It looks and feels responsible from the inside.

But what happens to often in practice is that when the data finally lands, most of the field has either:
A) Never heard of you and doesn't understand the problem you were solving.
B) Takes their interpretation of your results from whoever else was willing to talk about the space ahead of time.

Don’t believe me? Let’s take a look at one of the best examples from just a couple years ago - Vertex vs bluebird bio.

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The Problem

On December 8, 2023, the FDA approved 2 gene therapies for sickle cell disease on the same day:
- Vertex and CRISPR Therapeutics’ Casgevy.
- Bluebird bio’s Lyfgenia.

Both promised to change the lives of patients with a devastating inherited blood disorder. Both carried multimillion-dollar price tags, and they each had strong clinical data behind them.

Casgevy pulled ahead almost immediately on collections, treatment center activations, and mindshare. In all fairness, a lot of factors contributed to that outcome (pricing and commercial infrastructure being two of them). But there is one other factor. There’s one factor that cannot be ignored: the CRISPR category had been publicly narrated for a decade before Casgevy launched.
Lentiviral gene therapy had not.

By the time Casgevy hit the market, “CRISPR” was in the vernacular. Doudna and Charpentier had won a Nobel Prize. The germline editing debates had played out in the mainstream press. TED talks, magazine covers, documentaries, and thousands of hours of public discussion had already established what CRISPR was, it’s potential impact, etc. When the first CRISPR therapy launched, the audience was primed.

Lyfgenia had to explain its own existence at the moment of approval. Lentiviral gene therapy is older, clinically mature, and arguably had more long-term follow-up data than Casgevy. However, most people outside the field had never heard the word “lentiviral.” The category had no public narrative to slot the launch into.

I’ve seen it myself inside of biotech, time and time again.
When the ground is prepared in advance, launches land.
When it isn’t, it’s a much more risky roll of the dice.

How to Address This Responsibly

Ok, you see the value here. Maybe you should take a more active role.
But certainly there is a still an element of risk… right?
You certainly don’t want to overshare or distribute IP.
Agreed.
Therefore,3 guiding components to building out a responsible strategy are outlined below.

1. Where the line sits

The proprietary core stays sealed. Your specific mechanism, your specific data, your specific manufacturing process, your unpublished results. All of that stays in the lab until the paper is submitted. The line hasn’t moved.

But everything around that core is fair game:

• The unmet need you’re targeting.

• Why existing approaches have fallen short, or the limitations they’ve experienced

• The theoretical potential of a better approach.

• The surrounding technologies and adjacent problems.

• Manufacturing and regulatory challenges the field is grappling with.
  

You can talk about all of this at length without leaking a single proprietary detail.

And, yet, so often, the academic-trained scientist conflates the 2 categories.
They apply the same protective silence to the surrounding conversation that they apply to the core data, and they lose an enormous amount of ground because of it.

2. How you own the problem space

Once you accept that the surrounding conversation is yours to have, the question becomes how to have it. Short-form video? Substack? LinkedIn posts? A vlog? Podcasts?

My recommendation: run a high-throughput screen.
Try everything.
Formats vary in how they land depending on the person, the audience, and the topic.

Short-form LinkedIn posts breaking down one aspect of a problem at a time.
Long-form Substack pieces contextualizze the broader landscape.
Podcast appearances arguing that a problem exists.
Video explainers of adjacent technology.
Conference talks that walk through your view of the field.
Op-eds.

All of it counts. All of it can stack. Find out what works for you.

Social media does the heavy lifting for most people because that’s where the audience consumes information now. Conferences are a smaller piece of the mix than most executives believe, because the reach is a fraction of what a well-targeted post can generate. Different people in your audience live on different channels, so being present in more than one is an important component.

Cadence is more important than format. Sporadic doesn’t work. You need enough surface area over time, that when someone searches your problem space, you are one of the first primary sources they encounter.

Build toward that goal.

3. Overcoming the fear of “Getting scooped”

The academic reflex says: don’t talk about the work until it’s published, because someone might scoop you, because you might get the interpretation wrong in public, because your credibility depends on the peer-reviewed venue.
Absolutely.
And again, they apply to the core proprietary data.
Almost none of them apply to talking about the surrounding problem.

Also, you’re missing the cost of silence. Publications, for the most part, won’t reach the relevant people on their own. There’s nothing worse than getting a paper out, and only 12 people seeing it.
Building the audience is work you must do in advance, because there is no audience-building mechanism that activates when the paper drops.

Secondly, there’s a second failure mode that’s arguably worse than obscurity - someone else narrates it.
Competitors talk about their approach as the answer to the problem you were also trying to solve.
Analysts frame the category based on whoever was loudest.
Journalists write the primer based on who was willing to give quotes.
By the time your data publishes, there is a significant risk that your audience has already been trained on a framing that isn’t yours.

Don’t fall into the trap of thinking it’s a binary decision between talking publicly or staying quiet. Much more is at stake.
Being the primary source people find on a topic means you set the frame everyone else has to argue with.

Showing up second (or last) means you’re arguing against a frame that was built without you.

What This Means For Your Role

Three archetypes should act differently if you buy this argument.

Scientists: Post on LinkedIn about the problem you’re working on, without getting into the specifics of your solution. Talk about the problem itself: the limitations of what’s out there, the reasons you got into this space, why existing approaches have hit their ceilings.
(I found a lot of success doing this early on in my career)

If you’re going to a conference, use the visibility to talk around the problem you’re solving and let people know you’re excited to share more about the solution when the time comes.
Start small.
One post a week about the science you know better than anyone else in the room can put you ahead of everyone else.

