Why your best scientist makes your worst content

If you run marketing for a biotech and you have ever waited three weeks for a founder's "quick post," then quietly rewritten all of it, this post is for you. It explains why scientific training works against good content, and gives you a system for extracting what is in your scientist's head without making them the writer.

Why scientific training produces unreadable content

A scientist is trained, over a decade or more, to communicate in a way that is almost perfectly wrong for social content. This is worth stating plainly, because most marketing teams treat the problem as fixable with feedback. It is not. You are working against years of professional conditioning.

Four trained instincts do the damage.

Precision over clarity

A scientist will not write "this slows tumor growth" when the honest, defensible statement is "we observed a statistically significant reduction in tumor volume in a subcutaneous xenograft model at the 30 mg/kg dose." The second sentence is more correct. It is also content that no one outside the lab will read to the end.

Hedging as a reflex

Peer review punishes overstatement, so scientists learn to qualify everything. "May suggest," "could potentially indicate," "warrants further investigation." A post built from hedges has no point of view, and a post with no point of view does not travel on LinkedIn.

Completeness over selection

Scientific writing rewards accounting for every caveat and prior result. Good content does the opposite: it cuts everything that is not the single thing the reader should remember. Asking a scientist to leave out a true, relevant detail feels to them like dishonesty.

Jargon as a marker of competence

Among peers, technical vocabulary signals that you belong. The instinct to use it is deep and mostly invisible to the person doing it. This is the curse of knowledge, described by Steven Pinker in The Sense of Style: once you know something well, you cannot easily imagine what it is like not to know it, so you write for people who already understand.

None of this is unique to your company. A 2017 study in eLife found that the readability of scientific abstracts has declined steadily since the 1960s, driven in part by a rising density of specialist jargon. The people writing for other scientists have been getting harder to read for sixty years. Now you are asking one of them to write for a non-specialist audience on a platform built for skimming.

The mistake most teams make is responding to this with edits and encouragement. The fix is structural. You change what you ask the scientist to do.What partnership conversations actually require before the data arrives

Business development at large pharma and mid-sized biotech works on long cycles. The people evaluating your asset in 2027 have been building their watchlist since 2024. They are not browsing the scientific literature looking for surprises. They are tracking companies and programs they've already identified as interesting. You need to be interesting to them before you have anything to sell.

Pharma companies with top-quartile sourcing networks identify licensing targets 6 to 8 months earlier than their competitors, according to Vision Lifesciences' 2026 Biotech Licensing Deal Tracker. That gap compounds: by the time a mid-tier pharma company becomes aware of your program, the strategic players may have already completed initial due diligence and formed a view.

Once formal discussions begin, deals themselves take anywhere from 6 weeks to 12 months to close depending on complexity, according to Novartis's published BD process guidance. The implication is clear: the window for building awareness needs to open well before you think you are ready for partnership conversations.

The system: extract expertise, don't assign writing

The core move is to stop treating your scientist as an author and start treating them as a source. Authors produce drafts. Sources produce raw material. Your scientist is the best source you have and one of the worst authors, so use them for what they are good at.

Here is the four-part system high-output teams use.

Element 1 — Interview, don't assign

Never send a scientist a blank document and a deadline. You will get either nothing for three weeks or a 600-word abstract that needs to be torn down to the studs.

Instead, book twenty minutes and ask questions. "Why does this matter to a patient?" "What would a competitor get wrong about this?" "If you had to explain this to your smartest friend who isn't in the field, what would you say?" Record it. People speak in plain language even when they cannot write it, and the spoken version is almost always the post. Your job is to capture the moment the scientist forgets they are being precise and just explains the thing.

The interview also solves the deadline problem. Getting twenty minutes on a calendar is a far smaller ask than getting a written draft, and it moves the writing burden onto the person who is actually good at writing.

Element 2 — Separate accuracy review from drafting

The single most useful change you can make is to split one job into two: writing the post, and confirming the post is true. These are different tasks for different people at different times.

The marketer writes. The scientist reviews for accuracy only. When you hand a scientist a finished draft and ask "is this right?", you give them a job they are excellent at: catching the one claim that overstates the data. When you hand them a blank page and ask "can you write something?", you give them a job they are bad at and that they dread.

This reframing changes the emotional register of the whole process. The scientist is no longer being asked to do marketing. They are being asked to protect the science, which is exactly what they want to do.

Element 3 — Translate the "so what," not the "what"

The scientist owns the "what": the mechanism, the result, the method. You own the "so what": why anyone who is not in the lab should care. Most failed biotech content is all what and no so what.

