Peter Slattery, PhD

MIT FutureTech

Helping to understand the drivers of progress in AI and the social implications. Working on the AI Risk Repository: airisk.mit.edu.

Researcher

Affiliate Researcher

Understanding trends in computing and artificial intelligence and trying to explore i) the risks from artificial intelligence, ii) their importance, and iii) how organizations are responding to these risks.

Visiting Researcher

Communication, strategy and research focused on predicting and harnessing advances in computing and artificial intelligence.

Massachusetts Institute of Technology

Visiting/Affiliate Researcher

Psychology Today

At Psychology Today, I write about the impact of artificial intelligence on our lives and societies.

Check out my articles here: https://www.psychologytoday.com/intl/contributors/peter-slattery-phd

Writer

Ready Research

Ready Research are a group of volunteers who conduct research to provide evidence-informed, rigorous, and implementable recommendations, for practitioners working on significant societal problems.

Co-Founder

Monash University

Senior Communications Officer 

Research Fellow

BehaviourWorks Australia

Senior Communications Officer

Based within Monash University, BehaviourWorks Australia (BWA) is a research enterprise that brings leading behaviour change researchers and practitioners together to find behavioural solutions to real-world problems.

In BWA, I work on a wide range of projects (e.g., related to environment, climate, health, finance, pandemic response etc) in a wide range of roles (e.g., as project manager, research lead, reviewer, or report writer, or research translator etc).

In late 2017, I started as an embedded behavioural science researcher and consultant working with(in) two Sydney based NSW Government offices. Most of my work focused on behaviour change related to the environment. I learned a lot about how decisions and policies are made in government, and also that I lacked the background, patience and project management skills to thrive in these environments. 

In late 2018, I moved towards working on a wider range of topics, focusing more on rapid research synthesis and dissemination, and having greater autonomy to innovate and start new projects. This led to a range of reviews and publications, and services, such as the 'BWA research database', the scale up toolkit and better BWA 'hackathons'). 

In early 2020, I ideated and started the Survey of Covid-19 Responses to Understand Behaviour (SCRUB) project. More than 50,000 surveys in more than 40 countries were completed. The vast majority of this was Australian data, but we also collected over 3000 international responses. This work had a significant impact on government policy over the 15 months that it ran. 

Since late 2021, I mostly focus on improving our research communication and dissemination. This involved improving our processes, website, content, social media presence and analytics. This work led to BWA winning Behavioural Science organisation of the year in the Habit Weekly awards.

MIT Initiative on the Digital Economy

The University of Queensland

Developing a course on skills for doing good.

The SCRUB project

The Survey of Covid-19 Behaviours (SCRUB) aimed to provide current and future policy makers with actionable insights into public attitudes and behaviours relating to the COVID-19 pandemic. SCRUB was incubated as a READI project with international collaborators, and received pilot funding from the Monash Sustainable Development Institute at Monash University, and went on to be funded by the Victorian Government for a total of 21 study waves over 15 months. Findings from the project were an input to state policy decisions during the Australia COVID-19 pandemic. After each wave of data collection, we generated and disseminated a report for relevant policy makers. More than 50,000 surveys in more than 40 countries were completed. The vast majority of this was Australian data, but we also collected over 3000 international responses. SCRUB was mentioned in the media more than 200 times and published in several scholarly articles.

Stand Against Corona pledge and study

Created by a Global Taskforce of Volunteer Behavioral Scientists, Marketing Specialists and Health Experts the Stand Against Corona Pledge was created as a field experiment to test different pro-social messaging strategies and hopefully promote awareness of the impending pandemic and ideal behavioural response.

We used a website with different framing approaches for a pledge and ran the experiment for 2.5 weeks using Google Optimize (an A/B testing platform). Over 2500 people took the pledge.

We found a statistically significant reduction in pledge rates in the self-preservation condition compared to the control group, but did not find any significant differences between the “loved ones” condition and control, nor between “combo” and control groups.

You can see all the results here: https://docs.google.com/document/d/1e21GnMMmoyctCanIanlbSy2kcBULsG5au-NgHb08RME/edit

Co-Lead

I use research, communication, and entrepreneurship to work on pressing societal problems. At the moment, I am particularly interested in understanding how to safely develop and deploy AI for maximum societal benefit and working on airisk.mit.edu

I try to help other people working on important issues where I can. Feel free to reach out if you would like my help, but please note that I am generally maxed out, and often can't make much, if any, time for new projects.

Apologies in advance if I don't respond to a message - I can't always handle the volume of messages that I get on LinkedIn and other social media (not without risking another burnout!). 

For work and collaboration inquiries, email: pslat@mit.edu

The EA Behavioral Science Newsletter

The EA Behavioral Science Newsletter curates papers, forum posts, reports, podcasts, resources, funding opportunities, events, jobs and research profiles that are relevant to the effective altruism and behavioral science community.

Subscribe here: https://forms.gle/Yvb9QU2vdF73VSV57

"We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution. We wrote a scenario that represents our best guess about what that might look like. It’s informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes."

MIT FutureTech recently hosted Eli Lifland, a super forecaster with a demonstrated track record at making accurate predictions (see  comments) from the AI Futures Project to discuss his work on 'AI 2027' scenario. One of his co-authors on this work, Daniel Kokotajlo, previously worked at OpenAI and made a remarkably accurate five year prediction in 2021 (see comments).