Executives and Leaders: Rip off the band-aid. Get into long-form formats. Podcasts, essays, interviews, panel discussions. Get those clipped into short form content for posting on socials. Share your views on the limitations of existing approaches and where the field is heading. The war of ideas is being fought whether or not you show up. If you don’t venture into it, someone else’s views become the default. Own that position.

Investors: This piece may be less actionable immediately. You already have direct access to the data behind the scenes. However, when you’re evaluating a company’s readiness for a major data drop, ask whether they’ve built an audience that will care when the paper lands. If they haven’t, factor that into your view of how the launch will go.

The Takeaway

There are 2 ditches on either side of this road.
In one, no one hears about your science because you never built the audience to hear it.
In the other, competitors did a better job of positioning the problem you’re working on, and their narrative became the default for how your data is evaluated.

Take the initiative.
Don’t fall into either ditch.

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Why this list looks the way it does

Every year I see the same thing happen at ASGCT.

Folks pick a lane the first morning, settle into AAV row, and stay there for four days. The CAR-T people stick with CAR-T. The manufacturing people stick with manufacturing. The clinical folks stick with clinical readouts. Everyone leaves having heard exactly what they expected to hear.

That is a real shame. Because the most useful conversations I’ve had at ASGCT have always come from the talk I almost skipped.

The gene therapy person who wandered into a CAR-T resistance session and finally understood why their AAV durability data looked weird in immune-active tissues. The CMC person who sat in on a clinical biomarker talk and walked out with a new hypothesis about lot-to-lot variability. The scientist who watched an in utero therapeutics session and started asking better questions about pediatric dosing.

Cross-modality exposure is how the field moves forward. So this schedule is built deliberately wide. AAV genome biology, CAR-T resistance, epigenome editing, exosome delivery, regulatory T cells, in utero therapeutics, ARPA-H funding strategy, CBER fireside chat, AI for sequence design, and yes, some manufacturing analytics for good measure.

Below you’ll find every session and every individual talk I’m planning to attend, organized by day. I’ve attached a ZIP file at the top of this post containing 58 calendar invites (one for each session and one for each individual talk) that you can drop straight into Outlook, Google Calendar, or Apple Calendar. Grab the whole bundle, pull out the ones you want, and delete the rest.

A few things that need flagging:

• All times are local Boston time (EDT)

• If you spot a session I missed that you think should be on this list, I want to hear about it.

Tuesday, May 12, 2026

8:00a–9:30a · Artificial Intelligence in Cell and Gene Therapy: Transforming Data into Discovery and Patient Impact

MCEC, Room 052AB (Exhibit Level) · Education Symposia

• 8:00a–8:20a Applying AI to accelerate discovery, improve therapeutic design, and optimize patient outcomes in CGT (Issam El Naqa, PhD)

• 8:20a–8:40a From resistance to redesign: engineering resilient CAR-T cells with generative AI (Andrea Schmidts, MD)

8:00a–9:45a · Engineering High-Performance CAR T Cells to Overcome Resistance and Improve Potency

MCEC, Room 258ABC (Level 2) · Oral Abstract Sessions

• 8:45a–9:00a Systematic discovery of CRISPR-boosted CAR T cell immunotherapies (Cosmas D. Arnold)

• 9:15a–9:30a Decoding CAR-T Cell Behavior In Vivo Through Integrated cell-free DNA Profiling (Daniela Cesana, PhD)

• 9:30a–9:45a Transient mRNA-Based STAT5 Augmentation Enables Lymphodepletion-Free CD19 CAR-T Cell Therapy (Megan Tennant)

10:15a–12:00p · Advances in Cell and Gene Therapies for Chronic Infectious Diseases

MCEC, Room 205ABC (Level 2) · Scientific Symposia

• 10:15a–10:41a CAR-T for HIV (Steven Deeks, MD, UCSF)

• 10:41a–11:07a Virus-specific T cells for treatment of viral reactivation after solid organ transplant (Thomas Pfeiffer, MD, WashU)

• 11:07a–11:33a A Lyme disease mRNA vaccine targeting Borrelia burgdorferi OspA induces strong immune responses and prevents transmission in mice (Norbert Pardi, PhD, UPenn)

3:30p–5:00p · Effective Communication for Funding, Peer Review, and Beyond

MCEC, Room 052AB (Exhibit Level) · Education Symposia · Co-chairs: Lynnea Olivarez (Ticket to Biotech), Florie Borel, PhD (Arbor Biotechnologies)

• 3:30p–4:00p Funders’ lens: what resonates when seeking capital (Cynthia Clayton, Clayton Bio Communications)

• 4:00p–4:30p Scientific peer/grant review lens: writing with clarity and precision (Sarah Ellinwood)

• 4:30p–5:00p Communication strategy lens: building your narrative beyond the application (Lori Lennon, Thinkubator Media)

(NOTE: I’m on the Communications committee, so I am a bit biased towards this one :D )

Wednesday, May 13, 2026

8:00a–9:45a · Emerging Clinical Evidence in CGT: Long-Term Outcomes, Biomarkers, and First-in-Human Insights

MCEC, Room 107ABC (Level 1) · Oral Abstract Sessions

• 8:00a–8:15a Gene therapy for Friedreich ataxia cardiomyopathy: safety and preliminary assessment of efficacy (Ronald G. Crystal)

