biotech, both preclinical safety testing in animals or alternatives and human clinical trials. CDMOs (contract development and manufacturing organizations) make the drug itself, from small batches for early trials up to commercial scale. Most smaller biotechs own neither capability, so nearly every AI-designed molecule passes through these companies on its way to patients.
The AI tailwind would primarily impact volume. If the loop truly works and produces more viable drug candidates, each one still needs to be synthesized, safety-tested, run through trials and manufactured, and that work gets outsourced.
IQVIA (IQV) – The largest clinical CRO and also one of the largest owners of healthcare data (prescriptions, claims and real-world patient records). That combination makes it the natural home for AI in clinical development. It also has a large software arm and in March launched IQVIA.ai, a unified AI agent platform built with NVIDIA.
Charles River (CRL) – Charles River is the leader in preclinical safety testing, the studies every drug must pass before human trials. Historically that has meant animal studies, but they have pivoted to alternatives after FDA's April 2025 plan to cut animal testing (in favor of organ-on-a-chip, computer models and advanced lab tests). In June it joined Lilly TuneLab, contributing safety-testing and validation to Lilly's AI platform.
WuXi AppTec (2359.HK) and WuXi Biologics (2269.HK)
Large Chinese CRDMO, which takes molecules and does outsourced manufacturing, testing, validation, and development. AppTec covers small molecules, while Biologics does the same for antibodies, bispecifics and ADCs.
Lonza (LONN.SW) - The largest biologics contract manufacturer outside China and where many AI-designed antibodies would likely get made at scale. It has divested other arms and is now a pure-play CDMO that would benefit from increased biologic drug volume and from biotechs shifting work away from WuXi due to BIOSECURE Act concerns.
There are many other CROs/CDMOs, but I kept this list to a few names that felt most involved in a potential AI inflection.
9. Bioprocessing
These are the “scale-up” layer. Modern drugs skew toward biologics (antibodies, vaccines, mRNA, cell and gene therapies), which are larger, more complex molecules. Bioprocessing is the industrial process of manufacturing these molecules and processing them into a pure, safe drug. These names benefit if AI produces a higher volume of viable drug candidates. Of note, most consumables, including the specific vendor, are locked in by time of FDA filing, so these wins can scale even before drugs hit mass market.
Note that bioprocessing was already emerging from a deep cyclical trough even before this new AI tailwind. Onshoring and the shift of drug development mix toward biologics represent additional potential secular tailwinds for the sector. Bioprocessing names are a more downstream beneficiary of AI investment and require viable drug candidates to enter the pipeline first.
Repligen (RGEN) – The most focused pure play in bioprocessing, as other names are diversified across a variety of sectors. Offers filtration, chromatography and perfusion tools used to make antibodies, mRNA and cell and gene therapies. They are winning a high share of bioprocessing orders for newer biologics.
Danaher (DHR) Cytiva unit - Cytiva is Danaher's bioprocessing business and the scale leader in antibody manufacturing tools. AI drug design is well suited for antibodies, where Cytiva is the default supplier through Protein A, used for antibody purification. Danaher's June bioprocessing summit also highlighted a shift toward small-batch and patient-specific manufacturing.
Avantor (AVTR) – A major supplier of disposable consumables. High-throughput and automated labs would use far more plates, tips and reagents than manual benches. Avantor is a consumables toll collector on that experiment volume that would benefit from automated lab scaling.
Sartorius Stedim Biotech (DIM FP) – Sartorius Stedim is the bioprocessing arm of Sartorius and the leader in single-use manufacturing: disposable bioreactor bags, filters and fluid-handling systems that replace steel tanks. Its most loop-relevant product is Ambr, a line of automated mini-bioreactors that run dozens of small parallel cultures to work out how to manufacture a drug before scaling up.
10. Individualized medicine + clinical data
This group has a somewhat tenuous connection to the core bio x AI buildout, but thus far has traded in a highly correlated fashion with theme leaders such as $TWST and $TXG. They are worth a mention due to correlation, but I will keep the descriptions brief. Notably, many of these names are strong businesses and have tailwinds/catalysts not directly related to the rest of the bio x AI ramp described here.
There are a number of sequencing and diagnostic companies in possession of valuable data that can feed foundation models, although this is not their core business and the value of this clinical data in drug development is still an evolving question. Tempus AI (TEM) is the biggest player for genomic sequencing tests in oncology. They are sitting on a wealth of data, 45M+ de-identified patient journeys that can potentially feed oncology foundation models. Pending purchase of Personalis for $1.5B.
Numerous other names that provide sequencing or other testing services also have potentially valuable data, including Guardant (GH) for tumor liquid biopsies, GRAIL (GRAL) with a machine learning-based multi-cancer diagnostic test pending FDA approval, Natera (NTRA) with its longitudinal minimal residual disease (MRD) cancer test, Caris (CAI) for whole exome/transcriptome profiling, GeneDx (WGS) for rare diseases, and Adaptive (ADPT) for immune receptor profiling.
Finally, it’s worth mentioning Moderna (MRNA). They represent the dream of individualized cancer vaccines. They reported the first positive Phase III data for a personalized mRNA cancer vaccine in resected melanoma on Aug 19, sending the stock flying. Maravai (MRVI) provides picks and shovels for personalized mRNA. Its CleanCap reagent goes into nearly every mRNA vaccine.
Quick FAQ
Q: There are some tickers missing here.
A: Definitely — this is meant to be a fairly comprehensive description of the universe but difficult to be all-encompassing. If there are any companies readers want to highlight, please leave them in the comments and I will likely write a follow-up article.
Q: There are some important details missing about some of the companies.
A: Also true — I tried my best to highlight relevant details but had to be selective in scope to get the piece out. Again feel free to comment, and I will likely include them in a follow-up article.
Q: I want to focus on a more concentrated portfolio. What single stock names and themes should I select?
A: I’ve written at length about $DNA as my highest conviction play. The primary topic of my next piece will be a basket of 7-10 names that I believe best represent the next phase of the bio x AI inflection.
I poured hours writing this article to expand the content and research beyond merely regurgitating AI. Please consider supporting with a like or a follow!
This is part 1 of a free multi-part series on bio x AI. Part 2 will cover individual stocks and my favored subthemes. Follow me to not miss it.
Disclaimer
This primer is for informational and educational purposes only. It is not financial, investment, legal or tax advice, and it is not a recommendation or solicitation to buy or sell any security.
The companies discussed are examples used to illustrate a theme, not endorsements. Many are early-stage, unprofitable, or dependent on binary events such as clinical trial readouts, regulatory decisions and pending acquisitions, and their shares can be extremely volatile. Some trade on foreign exchanges or carry geopolitical and regulatory risks. Forward-looking statements, including company guidance and management commentary cited here, may not come true.
Information is drawn from public sources believed to be reliable, but I can’t guarantee its accuracy or completeness. I am an individual investor who may hold positions in several of the stocks mentioned, and I may trade them at any time without updating this post.
Do your own research, consider your own financial situation and risk tolerance, and consult a licensed financial advisor before making any investment decision.