My point was that the 10-70% range reflects different outcomes depending on the actions we take, not just optimism as a feeling or a belief. Optimism can certainly motivate us to act, but without those actions, it has little to no impact on the actual probabilities.
It seems our main disagreement lies in how we view these probabilities. I see them as dynamic and heavily influenced by the actions we take as a movement, while you seem to view them as more static and inherent to the situation, essentially outside of our control.
I think that’s an extremely important distinction because it fundamentally shifts how we approach this challenge. If we believe the odds are fixed, we become passive observers, resigned to whatever fate has in store. But if we recognise our power to influence those odds through strategic action, technological innovation, and effective execution, we become active participants in creating the future we want. This empowers us to take responsibility, to strive for optimal solutions, and to push beyond the limitations of the status quo.
To better understand your perspective, could you provide your estimated probabilities for ending factory farming by 2060, considering these scenarios:
Scenario 1: Complacency.We maintain the status quo, with minimal changes to our current approach. We fail to identify and effectively target the key pressure points within the system, and we don’t create the necessary feedback loops to amplify our impact and hinder the growth of industrial animal agriculture.
Scenario 2: Moderate Improvement.We make incremental progress in our strategies and adoption of new technologies, but we don’t fully capitalize on opportunities for exponential growth. We achieve some success in identifying and influencing key pressure points, but our efforts are not comprehensive or optimally coordinated.
Scenario 3: Optimal Execution.We proactively identify and exploit every opportunity for exponential growth within the movement. Simultaneously, we precisely target the most influential pressure points within the system and create powerful feedback loops that accelerate our progress while hindering the expansion of industrial animal agriculture. We achieve maximum coordination and efficiency, fully leveraging every available resource and opportunity with optimal strategic foresight.
I’m really interested in seeing how your probabilities compare across these scenarios, especially for scenarios 2 and 3. While 1-5% might seem understandable (albeit quite pessimistic) for scenario 1, where we assume minimal change, it seems considerably less likely that those odds wouldn’t drastically increase with the improved strategy and execution described in scenarios 2 and 3.
To put it into perspective, imagine a basketball team with a 1-5% chance of winning a game. If they then acquire a star player, develop a brilliant new strategy, and execute it flawlessly, wouldn’t their chances of winning increase substantially? Similarly, in the fight against factory farming, if we effectively leverage exponential technologies, coordinate our efforts optimally, and execute our strategies with precision, it seems almost impossible that our probability of success wouldn’t see a major boost.
Hi Sam, I’m finding it hard to respond to your request because IMO the scenarios are too vague. To use your basketball metaphor, a specific player is something that I can integrate meaningfully into a prediction, but executing the strategy flawlessly is much more nebulous. Do you have specific ideas in mind of what scenario 3 might look like? How much increased funding is there? I think to make a good conditional prediction it would need to be something we could clearly decide whether or not we achieved it? Raised an extra $50m for the movement has a clear yes/no, whereas “achieve maximum coordination and efficiency” seems very subjective to me.
My point was that the 10-70% range reflects different outcomes depending on the actions we take, not just optimism as a feeling or a belief. Optimism can certainly motivate us to act, but without those actions, it has little to no impact on the actual probabilities.
It seems our main disagreement lies in how we view these probabilities. I see them as dynamic and heavily influenced by the actions we take as a movement, while you seem to view them as more static and inherent to the situation, essentially outside of our control.
I think that’s an extremely important distinction because it fundamentally shifts how we approach this challenge. If we believe the odds are fixed, we become passive observers, resigned to whatever fate has in store. But if we recognise our power to influence those odds through strategic action, technological innovation, and effective execution, we become active participants in creating the future we want. This empowers us to take responsibility, to strive for optimal solutions, and to push beyond the limitations of the status quo.
To better understand your perspective, could you provide your estimated probabilities for ending factory farming by 2060, considering these scenarios:
Scenario 1: Complacency. We maintain the status quo, with minimal changes to our current approach. We fail to identify and effectively target the key pressure points within the system, and we don’t create the necessary feedback loops to amplify our impact and hinder the growth of industrial animal agriculture.
Scenario 2: Moderate Improvement. We make incremental progress in our strategies and adoption of new technologies, but we don’t fully capitalize on opportunities for exponential growth. We achieve some success in identifying and influencing key pressure points, but our efforts are not comprehensive or optimally coordinated.
Scenario 3: Optimal Execution. We proactively identify and exploit every opportunity for exponential growth within the movement. Simultaneously, we precisely target the most influential pressure points within the system and create powerful feedback loops that accelerate our progress while hindering the expansion of industrial animal agriculture. We achieve maximum coordination and efficiency, fully leveraging every available resource and opportunity with optimal strategic foresight.
I’m really interested in seeing how your probabilities compare across these scenarios, especially for scenarios 2 and 3. While 1-5% might seem understandable (albeit quite pessimistic) for scenario 1, where we assume minimal change, it seems considerably less likely that those odds wouldn’t drastically increase with the improved strategy and execution described in scenarios 2 and 3.
To put it into perspective, imagine a basketball team with a 1-5% chance of winning a game. If they then acquire a star player, develop a brilliant new strategy, and execute it flawlessly, wouldn’t their chances of winning increase substantially? Similarly, in the fight against factory farming, if we effectively leverage exponential technologies, coordinate our efforts optimally, and execute our strategies with precision, it seems almost impossible that our probability of success wouldn’t see a major boost.
Hi Sam, I’m finding it hard to respond to your request because IMO the scenarios are too vague. To use your basketball metaphor, a specific player is something that I can integrate meaningfully into a prediction, but executing the strategy flawlessly is much more nebulous. Do you have specific ideas in mind of what scenario 3 might look like? How much increased funding is there? I think to make a good conditional prediction it would need to be something we could clearly decide whether or not we achieved it? Raised an extra $50m for the movement has a clear yes/no, whereas “achieve maximum coordination and efficiency” seems very subjective to me.