• 8:15a–8:30a Circulating T-Cell Dynamics in a Responder From a Phase 1 Trial of CISH-Edited TILs for Advanced Metastatic Colorectal Cancer (Matthew Johnson, PhD)

• 8:30a–8:45a Zorevunersen demonstrates potential as a disease-modifying therapy in Dravet syndrome through 36 months OLE (Andreas Brunklaus)

• 8:45a–9:00a Single-cell profiling of PBMC subsets following rese-cel across RESET-Myositis, RESET-SSc, and RESET-SLE Phase 1/2 cohorts (Daniel Nunez, PhD)

• 9:00a–9:15a First-in-human clinical trial of AAV9-mediated gene augmentation therapy for CTNNB1 neurodevelopmental syndrome (Damjan Osredkar, MD, PhD)

• 9:15a–9:30a Single-Cell Identification of Predictive HSC Biomarkers Defines Clinical Response to Lentiviral Gene Therapy in beta-thalassemia (Maria Rosa Lidonnici)

• 9:30a–9:45a Long term follow up of the participants in the AAV9.U7-ACCA clinical trial for Duchenne muscular dystrophy (Megan Waldrop, MD)

10:15a–11:45a · Engineering Programmable Gene Therapy Systems

MCEC, Room 107ABC (Level 1) · Oral Abstract Sessions

• 10:45a–11:00a An engineered UGA suppressor tRNA gene enables AAV delivery and disease-agnostic in vivo gene therapy (Mengyao Xu, PhD, UMass Chan)

• 11:30a–11:45a A multimodal AI framework for engineering CRISPR-Cas proteins (Christian Plumeyer)

10:15a–12:00p · AAV: Mechanistic and Translational Advances in Alternative Viral Gene Delivery Systems

Westin, Commonwealth Ballroom ABC (Concourse Level) · Oral Abstract Sessions

• 10:45a–11:00a Lentiviral Gene Therapy for Alpha Thalassemia (Eva Segura Gensler, PhD)

• 11:15a–11:30a Discovery and development of engineered neutralizing antibody-evading helper-dependent adenovirus capsids for in vivo gene therapy (Marcin Maziarz)

12:15p–1:15p · Dyno Therapeutics: Advancing AI and Delivery Frontiers to Empower Patients with Genetic Agency

TBD · Sponsored Symposia

• 12:15p–1:15p Dyno’s breakthroughs in gene delivery and AI-powered sequence design (Eric Kelsic, CEO/Cofounder, Dyno)

3:30p–5:00p · State of the Field with CBER Leadership

MCEC, Exhibit Hall B1 (Exhibit Level) · Fireside Chats

• 3:30p–5:00p State of the Field with CBER Leadership (Katherine Szarama, PhD (FDA); Megha Kausha, MD (FDA); Co-Chair: Terence Flotte, MD, UMass Chan)

Thursday, May 14, 2026

8:00a–9:45a · A 360 Degree View of Gene Therapy Development

MCEC, Room 258ABC (Level 2) · Scientific Symposia

• 8:00a–8:26a Lessons from DMD: early development to clinic, discovery of the gene to trials (Kevin Flanigan, MD, Nationwide Children’s)

8:00a–9:45a · Transforming Immune Modulation with Gene-Modified Regulatory T-Cells

Westin, Grand Ballroom AB (Concourse Level) · Oral Abstract Sessions

• 8:45a–9:00a Allogeneic murine tissue engineered T regulatory cells improve outcomes in acute lung injury and pulmonary fibrosis (Maegan Hoover, MS)

10:15a–12:00p · Next-Generation Sequencing and Analytics for CGT Manufacturing

MCEC, Room 162AB (Level 1) · Education Symposia

• 10:41a–11:07a Developing quantitative and qualitative sequencing assays for AAV manufacturing (Guangping Gao, PhD, UMass Chan)

• 11:07a–11:33a Integrating sequencing into product release and comparability testing (Timothy Fenn, PhD, Lexeo)

10:15a–12:00p · In Utero Therapeutics: Engineering Fetal Futures with CGT

MCEC, Room 204AB (Level 2) · Scientific Symposia

• 11:33a–11:59a Artificial womb to support fetal development and manipulations (Alan Flake, MD, CHOP)

3:30p–5:00p · Programmable Epigenetic and RNA Editing for Precision Regulation

Westin, Grand Ballroom AB (Concourse Level) · Oral Abstract Sessions

• 3:30p–3:45p Therapeutic epigenome editing for Prader-Willi Syndrome via novel non-viral, non-nanoparticle CRISPR/dCas9 delivery (Xiaona Lu, PhD, MD)

• 4:45p–5:00p Highly compact, AAV-deliverable MiniTET-activator epigenetic editors reactivate frataxin in Friedreich’s Ataxia cells (Kaitlin Pensabene, Epicrispr)

3:30p–5:00p · Armored T-Cells for Solid Tumor Treatment

MCEC, Room 204AB (Level 2) · Oral Abstract Sessions

• 4:15p–4:30p Modulation of T cell signaling pathways to mitigate age-associated CAR T cell dysfunction (Nadine Brueckner, MSc, Tubingen)

Friday, May 15, 2026

8:30a–9:45a · Engaging with ARPA-H: Funding Priorities and Future Directions

Westin, Grand Ballroom CDE (Concourse Level) · Fireside Chats

• 8:30a–9:45a Panel Discussion on ARPA-H funding priorities for CGT (Daria Fedyukina, PhD (ARPA-H); Daniel Gibbs, PhD (Cirsium Biosciences); Co-Chair: Terence Flotte, MD, UMass Chan)

