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Progress in AI is accelerating. Alignment research isn’t keeping up. Most solutions focus on narrow paths. We believe the space of possibilities is larger — and largely unexplored.
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Study how humans naturally cooperate. Apply it to design more aligned AI behaviors.
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Leverage brain-computer interfaces to better model decision-making and agency.
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Build bipartisan consensus to create durable AI safety policies.
Protect insiders who raise concerns. Surface critical information early.
Channel capital toward underexplored, high-leverage research directions.
Investigate how awareness and self-modeling might guide safer AI.
Built different by design.
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We've been focused on this for a decade with no external distractions.
No outside investors pushing for speed over safety.
Developers, neuroscientists, and AI researchers solving hard problems together.
We prioritize progress over publication. Building over posturing.
Modern society is too focused on near term gains instead of the long term consequences. We've invested time and money into something we believe is crucial to our species in the long run.
Our original theory of change involved enhancing human cognitive capabilities to address challenges like AI alignment. While we're now exploring multiple approaches to AI safety, we continue to see potential in BCI technology. If AI-driven scientific automation progresses safely, we anticipate increased investment in BCI research. We're also advocating for government funding to be directed towards this approach, as it represents an opportunity to augment human intelligence alongside AI development.
While our emphasis has shifted towards AI alignment, our work in Brain-Computer Interfaces (BCI) remains an important part of our mission to enhance human agency:
We've developed and open-sourced several tools for the propel and democratize BCI development, like the Neural Data Simulator that facilitated the development of closed-loop BCIs, and the Neurotech Development Kit to model transcranial brain stimulation technologies. These tools have contributed to lowering barriers in BCI research and development.
We won first place in this challenge to develop the best ML models to predict neural data topping the best research labs in the space.
We led the development of widely accepted neuro metadata standards and tools, supporting open-source neuro-analysis software projects like MNE, OpenEphys, and Lab Streaming Layer.
We've joined forces with leading BCI companies like Forest Neurotech and Blackrock Neurotech, helping to bridge the gap between academic research and industry applications.
We've developed secure methods for analyzing neural data and training privacy-preserving machine learning models, addressing crucial ethical considerations in BCI development.
Our original theory of change involved enhancing human cognitive capabilities to address challenges like AI alignment. While we're now exploring multiple approaches to AI safety, we continue to see potential in BCI technology. If AI-driven scientific automation progresses safely, we anticipate increased investment in BCI research. We're also advocating for government funding to be directed towards this approach, as it represents an opportunity to augment human intelligence alongside AI development.
While our emphasis has shifted towards AI alignment, our work in Brain-Computer Interfaces (BCI) remains an important part of our mission to enhance human agency:
We've joined forces with leading BCI companies like Forest Neurotech and Blackrock Neurotech, helping to bridge the gap between academic research and industry applications.
We won first place in this challenge to develop the best ML models to predict neural data topping the best research labs in the space.
Our original theory of change involved enhancing human cognitive capabilities to address challenges like AI alignment. While we're now exploring multiple approaches to AI safety, we continue to see potential in BCI technology. If AI-driven scientific automation progresses safely, we anticipate increased investment in BCI research. We're also advocating for government funding to be directed towards this approach, as it represents an opportunity to augment human intelligence alongside AI development.
While our emphasis has shifted towards AI alignment, our work in Brain-Computer Interfaces (BCI) remains an important part of our mission to enhance human agency:
At AE Studio, we tackle ambitious, high-impact challenges using neglected approaches.
Starting with Brain-Computer Interfaces (BCI), we bootstrapped a consulting business, launched startups, and reinvested into frontier research—leading to collaborations with Forest Neurotech and Blackrock Neurotech.
Today, we’re 160 strong—engineers, designers, and data scientists—focused on increasing human agency.
Now, we’re applying our proven model to AI alignment, accelerating safety startups like Goodfire AI and NotADoctor.ai to tackle existential risks.
Our data scientists - from places like Stanford, CalTech and MIT - are highly collaborative, efficient and pragmatic.
Humanity is too focused on capitalism in a competitive landscape. We've invested time and money into something we believe is crucial to our species in the long run.
We described our alignment research agenda, focusing on neglected approaches, which received significant positive feedback from the community and has updated the broader alignment ecosystem towards embracing the notion of neglected approaches. Notably, some of the neglected approaches we propose could have a negative alignment tax, a concept we elaborate on in our LessWrong post "The case for a negative alignment tax" that challenges traditional assumptions about the relationship between AI capabilities and alignment.
We also discussed our approach to alignment, AI x-risks, and many other topics in a couple of podcasts:
We described our alignment research agenda, focusing on neglected approaches, which received significant positive feedback from the community and has updated the broader alignment ecosystem towards embracing the notion of neglected approaches. Notably, some of the neglected approaches we propose could have a negative alignment tax, a concept we elaborate on in our LessWrong post "The case for a negative alignment tax" that challenges traditional assumptions about the relationship between AI capabilities and alignment.
We described our alignment research agenda, focusing on neglected approaches, which received significant positive feedback from the community and has updated the broader alignment ecosystem towards embracing the notion of neglected approaches. Notably, some of the neglected approaches we propose could have a negative alignment tax, a concept we elaborate on in our LessWrong post "The case for a negative alignment tax" that challenges traditional assumptions about the relationship between AI capabilities and alignment.
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