Experiments Illustrated: Can $1 Change Behavior More Than $100?

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Experiments Illustrated: Can $1 Change Behavior More Than $100?

I currently lead a small data team at a small tech company. With everything small, we have a lot of autonomy over what, when, and how we run experiments. In this series, I’m opening the vault from our years of experimenting, each story highlighting a key concept related to experimentation. And here we’ll share a surprising result from an early test on our referral-bonus program and use it to discuss how you might narrow your decision set for experiments (at least when they involve humans). Background: It’s COVID and we need to hire a zillion nurses IntelyCare helps healthcare facilities match with nursing talent. We’re a glorified nurse-recruiting machine and so we’re always looking to recruit more effectively. Nurses come to us from many sources, but those who come via referrals earn higher reviews and stay with us longer. The year was 2020. IntelyCare was a baby company (still is by most standards). Our app was new and most features were still primitive. Some examples… We had a way for IntelyPros to share a referral link with friends but had no financial incentives to do so. Our application process was a major slog. We required a small mountain of documents for review in addition to a phone interview and references. Only a small subset of applicants made it all the way through to working. During a recruiting brainstorm, we latched onto the idea of referrals and agreed that adding financial incentives would be easy to test. Something like, “Get $100 when your friend starts working.” Zero creativity there, but an idea doesn’t have to be novel to be good. Knowing that many people might refer again and again if they earned a bonus, and knowing that our application process was nothing short of a gauntlet, we also wondered if it might be better instead to give clinicians a small prize when their friends start an application. A small prize for something easy vs a big prize for something difficult? I mean, it depends on many things. There’s only one way to know which is best, and that’s to try them out. The referral test We randomly assigned clinicians to one of two experiences: The clinician earns an extra $1/hour on their next shift when their referral starts a job application. (Super easy. Starting an application takes 1–2 minutes.) The clinician earns $100 when their referral completes their first shift. (Super hard. Some nurses race through it, but most applicants take several weeks or even months if they finish at all). We held out an equal third of clinicians as a control and let clinicians know the rules via a series of emails. There’s always a risk of spillovers in a test like this, but the thought of one group stealing all the referrals from the other group seemed like a stretch, so we felt good about randomizing across individuals. Decidedly non-social: Many people hear these two options and ask, “Did you think of trying prosocial incentives?” (Example: I refer you, you do something, we both get a prize). Studies show they’re often better than individual incentives and they’re quite common (instacart, airbnb, robinhood,…). We considered these, but our finance team became very sad at the idea of us sending $1 each to hundreds of people who may not ever become employees. I guess Quickbooks doesn’t like that? At some point, you just accept that it’s best to not mess with the finance team. Since the $1/hr reward could not be prosocial without becoming a major headache, we limited payouts in both programs to the referring individual only. This gives us two referral programs where the key differences are timing and the payout amount. Turns out timing and presentation of incentives matter. A lot. Social incentives also matter. A lot. If you’re trying to growth-hack your referral program, you would be smart to consider both of these dimensions before increasing the payout. Nerdy aside: Thinking about things to test Product data science, with minimal exception, is interested in how humans interact with things. Often it’s a website or an app, but it could be a physical object like a pair of headphones, a thermostat, or a sign on the highway. The science comes from changing the product and watching how humans change their behavior as a result. And you can’t do better than watching your customers interact with your product in the wild to learn whether a change was helpful or not. But you can’t test everything. There are infinite things to test and any group tasked with experimenting will have to cut things down to a finite set of ideas. Where do you start? Start with the product itself. Ask people who are familiar with it how they like it, what they wish was different, Sean Ellis, NPS, the Mom Test, etc. This is the common starting point for product teams and just about everyone else. Start with human nature. For many decades Behavioral Scientists have documented particular patterns in human behavior. These scientists go by different names (behavioral economists, behavioral psychologists, etc.). In my humble opinion, the 2nd of these starting points is severely underrated. Behavioral science has documented dozens of behavior patterns that can inform how your product might change most effectively. A few honorable mentions… Loss aversion: people hate losing more than they like winning Peak-End: people remember things more favorably when they end on a positive note Social vs Market Norms: everything changes when people pay for goods and services instead of asking for favors Framing: people make choices based on how information is presented Left-digit Bias: perceptions of a price are disproportionately influenced by the leading digit ($0.99 = 🔥, $1.01 = 🥱) Present Bias: people hate waiting These mental shortcuts aren’t a silver bullet for product and Marketing. We’ve tested many of these in different settings to no effect, but some have worked. Maybe you’ll find $160M under your couch cushions. Zooming in on present bias Many humans demonstrate a behavior pattern where they prefer small, immediate rewards over larger rewards in the future. Social scientists call this present bias and measure it with tough questions like… People who choose immediate, smaller rewards have some measure of present bias. Present bias can vary across people and circumstances. You could be a patient person in most situations. But when you’re hungry or tired… not so much. This is one reason why A/B tests are so useful compared to, say, surveys or interviews. For our experiment, the question is not so different from the lab questions: Would you rather have ~$8 sometime this week or $100 sometime in the coming weeks and maybe never? Experiment results Many of us (present company included) thought the $100 offer would do best. It sounds bigger. It looks better in an email. It’s easy to explain. I also thought we might get more applications from referrals with the $1/hr program, but didn’t think that would carry through to more referrals. I figured people would take the money and run. To my surprise the $1/hr program delivered more applications via referrals and more working clinicians, all at a much lower cost. Image by author The $100 offer led to a 65% increase in referrals compared to our control group. This is huge!. Many people like this type of program because we’re only giving rewards when a referral is successful. It feels like the money is well spent. The $1/hr offer, however, is even better. Referrals increased by 81% compared to our control group. And even though the rewards were smaller and paid out earlier, referrals from these IntelyPros still went on to start working for us at about the same rate as the $100 group.Yes, many IntelyPros earned rewards for referrals that applied and then failed to cross the finish line, but the math still worked out. Even with an imperfect conversion rate from application to working, the total cost per new working IntelyPro was less than half of the $100 group’s cost. ABT: Always be testing The role that referrals play for a business can change for many reasons. As a company becomes bigger and more familiar, referrals become less important. You don’t need people to spread the word because the word is already out. We ran this test in 2020 during a global pandemic. We ran a smaller-scale test a year later and saw similar results. Would we see the same results now, in 2025? Hard to say. Things are much different in the healthcare staffing space nowadays. This is yet another reason why testing is so important. Something like referral bonuses could work wonders for company A while being a dud for company B. The upside of product experiments is that the world is your laboratory. The downside is that it’s hard to know what generalizes to other products. Key takeaways for those who made it this far When you’re running experiments to improve your product and collect data from the humans using your product, behavioral science is a fruitful place to look for test ideas. Sometimes the ideas that seem less great turn out to have the highest impact! Timing and presentation matter for incentives. Before you increase the size of some payout, perhaps put some thought into the time and effort required to earn that payout. (This post was refurbished from my own 2022 post on behavioraleconomics.com)