10:15a–12:00p · Exosomes, VLPs, and Nanoagents II

Westin, Commonwealth Ballroom ABC (Concourse Level) · Oral Abstract Sessions

• 10:15a–10:30a Activated T cell-derived extracellular vesicles: a naturally occurring acellular immune gene therapy (Mengying Hu)

• 11:30a–11:45a MSC-derived SIRPalpha-engineered extracellular vesicles enable targeted NLRP3 ASO delivery and mucosal regeneration in experimental colitis (Min Kyoung Jo, PhD)

10:15a–12:00p · Engineering Blood Cells: New Frontiers in In Vivo Selection and Conditioning

MCEC, Room 052AB (Exhibit Level) · Scientific Symposia

• 11:07a–11:33a Adenoviral vectors and in vivo selection: Designing clinical strategies for durable benefit (Andrew C. Dietz, MD, MSCR, Ensoma)

3:30p–5:00p · New Insights into AAV Genome Biology

Westin, Grand Ballroom AB (Concourse Level) · Oral Abstract Sessions

• 3:45p–4:00p Host cell DNA contaminants of recombinant AAV are biased towards genomic sites of wild-type AAV integration (Mark Brimble, PhD, St. Jude)

• 4:15p–4:30p circVec: A powerful circular RNA expression platform that enhances AAV transgene output and enables significant dose reduction (Eoghan O’Leary)

• 4:45p–5:00p MAAP Promotes Full-Capsid Assembly and Potent Transduction of Extracellular Vesicle-Associated Recombinant AAV (Donovan Richart)

The two conflicts I’m still trying to resolve

These are the cross-venue collisions where I genuinely cannot be in two rooms at once. If you’re going to one of them and willing to share notes, please reach out.

Wednesday, 10:45a–11:00a (cross-venue):

• MCEC Room 107ABC: An engineered UGA suppressor tRNA gene enables AAV delivery and disease-agnostic in vivo gene therapy (Mengyao Xu, PhD, UMass Chan)

• Westin Commonwealth Ballroom ABC: Lentiviral Gene Therapy for Alpha Thalassemia (Eva Segura Gensler, PhD)

Friday, 11:07a–11:33a (cross-venue):

• MCEC Room 052AB: Adenoviral vectors and in vivo selection: Designing clinical strategies for durable benefit (Andrew C. Dietz, MD, MSCR, Ensoma)

• Westin Commonwealth Ballroom ABC: MSC-derived SIRPalpha-engineered extracellular vesicles enable targeted NLRP3 ASO delivery and mucosal regeneration in experimental colitis (Min Kyoung Jo, PhD)

One last ask

If this lineup is useful to you, the best thing you can do is forward it to one colleague who’s planning to spend all week in their home track. Pull them into a session that has nothing to do with their day job. The hallway conversations afterward are where the real work happens.

See you in Boston.

Ben

A gene therapy company in San Francisco raised ~$4.5M through crowdfunding.
It had never been done before (to that extent)
Sure, they had cool data. But they had a (not so) secret weapon:
Their founder and CEO, Nicole Paulk, decided to show up - on podcasts, on LinkedIn, on Twitter/X, in public conversations - and talk about her science with genuine enthusiasm, in real time. Siren Biotechnology is now one of the most recognized early-stage gene therapy companies in the space.
The communications strategy was a core component of the fundraising strategy.

Most biotech C-suite executives are watching that, and still not moving.

The Problem

The traditional biotech space has built an elaborate apparatus for saying nothing quickly.
It’s starting to cost us.
Marketing and communications teams run 5-step approval processes for a LinkedIn post.
CEOs, CSOs, and CMOs sit on perspectives for weeks, waiting for clearance that never quite comes. The default posture across the C-suite is: when we are ready, we will communicate. The public can wait.

Here’s the issue: the internet does not wait.

We are operating in an attention economy where timeliness is currency, and biotech’s leadership class is spending that currency on internal approval chains.
By the time a company puts out a considered, carefully worded statement on a development that happened 3 weeks ago, the conversation has moved on.
The audiences that matter - patients, investors, potential collaborators, future hires - have already formed their views from whoever got there first.

There’s a reason for this. It’s called the OODA loop.

The OODA loop

In the 1950s, military strategist John Boyd developed a framework to explain why some fighter pilots won dogfights and others didn't. He called it the OODA loop - Observe, Orient, Decide, Act. His conclusion was simple: the pilot who cycles through that loop faster wins.
Speed of processing, not accuracy of processing, is the decisive variable.
But then it gets worse:
A faster “cyclist” does not win once. They win every subsequent cycle too, because their actions are always closer to the present moment, always harder to counter, always more relevant to what is happening right now.
The slower pilot is perpetually responding to a reality that has already changed.

Now lets apply it to science communication.
A meaningful development happens in biotech: a trial result, a regulatory decision, or a new paper.
There is a finite window where commentary is valuable and visible. The individual who observes the development, orients through their years of expertise, decides what they think, and acts by posting, recording, or speaking inside that window, captures the attention.

The executive who runs the same process through a comms approval chain arrives after the window has closed.

Faster voices win.
They shape the public and industry perception.
And that advantage compounds over time.

Sadly, conventional biotech is built to be the slower pilot by design.

The Cautious Industry

The standard defence of slow science communication is patient sensitivity and regulatory risk. Sure. I get it. And in a regulated environment, they deserve genuine respect. There are things that should not be said publicly about an IND-stage asset, and good reasons why.

