HeyJaredScaling Biotech
Exploring how big pharma partners with AI startups to build internal capabilities, data and models
- Indexed issues, last 90 days
- 11
- Latest publication
- Sep 30, 2026
- Audience
- Checking…
- Earliest in this view
- Jul 22, 2026
Latest issues
Automation adoption wasn’t defined by automation (opens the original)
Read excerpt
In the last few posts I've been arguing that newer digital tools don't fit as cleanly into existing drug discovery processes as past waves of technology, and that the most effective ways to adopt these new tools will require fundamentally changing the scientific processes to create the places where the tech can slot in. A similar phenomenon seems to have happened for lab automation, and while this is more about hardware than data and algorithms, I think it’s illustrative. So this week I want to
Evolving science and tech in parallel (opens the original)
Read excerpt
One thing I’ve been thinking a lot about lately, as I explore how the relationship between newer digital models and drug discovery teams has changed compared to previous waves of technology, is the increasing need to deliberately coordinate the evolution of the tools and the processes that use them. I wrote a lot about this a few years ago, with a framework I called the Reciprocal Development Principles, before moving on to other topics. But now I feel drawn back to it, particularly as it has be
Who writes the playbook for new technology? (opens the original)
Read excerpt
One of the things that makes the most recent wave of digital models for biopharma different is that they’re increasingly coming into drug discovery teams via external companies, as opposed to previous waves that were mostly developed internally before being turned into commercial products. This creates different dynamics for how teams roll out these tools. In this post I want to explore what this looks like and what it means for how new digital tools/models/systems are integrated into the drug d
Between internal and external technology (opens the original)
Read excerpt
Last week, I asked the question whether it’s possible for biopharma to adopt new digital technology that’s introduced from the outside, as opposed to the more common historical pattern of internal teams developing their own tools. I failed to mention an intermediate model that’s also important: the techbio startup that is formed around a new technology, with the goal of developing its own drug programs. This week, I want to explore what this looks like from the perspective of technology adoption
Does AI in biotech have to come from internal teams? (opens the original)
Read excerpt
To start answering the question I raised last week about what factors are slowing down tech adoption in biotech, I started looking at the history of how older technologies have been adopted by the industry. One clear pattern is that many of the tools that are standard today were developed by the same scientists who were planning to use them. This development model makes sense, but it’s also limiting. Is it the only way biotech innovation is possible?I won’t be able to answer that question this w
Publishing over time
Last 90 days. Choose a month to open its work.
Recurring subjects
Named in the text we hold. One piece can cover several.
Audience
No verified audience measurement yet.
About this data
Counts cover the work we have indexed. Tone needs enough text and a confident classification. Excerpts and episode notes are not full articles or transcripts.
Identity or attribution wrong? Suggest a correction.