And here we’ll share a surprising result from an early test on our referral-bonus program and use it to discuss how you might narrow your decision set for experiments (at least when they involve humans).

Background: It’s COVID and we need to hire a zillion nurses

IntelyCare helps healthcare facilities match with nursing talent. We’re a glorified nurse-recruiting machine and so we’re always looking to recruit more effectively. Nurses come to us from many sources, but those who come via referrals earn higher reviews and stay with us longer.

The year was 2020. IntelyCare was a baby company (still is by most standards). Our app was new and most features were still primitive. Some examples…

We had a way for IntelyPros to share a referral link with friends but had no financial incentives to do so.
Our application process was a major slog. We required a small mountain of documents for review in addition to a phone interview and references. Only a small subset of applicants made it all the way through to working.

During a recruiting brainstorm, we latched onto the idea of referrals and agreed that adding financial incentives would be easy to test. Something like, “Get $100 when your friend starts working.” Zero creativity there, but an idea doesn’t have to be novel to be good.

Knowing that many people might refer again and again if they earned a bonus, and knowing that our application process was nothing short of a gauntlet, we also wondered if it might be better instead to give clinicians a small prize when their friends start an application.

A small prize for something easy vs a big prize for something difficult? I mean, it depends on many things. There’s only one way to know which is best, and that’s to try them out.

The referral test

We randomly assigned clinicians to one of two experiences:

The clinician earns an extra $1/hour on their next shift when their referral starts a job application. (Super easy. Starting an application takes 1–2 minutes.)
The clinician earns $100 when their referral completes their first shift. (Super hard. Some nurses race through it, but most applicants take several weeks or even months if they finish at all).

We held out an equal third of clinicians as a control and let clinicians know the rules via a series of emails. There’s always a risk of spillovers in a test like this, but the thought of one group stealing all the referrals from the other group seemed like a stretch, so we felt good about randomizing across individuals.

Decidedly non-social: Many people hear these two options and ask, “Did you think of trying prosocial incentives?” (Example: I refer you, you do something, we both get a prize). Studies show they’re often better than individual incentives and they’re quite common (instacart, airbnb, robinhood,…). We considered these, but our finance team became very sad at the idea of us sending $1 each to hundreds of people who may not ever become employees.