But fear is the actual mechanism driving the silence.
Fear of getting scooped (somehow). Fear of being wrong in public.
Fear of the professional humiliation that was baked into scientific training from the first lab meeting, where an imprecise claim got dismantled in front of the room. Scientists are trained to be right before they speak, and that instinct does not disappear when you become a CEO.
The approval chain is the organizational expression of that same fear.

What’s Wrong About the “Fear”

The executives and comms teams running approval processes are optimizing to avoid saying the wrong thing.
They are not accounting for is the cost of saying nothing.

In a noisy world, silence is not neutral.
Silence means someone else fills the space.
It means your narrative gets written by people with less context and less stake in getting it right.

The tech mindset understands this intuitively. The risk of a slightly imperfect post is orders of magnitude smaller than the risk of being invisible.
No one is going to steal your platform strategy from a LinkedIn post or tweet. No one is going to fly off the handle because your CSO shared a perspective on a new paper. The catastrophe that the approval process is protecting against is almost entirely theoretical.

Nicole Paulk built Siren’s profile without abandoning scientific rigor.
She adopted a communication cadence that matches the speed of the world she is operating in. Podcasts. LinkedIn. X. Public enthusiasm about her science, expressed consistently and in real time.
The result is a fundraising base that most early-stage gene therapy companies cannot access because no one knows who they are.

What This Means

The attention economy rewards speed.
You need to embrace it.
- Post on LinkedIn or X before the conversation moves on.
- Go on that podcast.
- React to the trial result the day it drops.

Keep the basic guardrails in place - your board relationship matters, and abide by the regulatory constraints.
But within that space, the room to move is far larger than the current default suggests.

The OODA loop rewards the individual who acts while others are still orienting. Your ten or twenty years of domain expertise is the most credible voice in the room. The tech founder with six months in the industry is not a better communicator because they know more. They are a better communicator because they move faster.
That advantage can be yours, it you’re willing to move faster.

The Takeaway

The OODA loop teaches that the winner of the dogfight is the pilot who reacts faster.
Biotech’s C-suite has some of the best pilots there are. The question is whether they are willing to fly at the speed the attention economy demands.

No one remembers the perfectly generic post on a 3 week old topic.
Everyone remembers the founder who made a personal emotional mark on them - who had the good takes, showed up consistently, and was willing to say something before the committee signed off.

The OODA loop is available to you right now.

Don’t let your own approval chain be someone else’s advantage.

Pfizer’s Beqvez was FDA-approved in April 2024. By February 2025, Pfizer had pulled it. Zero patients treated post-approval. Zero.

Hemgenix - priced at $3.5 million per dose, with genuinely strong clinical data behind it - had reached roughly a dozen patients by mid-2024. BioMarin’s Roctavian pulled in $3.5 million in 2023 revenues against an internal target of $50-150 million.

The industry’s read on all of this has been remarkably consistent.
Reimbursement challenges. Payer hesitancy. Patient access gaps.

But all these things are downstream of the actual problem.

Gene therapy is being held back by a tech stack built from the best tools available at the time - tools that have now been superseded, and that the field has been too slow to replace. The commercial graveyard we’ve seen is the predictable outcome of running a 2025 industry on a 1990s platform.

I’ve spent years working inside this stack… both in manufacturing and analytics.

New platforms to address these issues are now available. We need to pivot towards them - with urgency.

The Problem with the Stack

The gene therapy field built its foundation on the best tools available at the time.

One on delivery: AAV capsids selected from nature and minimally engineered, because the computational tools to design better ones didn’t exist.

One on manufacturing: HEK 293 cells as the production workhorse, because scalable alternatives hadn’t been developed.

One on analytics: characterization methods built around physical separation, with no ability to chemically interrogate genomic contents, because the technology to do more wasn’t there yet.

Those were reasonable choices given what existed. The problem is that better options have emerged over the past five years, and the field has largely continued operating as though they haven’t.

The tragedy is that we’ve had greatly promising clinical efficacy. The data on hemophilia B, on certain muscle diseases, on several ocular indications, is genuinely compelling.
Patients who received these therapies in trials saw real benefit.
The science worked.

The stack couldn’t deliver it at a price anyone could pay.

This article is intended to overview how that must be changed.

1. Delivery: AI has given us better capsids.

The dominant capsids in approved gene therapies were selected from nature because, until recently, we lacked the tools to do better. That constraint no longer exists.

Take muscle gene therapy. Standard programs dose at around 1.33e14 vg/kg. At that dose, you’re pushing an enormous systemic capsid burden into a patient. A substantial fraction lands in the liver - off-target, adding to immune burden, and at high enough doses triggering responses that have killed patients. The DMD field has lived with this reality for several years now, and it has shaped clinical hesitancy in ways that go well beyond the specific programs involved.

We absorbed these dose requirements as permanent constraints of the field because, until recently, there was no viable path around them.

That is no longer the case.

Take, for example, Dyno Therapeutics on their Dyno-bn8 capsid. Muscle delivery at 5.2e12 vg/kg via IV injection - a 25-fold dose reduction against the current standard. The capsid was built by training AI models on NHP performance data across millions of variants. It comes with engineered liver detargeting, directly addressing the toxicity mechanism that has caused so much damage to field confidence.

What makes this particularly pointed… Dyno-bn8 is fully compatible with existing AAV9-based manufacturing processes. The equipment transition is minimal. The business model transition is the hard part - because a 25-fold dose reduction means 25 times less vector sold per patient. I’ll let you do that math on what it implies for companies whose revenue is built on manufacturing volume at current dose levels.

