Dear reader,

Since last month’s announcement about the change in business structure, this is our first proper update on what has been happening.

One major project is now underway using national AI super-computing infrastructure. Another brings these questions into an international research forum in April. And a foundational research strand has recently concluded.

Across all of them sits the same concern:

As AI becomes embedded in biological design, how do we ensure what it generates is stable, accountable, and trustworthy?

Here is where things stand.

Stress-Testing AI-Designed Proteins for Biodesign

Our latest hands-on AI-biodesign project has been accepted through the UK’s AI Research Resource Gateway route and is now well underway.

I am serving as Principal Investigator on this project.

The work is supported through access to national AIRR supercomputing infrastructure, the Isambard-AI. The focus is AI-generated proteins relevant to biodesign and biofabrication.

Rather than optimising existing components, we are examining how newly generated biological interfaces behave under controlled variation. At this stage, the emphasis is not on performance improvement, but on structural stability.

As generative tools become embedded in design workflows, the question shifts from possibility to reliability. Not only what AI can generate, but how robust those outputs are when conditions change.

More details to follow.

CHI 2026 Panel

Biodesign × AI: Interactions in the Algorithmic Wet Lab

In April in Barcelona, I will be moderating a panel at CHI 2026 exploring what we call the algorithmic wet lab.

The panel brings together:

• Orkan Telhan

• Iohanna Nicenboim

• Margherita Pevere

• Carolina Ramirez-Figueroa

AI systems are no longer only analysing biological data. They are shaping experimental direction, material design, and optimisation workflows.

That raises structural questions:

Who designs when AI participates in laboratory decisions? Where does responsibility sit when outcomes emerge from human–algorithm–organism systems? What does accountability look like when interpretation is distributed?

As moderator, my role is to surface tensions between technical capability, design responsibility, and institutional governance.

More information about the session will follow shortly.

Rethinking Trust at the Molecular Scale

The ARIA-supported Trusted Molecular Memory project has now concluded.

The work formed part of Advanced Research and Invention Agency’s Trust Everything, Everywhere discovery programme, examining how trust infrastructures must evolve as computation intersects with physical and biological systems.

The project was led by Larissa Pschetz at the University of Edinburgh. Through Biodesign Academy, I contributed on the analytical side, focusing on how verification must be reframed when AI-driven automation meets material processes.

Rather than proposing a new technical mechanism, the work concentrated on clarifying how trust is structured in hybrid bio-digital systems.

Further details will be shared once the publication is publicly available.

Independent Technical Diligence

Across these strands of work, a consistent theme emerges.

As AI enters biological design, claims accelerate. Stability, feasibility, and governance do not automatically follow.

Alongside research, I am conducting independent technical diligence for EU and UK investors and institutions evaluating AI-driven biology ventures.

The focus is straightforward: separating biological feasibility from narrative-driven AI claims.

If you are assessing AI–biology companies or institutional AI integration in this space, feel free to get in touch.

AI in biology is moving quickly. What matters now is not only capability, but clarity about how these systems hold up under scrutiny. The projects above are different expressions of the same effort: building reliability into AI-driven biodesign.

Until next time,

Raphael

Founder, Biodesign Academy

How AI Is Reshaping Biodesign: Stability, Accountability, and Trust in Algorithmic Biology

As AI becomes embedded in biological design, the central question is no longer what it can generate, but whether those outputs are stable, accountable, and trustworthy under real-world conditions.

This update outlines three interconnected strands of work:

• Stress-testing AI-designed proteins using national supercomputing infrastructure

• Examining responsibility in the “algorithmic wet lab” at CHI 2026

• Rethinking trust in hybrid bio-digital systems through ARIA-supported research

• Conducting independent technical diligence on AI-driven biology ventures

Together, they address a single structural issue: how to build reliability into AI-driven biodesign.

Stress-Testing AI-Designed Proteins Using National AI Infrastructure

What happens when AI generates biological components from scratch?

A new hands-on AI–biodesign research project is now underway via the UK’s AI Research Resource (AIRR) Gateway route, with access to national supercomputing infrastructure: Isambard-AI.

I am serving as Principal Investigator on this project.

Research Focus

• AI-generated proteins relevant to biodesign and biofabrication

• Structural stability under controlled variation

• Behaviour of newly generated biological interfaces

• Reliability under changing environmental conditions

Unlike optimisation-focused workflows that refine existing proteins, this project investigates first-principles generation — examining how de novo AI-designed biological components behave when conditions shift.