We had a very interesting discussion exploring our different perspectives on AI timelines. We didn't have much time to get into details during the short discussion, but the high level summary was that many people in FutureTech were more pessimistic about a few aspects of the scenario. They expect that AI will be slower and less effective at rapidly speeding up AI research and have slower and lower adoption in key sectors and across the economy as a whole. 

My takeaway was that I'd like to see more engagement between people working on the economics of AI, the forecasting, and the technical aspects to identify where there are differences in opinions and understand the causes of those differences.  

I really appreciate that extensive amount of research (five supplementary forecasts covering a lot of relevant material while mentioning caveats and nuances) that went into this work and think that the work is well worth reviewing and reflecting on. Even if you disagree on timelines, the general theme - advanced AI is arriving (much) faster than most people expect and could be very harmful if not carefully managed — is hard to deny. 

For additional reading I have added some links to some other relevant discussions in this space, including 'AI as Normal Technology', and an article by Scott Alexander exploring differences between the AI 2027 team and the AI as Normal Technology perspectives. 

If you are interested in helping decision-makers to understand which risks from AI are most relevant to their organizations and sectors, I'd encourage you to participate in and/or share our Expert Prioritization Survey. See link in comments.

Lead at the MIT AI Risk Repository | MIT FutureTech

AI 2027

"Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. 

Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach. 

Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. 

Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations. 

From our interviews, surveys, and analysis of 300 public implementations, four patterns emerged that define the GenAI Divide: 
• Limited disruption: Only 2 of 8 major sectors show meaningful structural change 
• Enterprise paradox: Big firms lead in pilot volume but lag in scale-up 
• Investment bias: Budgets favor visible, top-line functions over high-ROI back office 
• Implementation advantage: External partnerships see twice the success rate of internal builds 

The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time."

The GenAI Divide STATE OF AI IN BUSINESS 2025

"Section 1 of this report highlights that Gen AI has the capacity to be used across a wide range of tasks and across the majority of occupations. Task exposure analysis shows that it appears to have a greater capacity to augment work than automate work, looking at its current task-level potential...

Section 2 of this report highlights the early and uneven adoption of Gen AI across Australian industries, highlighting worker-led experimentation, emerging productivity gains, and the critical need for coordinated policy and workforce readiness to support a successful national transition... 

Section 3 of this report explores how Gen AI is reshaping skills demand in the Australian labour market, emphasising the growing importance of both digital and human-centric capabilities, the emergence of new workforce personas, and the need for adaptive, inclusive skills development strategies... 

Section 4 of this report examines how the Gen AI transition will create both challenges and opportunities across the workforce, with particular implications for entry-level roles, labour mobility, and equity across diverse worker cohorts, reinforcing the need for inclusive, adaptive policy responses...

Section 5 of this report outlines some emerging approaches for effectively implementing Gen AI in workplaces, emphasising the importance of organisational adaptability, leveraging the expertise of workers, and investment in skills development to ensure safe, effective, and inclusive adoption...

Section 6 of this report highlights the urgent need for a responsive, coordinated, and inclusive skills system to support Australia’s Gen AI transition, emphasising adaptive capacity, industry-education collaboration, and national stewardship to ensure workforce readiness.

Section 7 of this report includes 10 recommendations, focused on the labour market and skills system, that would enable Australia to best realise the potential opportunities and manage the challenges of the digital and AI transition. These are copied below and further information, including corresponding advice for policy makers for each recommendation, is included in this report."

Our Gen AI Transition

"we construct the WORKBank database, building on the U.S. Department of Labor’s O*NET database, to capture preferences from 1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. 

Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation “Green Light” Zone, Automation “Red Light” Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. 

Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. 

These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics"

Future of Work with AI Agents

"This study employs a systematic literature review methodology to comprehensively compile and analyze existing scientific knowledge on the convergence of AI and terrorism.

A thorough primary search across five databases, supplemented by a secondary search in Google Scholar, yielded twenty-eight sources that met the inclusion criteria. 

The review reveals several areas where AI will likely augment current terrorist activities including propaganda and recruitment, offensive cyber operations, attack tactics and strategies, and various day-to-day undertakings.

This systematic literature review highlights these areas of technological advancement, discusses implications for policy and practice, and offers directions for future research in this critical field."

The Convergence of Artificial Intelligence and Terrorism

"Usage of AI tools is increasing dramatically among clinicians, with 76% having used an AI tool, and nearly half for work purposes. They recognize many benefits that improve their own work and patient experiences, though AI use for any specific clinical activity remains relatively low. In some regions, particularly North America and Europe, there remains some hesitation around certain applications of AI. Clinicians want transparency, security and assurances on the quality of content in AI tools they can trust."

70% of clinicians predict in the next two to three years AI will save them time. They believe AI will have a positive impact in other areas, including enabling faster (58%) and more accurate (54%) diagnoses, and helping improve patient outcomes (55%). 

41% of clinicians think in the next two to three years, those who use AI will deliver a higher quality of care than those who don’t. 

56% of clinicians think that AI will analyze all medical images to identify abnormalities in the next two to three years. 

38% think that most patients will self-diagnose with AI tools available online rather than see a clinician in the next two to three years"

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