A 25-fold dose reduction ripples across everything. Cost per patient, safety profile, commercial viability of indications that have been shelved as economically unworkable. The field has the tool. The field is watching it from a distance.

2. Manufacturing: It’s Time to Dump HEK293.

HEK 293 cells became the default AAV production platform because they were available, well-characterised, and capable of making virus. For a long time, there was no credible alternative at scale.

We need to re-evaluate the math.

HEK 293 production requires full GMP cleanrooms, expensive transfection reagents, expensive bioreactors, expensive media. Every one of those inputs carries its own compliance burden under GMP. The cost-of-goods floor is structurally high before a single dose leaves the building.

And because you’re producing AAV inside a human cell environment, the product co-purifies with human host cell proteins and host cell DNA. These are cells under stress - being forced to produce virus against their own biology. They respond by generating the full molecular signature of cellular stress and immune activation. A proportion of that ends up in the final product. A patient receiving high-dose AAV already carries a substantial immune burden from the capsid load itself. The process-related contaminants pile on top.

It’s time to dump this system. Something better (IMHO) is available.

AAV production in plants.

Plant-based manufacturing removes that contamination context at the source. One such company, Cirsium Biosciences* produces AAV in whole tobacco plants. No GMP cleanrooms upstream. No expensive transfection reagents or media. Plants grow in nutrient water in a vertical farm environment. Host cell contaminants are plant-derived - biologically inert from a human immune response standpoint. The immune provocation from process-related impurities drops substantially.

The cost differential sits a full magnitude (and then some) lower than standard HEK 293 production. Put that alongside the dose reduction from AI-engineered capsids… and the cost-of-goods math for gene therapy starts to look like a different industry.

And yet I still run across AAV developers who have never heard of this approach.

There’s a geopolitical point here as well.
China will always win a cost competition on legacy manufacturing platforms.
Labor arbitrage, state-subsidised infrastructure, scaled CDMO capacity - that race goes one way over time.
The path to North American competitiveness runs through a platform switch. Radical cost reduction driven by technology superiority. That seems to me as the answer to China’s manufacturing scale - is make the platform they’ve scaled into obsolete.

*(Full disclosure: I work with Cirsium Biosciences. I was a fan of their approach before they started paying me, and I’ll be a fan looong after they stop. They also have no idea that I’m writing this article. But I just want to let you know that I may have a conflict of interest here)

3. Analytics: Next-generation characterisation tools

Analytical ultracentrifugation became the gold standard for AAV capsid characterisation because it was the most capable tool available. AUC separates empty, partial, and full capsids based on sedimentation differences - and for thirty years that ceiling defined what adequate characterisation looked like. The ceiling has moved.

AUC gives you a weight-based population distribution. It tells you roughly what proportion of capsids are empty, partial, or full. Chemical interrogation of the genomic contents of those fractions is entirely outside its capability.

This matters because partials are not a uniform population. A partial capsid can carry a truncated transgene that contributes nothing to potency but adds directly to capsid dose and immune burden. Another partial carries a near-full genome that is functionally therapeutic. Another carries truncated sequence that actively drives immune activation. AUC counts all of them as partials and moves on.

Conventional assays compound the problem. qPCR and ddPCR assess genomic regions of roughly 100-150 nucleotides. A preparation with significant truncation mid-transgene looks clean on a short-amplicon assay. Functional genome titer gets systematically overestimated. Programs go into the clinic dosing patients against a titer number that overstates therapeutic reality.

Several years ago, I worked with NanoMosaic. I’ve seen firsthand what next-generation analytics reveals about product that looked clean on legacy methods. Recent NHP data from their Tessie nanoneedle platform makes the gap concrete: apparent “full” species identified by AUC turned out to contain long partial genomes and multimeric capsids bearing partial genomes, species AUC cannot resolve. The Tessie platform quantifies full-length transgenes over 4kb directly, maps truncation hotspots region by region, and does so from crude extracts.

We owe patients the confidence that we’re putting the best possible product possible inside them. AUC can’t do it. Next-gen methods like the Tessie platform can.

The field needs to integrate next-generation analytics across the full manufacturing and release workflow. Process optimisation without chemical interrogation of capsid contents is informed guesswork. Right now, we are shipping product we haven’t fully characterised.

Patients deserve better.

What This Means

These three layers lock together. Legacy capsids require high doses. High doses require high manufacturing volumes. HEK 293 production of those volumes is expensive and generates immunogenically complex product. Legacy analytics mean that product is less well-characterised than the batch release data suggests. The cumulative cost reaches the patient at a price payers won’t cover and a safety profile that keeps clinicians cautious.

The commercial failures of the past three years are carrying a consistent signal. The stack can’t get gene therapy to the patients who need it at a price the system can absorb.

AI-engineered capsids, plant-based manufacturing, and next-generation analytics each address a layer of that problem directly. Stacked together, they shift the economics of gene therapy from a platform that serves hundreds of patients at extraordinary cost… to one that could realistically reach the millions of patients waiting for it.

To the scientists reading this: I understand the inertia. The platforms you trained on represent years of hard-won expertise, and the sunk cost is tough. Your patients need the therapy to work, to be safe, and to be accessible. The question worth sitting with is whether the platform you’re defending can actually get them there.