A finding is not content. "Why this finding changes the treatment conversation for a specific patient population" is content. The scientist will rarely volunteer the so what, because in their world the what speaks for itself. You have to ask for it directly, and you have to be willing to push: "I understand what you found. Tell me why it matters to someone who has the disease, or to a partner deciding where to put their money."

Element 4 — Give them a redline lane, not a blank page

When the scientist does need to touch the document, give them a constrained role. A marked-up draft where their only job is to strike inaccurate claims and flag missing caveats is a task they can complete in ten minutes. A blank page is a task that expands to fill three weeks.

Define the lane explicitly: you may correct facts, you may flag anything that creates regulatory exposure, you may not rewrite for tone. Tone is not their job. The moment a scientist starts rewriting for tone, the hedges and the jargon come back, and you are back where you started.

How do you get a busy scientist to contribute to content without slowing everything down?

Replace the written draft request with a recorded twenty-minute interview, then have the scientist review only for factual accuracy rather than write from scratch. This splits the work along each person's strength: the scientist supplies expertise and catches errors, the marketer handles structure and tone. It typically cuts a multi-week drafting cycle down to a few days.

Where this breaks down

This system fails in predictable ways, and the failures are worth naming so you can avoid them.

The scientist becomes the bottleneck anyway

If accuracy review has no deadline, it becomes the new three-week wait. The review step needs a defined window, the same way a publishing rhythm needs one. A draft sent for accuracy review on Monday should have a "respond by Wednesday" attached to it, with a default of "no objection" if the window passes on low-risk evergreen posts.

The marketer oversimplifies and the science breaks

Translation has a failure mode in the other direction: cutting so aggressively that a claim becomes wrong. This is why the accuracy review is not optional. The goal is the simplest version that is still true, not the simplest version. A scientist who has been burned by an oversimplified post once will resist the whole system, so getting this right early matters more than speed.

Leadership treats the founder's voice as sacred

In founder-led biotechs, there is often pressure to publish the CEO's words verbatim because they are the CEO's words. This is how you end up with a feed full of unreadable posts that no one will tell the founder are unreadable. The system only works if the founder accepts the role of source, not author, for their own LinkedIn presence. That is a conversation worth having directly, early, and once.

What this looks like in practice

Consider a composite: a Series A immunology company with one marketer and a scientist-founder who insists on being involved in every post. For six months the founder has been promising to write about the company's mechanism of action. Nothing has shipped, because every draft he starts dies in his drafts folder.

The marketer switches approach. She books twenty-five minutes, asks the founder to explain the mechanism as if to a sharp generalist investor, and records it. In that conversation the founder says, offhand, "the reason this matters is that existing therapies hit the symptom and we hit the thing causing the symptom." That sentence, which he would never have written, becomes the spine of the post.

She drafts it in an afternoon. She sends it to the founder with one instruction: mark anything that is factually wrong or creates regulatory risk, do not touch the wording otherwise. He flags one overstated claim and approves the rest in fifteen minutes. The post ships two days after the interview, in a cycle that used to take a month and usually produced nothing.

Nothing about the founder changed. What changed was the job he was asked to do.

For teams trying to make this rhythm repeatable across many posts, what a high-performing biotech content calendar actually looks like covers how to build the review windows and approval roles this system depends on. And for pre-commercial companies still deciding whether any of this is worth it yet, why pre-commercial biotech companies build their commercial presence 18 months too late makes the case for starting before you think you need to.

The instinct to fix bad scientific content by coaching the scientist to write better is the wrong instinct. You are not going to out-train a decade of professional conditioning with a feedback comment. The teams that consistently turn deep scientific expertise into content people actually read are not the ones with the most articulate scientists. They are the ones that stopped asking scientists to be writers and started using them as the irreplaceable sources they are.

This is the structure tools like Pyroplane are built around: a clear split between drafting and review, defined accuracy approvals with deadlines, and a place to turn a recorded conversation or a flagged publication into a draft without handing a scientist a blank page. The system matters more than the tool, but the tool exists because this is the part that breaks for almost every life sciences team.

So the question to take into your next content meeting is not "how do we get our scientists to write better?" It is "what are we still asking them to write that we should be interviewing them about instead?"

Sources

• Pontus Plavén-Sigray et al., "The readability of scientific texts is decreasing over time," eLife, 2017 — elifesciences.org/articles/27725

• Steven Pinker, The Sense of Style: The Thinking Person's Guide to Writing in the 21st Century, 2014 — stevenpinker.com