Why Stability Matters

As generative systems move into laboratory workflows:

• Possibility is no longer the main concern

• Reliability becomes the core issue

• Robustness under perturbation determines feasibility

In biotechnology and biofabrication contexts, small instabilities can cascade into material failure or experimental collapse. AI outputs must therefore withstand scrutiny beyond computational metrics.

This work contributes empirical data to a growing field that often advances faster in claims than in verification.

Biodesign × AI at CHI 2026: The Algorithmic Wet Lab

In April 2026, I will moderate a panel at CHI 2026 (Barcelona) titled:

Biodesign × AI: Interactions in the Algorithmic Wet Lab

The session explores how AI systems are not merely analysing biological data but actively shaping:

• Experimental direction

• Material design

• Laboratory optimisation workflows

Panel Participants

• Orkan Telhan

• Iohanna Nicenboim

• Margherita Pevere

• Carolina Ramirez-Figueroa

These researchers and practitioners work across design, HCI, and bio-digital systems.

Core Structural Questions

When AI participates in laboratory processes:

1. Who is designing?

2. Where does responsibility sit?

3. How is accountability structured when outcomes emerge from human–algorithm–organism systems?

4. What governance mechanisms apply to distributed interpretation?

My role as moderator is to surface tensions between:

• Technical capability

• Design responsibility

• Institutional governance

As AI moves from analytical tool to experimental co-agent, responsibility frameworks must evolve accordingly.

Rethinking Trust at the Molecular Scale

How should trust function in hybrid bio-digital systems?

The Trusted Molecular Memory project, supported by the Advanced Research and Invention Agency (ARIA) under its Trust Everything, Everywhere discovery programme, has now concluded.

The project was led by Larissa Pschetz at the University of Edinburgh.

Through Biodesign Academy, I contributed analytical work focused on:

• Reframing verification in AI-driven automation

• Examining how trust infrastructures adapt when computation intersects with physical and biological systems

• Clarifying structural trust mechanisms in hybrid environments

Key Insight

Rather than proposing a new technical control layer, the project examined:

• How trust is constructed

• Where verification breaks down

• Why existing computational trust models fail at molecular and material scales

As biological systems become programmable, verification must extend beyond digital audit trails into material processes.

Publication details will follow upon release.

Independent Technical Diligence in AI-Driven Biology

Why diligence matters in AI–biology ventures

Across research and advisory work, a consistent pattern emerges:

• Claims accelerate faster than feasibility

• Stability is assumed rather than tested

• Governance lags behind integration

Alongside academic research, I conduct independent technical diligence for EU and UK investors and institutions assessing AI-driven biology companies.

Diligence Focus Areas

• Biological feasibility vs. AI narrative inflation

• Structural stability of generated biological systems

• Material verification pathways

• Governance and regulatory risk

• Integration into institutional infrastructure

This work draws on extensive experience across biotech, design academia, and industry collaboration — bridging computational methods and biological material systems.

The goal is simple: separate capability from credibility.

Building Reliability into AI-Driven Biodesign

AI in biology is advancing rapidly. The technical frontier is expanding.

What now matters most is:

• Structural robustness

• Verification under variation

• Clear responsibility models

• Trust infrastructures aligned with material reality

The projects outlined above — national supercomputing research, international research dialogue, and trust-focused inquiry — represent different expressions of the same effort:

Ensuring AI-driven biodesign systems hold up under scrutiny.

Key Takeaways

• AI-generated proteins must be tested for structural stability, not just computational plausibility

• Algorithmic participation in laboratory design raises accountability challenges

• Trust in bio-digital systems requires new verification frameworks

• Independent technical diligence is critical as AI–biology ventures scale

• Capability alone does not ensure feasibility or governance readiness

FAQs About AI in Biodesign

What is AI-driven biodesign?

AI-driven biodesign refers to the use of generative and analytical AI systems to design biological materials, proteins, interfaces, and laboratory workflows.

Why is stability more important than optimisation?

Optimisation improves performance within known systems. Stability determines whether newly generated biological systems remain functional under real-world variation.

What is the “algorithmic wet lab”?

The algorithmic wet lab describes laboratory environments where AI systems actively influence experimental direction, design choices, and material outcomes.

Why does trust need to be rethought at the molecular scale?

Traditional computational trust models rely on digital verification. Biological systems introduce material uncertainty, making verification more complex.

What does independent technical diligence involve in AI–biology?

It involves assessing biological feasibility, structural robustness, governance frameworks, and alignment between technical claims and material realities.