At Astera, we’re seeding an ecosystem of mission-driven, open-first tools, people, organizations, and products to ensure that the abundance unlocked by rapid technological progress is shared broadly. One of the principles we’ve adopted in service of this mission is radical transparency – we’re committed to doing all we can to make it easy for others to follow, reuse, and improve on whatever we do, even in cases where the results of our work aren’t compelling.

In this piece, we describe a workshop on scaling synthetic biology that we co-hosted with Metaculus back in June, titled “Scale is All You Need.” The workshop brought together professional forecasters with entrepreneurs, investors, and experts in synthetic biology in an attempt to work both backwards from shared objectives and forwards to forecast the anticipated effect of potential interventions. Our goals were (1) to identify a set of technical innovations or scientific insights that, if developed, could help resolve the bottlenecks that currently make it difficult for synthetic biology companies to scale to compete with traditional products and (2) to identify and discuss critical points of disagreement between experts’ models on this topic.

As discussed below, we found that the specific outputs of this workshop were not well suited for our particular purposes. However, we believe this workshop format has promise in general, and we would consider running a similar event in the future with modifications. This brief piece is intended to catalog (1) what we did and what we would recommend changing, (2) the materials (including templates and guides) we developed for the event, so others can use or build on them, and (3) the substantive outputs of the workshop, which may be helpful to others thinking about synthetic biology scaling even if they weren’t well calibrated for our own uses.

About the workshop

During the workshop, we used a series of structured exercises to guide conversation and identify objectives, bottlenecks, and interventions that might be useful for unlocking scale across a range of synthetic biology application areas, spanning medicine, climate, agriculture, food production, and the manufacturing of materials and sustainable chemicals.

The process we used had four stages:

1. Defining specific, quantitative objectives: What large-scale outcome do we hope to see in the world over a significant time scale?
   • Example: “reduce the use of synthetic nitrogen fertilizers by 30% by 2050”
2. Identifying bottlenecks: What blockers, if resolved, might allow those objectives to be met?
   • Example: “high annual cost of biofertilizers”
3. Proposing interventions: What specific, actionable innovations would help resolve those bottlenecks?
   • Example: “a genetically engineered microbe that can fix atmospheric nitrogen at 50% higher efficiency than natural bacteria”
4. Forecasting potential impacts: If the intervention is successfully implemented, what effect is it likely to have on the ultimate outcome we care about?
   • Example: “If a genetically engineered microbe that can fix atmospheric nitrogen at 50% higher efficiency than natural bacteria is created by 2030, will the use of synthetic nitrogen fertilizers decrease by 30% by 2050?”

The methodology is described in further detail in the Metaculus writeup of the event, and our Facilitator’s Guide and templates to guide workflows (here, here and here) may be of use to others wanting to build off this method.

We hoped that by focusing on such granular, concrete forecasting questions, we would be able to limit the number of implicit assumptions that affected participants’ forecasts, and as a result to identify the load-bearing places in which participants’ models, and the beliefs underlying them, actually diverged.

Results

In practice, the results were mixed: Time constraints and the participants’ lack of familiarity with forecasting made for a decent amount of chaos and confusion (despite the participation of Metaculus professional forecasters), and this led to a situation where many of the forecasting questions were not well scoped to address scaling challenges. If we were to run another event in the future, we would likely experiment with doing either the first 3 phases or the last one asynchronously, to allow more rounds of iteration on the forecasting questions and to make sure people were comfortable with the methodology. Additional lessons are addressed at the end of Metaculus’s writeup.

Despite the limited usefulness of specific forecasts, the work of identifying objectives, bottlenecks, and potential interventions did generate a potentially useful set of common themes as well as points of disagreement between the experts’ models. Below is a summary overview of the six themes, as well as links to more detailed distillations of each group’s work by science writer Stephen Thomas:

• Innovation in Biologics has the potential to address significant unmet needs in health. Bottlenecks around manufacturing cost, quality, and yield were discussed in particular.
• Improving crop yields and nutrition has the potential to enhance food security and agricultural sustainability, but is limited by challenges with limited data, long engineering cycles, and the difficulty of domesticating more climate-resilient crops.
• Plant-based proteins have been proposed as a solution to reduce the environmental impact of food production while meeting global protein needs, but price and quality have largely not yet been competitive with traditional animal proteins.
• There are a wide range of biotechnology-based methods for carbon capture and storage for mitigating climate change, spanning enzymatic processes for carbon fixation and enhanced rock weathering, as well as limiting methane released from animal agriculture and waste treatment.
• Biomaterials have a lot of potential for performance and sustainability across many markets, and the group focused on challenges in product and market development for novel materials.
• Bioproduction of clean chemicals can reduce or eliminate the use and generation of hazardous substances, but is limited by feedstock availability, high costs, and limited scalability of biological processes.

This initial experiment in collaborative forecasting yielded valuable methodological lessons and identified key themes in synthetic biology’s potential impact. In line with our commitment to radical transparency, we’ve shared both our process and outcomes—successes and shortcomings alike. Rather than an endpoint, we view this workshop as the beginning of an iterative process to develop better tools for navigating technological acceleration. We invite others to build upon these frameworks, adapt our templates, or suggest entirely new approaches as we collectively work toward a shared vision of technological abundance. By openly sharing our work and learning in public, we hope to catalyze a broader ecosystem of collaborative forecasting tools and practices.