To the investors: the programs exciting you are often built on infrastructure that structurally prevents them from reaching commercial scale. Before the next term sheet, ask whether the tech stack underlying the program can get from a hundred patients to a hundred thousand. That question has a specific answer. It’s worth knowing what it is.

The Takeaway

The industry has generated a lot of thoughtful work on outcomes-based payment models, payer education, and reimbursement innovation. I’m by no means discounting the good work on that side of the equation.

But the industry needs a platform that can be commercially viable. Building that platform is the work.

The CDMOs and large pharma players with billions in legacy infrastructure will make the case for incremental improvement. That argument protects their balance sheets.

It leaves tomorrow’s patients behind.

The technologies that can significantly change the economics of gene therapy exist right now.

How much longer can we afford to wait?

COVID exposed BioPharma’s deep discomfort with being questioned.

The message from the industry during those years was functionally “trust us, shut up, and comply.” Dissenting voices, even credentialed ones, got labeled. Concerns got dismissed.
(Calm down, I know the government played a MASSIVE role here too, but let’s not throw the baby out with the bathwater)

The result, for an industry that was genuinely performing one of the most remarkable scientific feats in human history, was a collapse in public trust.

We are still paying for that.
Vaccine hesitancy is at a generational high.

I got vaccinated. I got double boosted. I’m not writing this as a critique of the science. I’m writing it as a critique of what the industry did with the science - and what it continues to do, or rather, refuses to do.

The Problem

A lot of conversations inside the industry right now focus on capital.
Where is the funding?
Why are VCs pulling back?
Why can’t we get the liquidity providers to see the value of what we’re building when the timelines are long and the risk is real?

That’s a downstream problem. The real issue is that we have spent decades living inside our own bubble, talking to ourselves, and assuming the rest of the world either doesn’t need to understand what we do, or isn’t capable of it. We have decided, largely without saying it out loud, that public engagement is someone else’s job - or worse, that it’s beneath us.

Other regions haven’t made that mistake. China is making biotech a national priority, with the public narrative to match. While we debate whether science communication is “worth the time,” entire countries are building public appetite for biotech innovation at scale. That appetite translates into policy support, into talent pipelines, into capital flows.
We are not competing on science alone.
We are competing on narrative, and right now we are losing.

Let’s Break It Down

We cannot afford to stay isolated in the smug self-assurance of our science.
The path out of the ivory tower runs through four practical commitments.

1. Build narratives that reach people

The industry’s current approach to public communication is a nice ad at the Super Bowl and a press release when something gets approved.
A genuine narrative strategy it is not.

What actually works is what the best science communicators on YouTube have already proven: when you give people a genuine frame for understanding what’s happening in a field, they get excited. The curiosity is there.
The appetite is there. BioPharma just isn’t feeding it.

Large pharma companies have the resources to embed themselves into mainstream culture in ways that go far beyond advertising: TV shows, long-form documentary content, social campaigns built around real science and real patients. The technology to do this affordably has never been more accessible. The will to do it remains almost entirely absent.

Build the vision for the future the mainstream can get excited for.

2. Earn trust by inviting criticism

The opioid crisis happened. Thousands of families carry the consequences of that. Pharma has committed great sins in the past. Any strategy to rebuild public trust that doesn’t start with genuine humility about that history is not a strategy.

The industry’s instinct is still to manage perception rather than invite dialogue. That instinct is exactly what cost us during COVID and the opioid crisis - it will cost us again.
The public has no need for BioPharma to be perfect…
But they do need it to be honest.
There is a meaningful difference between those two things, and we have consistently confused them.

Executives - gone are the days when being the bland CEO with nothing to say was the safest option. We need dynamic and charismatic personalities, who are willing to sit in uncomfortable conversations, acknowledge real failures, and explain trade-offs plainly
Those of you who do will build durable credibility.
Opting to stay quiet carries its own risk: invisibility, irrelevance, and an audience that fills the silence with its own assumptions.

3. Open the doors to other industries

Cross-industry collaboration is one of the most underleveraged assets in this space - and one of the best tools for making biotech legible to the public.

One of my clients, Cirsium Biosciences, is using whole tobacco plants to manufacture AAV gene therapies at a fraction of the conventional cost. They are drawing directly on the operational and agronomic knowledge embedded in a $5 trillion food-agriculture industry and redirecting it toward advanced therapy manufacturing. The science is serious. The cost implications are real.
And the story - tobacco plants making gene therapies - is genuinely interesting to people outside the field.

Scientists, start with AI as a forcing function: pick one completely unrelated industry and spend time understanding how it operates. The lessons that transfer to what you’re working on may surprise you.

4. Give the capital chain something to see

The VCs understand the science. Their liquidity providers often don’t.
That gap is a structural problem for how capital flows into this space.

Instead of better pitch decks, the answer is a larger public. When biotech innovation is embedded in mainstream conversation - when people hear about it on podcasts, see it dramatized in shows, encounter it in contexts that aren’t investor memos - the subliminal case for the industry builds over time.

The liquidity providers who sit at the top of the funding chain are not immune to cultural narrative. They are, in fact, particularly susceptible to it. An industry that the public finds exciting is an industry that attracts capital from people who will never read a CMC filing.

Tangible Next Steps (IMHO)

For scientists: use AI to go deep on one industry that has nothing to do with yours. Find what they know that you don’t. The cross-pollination potential is enormous, and the tool to do it has never been more accessible. And then post about what you found.

For executives: your silence is no longer neutral. Every platform you decline to engage, every bold position you soften into a press release, every uncomfortable question you deflect; these are active choices with active costs. The industry needs visible leaders with something real (and interesting) to say. Allocate budget for this.

For VCs: the public audience you need doesn’t exist yet at the scale required. Building it isn’t someone else’s problem. Consider what you can fund, platform, or amplify that expands the mainstream conversation around biotech.
It needn’t be only for altruistic reasons; your liquidity providers are part of that mainstream.

The Takeaway

The science has never been more powerful.
The therapies coming through the pipeline represent genuine miracles: single-dose cures, precision oncology, gene editing that can rewrite inherited disease. The world would find this extraordinary if we gave it the chance.

We can’t afford to stay in our ivory towers or our spotless labs, then wonder why no one is paying attention.

BioPharma stopped talking to the world on their terms.
So the world stopped listening.
Let’s change that.

A 17-year-old with no university training is building a gene therapy designed to force cancer cells to kill themselves. His early datasets show roughly 73% cancer cell death in 20 hours - about 2.7x more elimination than the closest comparable therapeutic control. He skipped the credential pipeline entirely and went straight to the work.

Meanwhile, the billionaire co-founder of GitLab was told by his oncologists that he had exhausted the standard of care. So he assembled a SWAT team of data strategists, geneticists, and AI systems, built a 1,000-page research document tracking every diagnostic and therapeutic option, and is currently showing no evidence of disease.

The tech bros are coming for BioPharma. And we should open the door.

The Problem: A Gate With No Gatekeeper

For decades, our industry has operated on a foundational assumption: that scientific knowledge is the barrier to entry. That you earn the right to work on these problems by spending 12 years accumulating credentials, mastering the literature through proximity, and learning through the slow accumulation of bench time and institutional memory.

That assumption is no longer defensible.

The scientific knowledge that once gate-kept our industry has been commoditized by AI. When asked the right questions, AI systems can synthesize the key research across a specific area faster and more comprehensively than any individual could through years of literature review. The PhD who spent 80% of their time doing finicky experiments - getting arthritis from pipetting - is no longer the differentiator they once were.

The next decade of this industry requires something different from what the credential pipeline was designed to produce.

Let’s Break It Down

1. The knowledge gap is closing faster than the industry admits

Sid Sijbrandij is the co-founder of GitLab. He is not a biologist. When his osteosarcoma returned after exhausting standard care, he did not defer to specialists and wait. He built infrastructure. A data-driven research operation, maximal diagnostics, a therapeutic ladder of experimental options developed from first principles. He used AI to model his cancer as a dynamic, adaptive system and to track every interaction, every scan, every genomic result. The result - for now - is no evidence of disease.

He also noted something worth sitting with: it costs around $1M to dose a single person with a personalized therapy, against $1B to get a drug approved through traditional channels. That gap is growing - because it keeps getting easier to make new medicines while the cost of the Phase 3 keeps going up.

Dylan Paoletti, 17, co-founder of GenCTX, is on the other end of the wealth spectrum - but operating at the same intellectual level. He has skipped university entirely, armed himself with AI, and is using it to understand mechanisms, design genetic circuits, and iterate on a gene therapy for lung cancer.

Pay attention to both of them.

2. The credential model was never a perfect filter

The counter-argument from our industry is familiar: you need years immersed in a field to develop true wisdom. To understand not just the literature but the texture of the problems - the failures that never got published, the institutional context, the clinical nuance.

There is something to this. It is not nothing.

But let’s be honest about what the credential model also produces: scientists in debt who have never run a business, who have to find an experienced entrepreneur to hand them business basics, who have spent a decade optimizing for peer review rather than patient outcomes. The cookie-cutter pipeline - PhD, post-doc, junior scientist, senior scientist - filters for persistence and conformity as much as it filters for talent.

3. North America has a competitiveness problem it is not being honest about

China and other parts of the world are pulling ahead in biotech. The funding models that have sustained North American dominance in advanced therapies are under pressure. And yet our instinct, when someone like Dylan or Sid shows up, is to question their credentials rather than ask what they can see that we cannot.

We cannot afford that instinct. The urgency of the problems - rare diseases, cancers with no standard of care, therapies that cost more than a house to administer - demands more perspectives, not fewer. It demands the vigour of youth and the analytical frameworks of adjacent fields applied at full force to problems that have been stuck for decades.

4. The industry that closes its doors will be built around

The advanced therapy space will be transformed by people who do not look like the people who built it. Credentialing gatekeeping, funding structures that reward established networks, and cultural norms that treat outsiders as dilettantes will not stop that. They will just ensure that the transformation happens without us - that the Dylans and the Sids build their pipelines, their platforms, and their companies in the gaps we leave open, rather than alongside the expertise we’ve spent careers developing.

What This Means

For scientists and executives in this space, the implication is clear. We need to actively look for the Dylans and the Sids - the people who are working the problem from a different direction - and figuring out how to integrate their perspective rather than filtering it out.

It means funding structures that do not require a 12-year credential trail to be taken seriously. It means mentorship that runs in both directions - where the scientist teaches the founder the CMC realities, and the founder teaches the scientist how to move fast and take calculated risk.

It means recognizing that the boxes we operate in - regulatory, manufacturing, clinical - were drawn before these modalities existed. The people most likely to redraw those boxes are not necessarily the ones who spent the longest time inside them.

Conclusion

The scientific knowledge that once defined who belonged in this industry has been democratized.

The most interesting thing to happen to biotech in a generation is already underway.

The tech bros are coming for BioPharma.

The only question worth asking is…

Are we smart enough to welcome them with open arms?