I guess Quickbooks doesn’t like that? At some point, you just accept that it’s best to not mess with the finance team.

Since the $1/hr reward could not be prosocial without becoming a major headache, we limited payouts in both programs to the referring individual only. This gives us two referral programs where the key differences are timing and the payout amount.

Turns out timing and presentation of incentives matter. A lot. Social incentives also matter. A lot. If you’re trying to growth-hack your referral program, you would be smart to consider both of these dimensions before increasing the payout.

Nerdy aside: Thinking about things to test

Product data science, with minimal exception, is interested in how humans interact with things. Often it’s a website or an app, but it could be a physical object like a pair of headphones, a thermostat, or a sign on the highway.

The science comes from changing the product and watching how humans change their behavior as a result. And you can’t do better than watching your customers interact with your product in the wild to learn whether a change was helpful or not.

But you can’t test everything. There are infinite things to test and any group tasked with experimenting will have to cut things down to a finite set of ideas. Where do you start?

Start with the product itself. Ask people who are familiar with it how they like it, what they wish was different, Sean Ellis, NPS, the Mom Test, etc. This is the common starting point for product teams and just about everyone else.
Start with human nature. For many decades Behavioral Scientists have documented particular patterns in human behavior. These scientists go by different names (behavioral economists, behavioral psychologists, etc.).

In my humble opinion, the 2nd of these starting points is severely underrated. Behavioral science has documented dozens of behavior patterns that can inform how your product might change most effectively.

A few honorable mentions…

Loss aversion: people hate losing more than they like winning
Peak-End: people remember things more favorably when they end on a positive note
Social vs Market Norms: everything changes when people pay for goods and services instead of asking for favors
Framing: people make choices based on how information is presented
Left-digit Bias: perceptions of a price are disproportionately influenced by the leading digit ($0.99 = 🔥, $1.01 = 🥱)
Present Bias: people hate waiting

These mental shortcuts aren’t a silver bullet for product and Marketing. We’ve tested many of these in different settings to no effect, but some have worked. Maybe you’ll find $160M under your couch cushions.

Zooming in on present bias

Many humans demonstrate a behavior pattern where they prefer small, immediate rewards over larger rewards in the future. Social scientists call this present bias and measure it with tough questions like…

People who choose immediate, smaller rewards have some measure of present bias.

Present bias can vary across people and circumstances. You could be a patient person in most situations. But when you’re hungry or tired… not so much. This is one reason why A/B tests are so useful compared to, say, surveys or interviews.

For our experiment, the question is not so different from the lab questions:

Would you rather have ~$8 sometime this week or $100 sometime in the coming weeks and maybe never?

Experiment results

Many of us (present company included) thought the $100 offer would do best. It sounds bigger. It looks better in an email. It’s easy to explain.

I also thought we might get more applications from referrals with the $1/hr program, but didn’t think that would carry through to more referrals. I figured people would take the money and run.

To my surprise the $1/hr program delivered more applications via referrals and more working clinicians, all at a much lower cost.

The $100 offer led to a 65% increase in referrals compared to our control group. This is huge!. Many people like this type of program because we’re only giving rewards when a referral is successful. It feels like the money is well spent.

The $1/hr offer, however, is even better. Referrals increased by 81% compared to our control group. And even though the rewards were smaller and paid out earlier, referrals from these IntelyPros still went on to start working for us at about the same rate as the $100 group.

Yes, many IntelyPros earned rewards for referrals that applied and then failed to cross the finish line, but the math still worked out. Even with an imperfect conversion rate from application to working, the total cost per new working IntelyPro was less than half of the $100 group’s cost.

ABT: Always be testing

The role that referrals play for a business can change for many reasons. As a company becomes bigger and more familiar, referrals become less important. You don’t need people to spread the word because the word is already out.

We ran this test in 2020 during a global pandemic. We ran a smaller-scale test a year later and saw similar results. Would we see the same results now, in 2025? Hard to say. Things are much different in the healthcare staffing space nowadays.

This is yet another reason why testing is so important. Something like referral bonuses could work wonders for company A while being a dud for company B. The upside of product experiments is that the world is your laboratory. The downside is that it’s hard to know what generalizes to other products.

Key takeaways for those who made it this far

When you’re running experiments to improve your product and collect data from the humans using your product, behavioral science is a fruitful place to look for test ideas.
Sometimes the ideas that seem less great turn out to have the highest impact!
Timing and presentation matter for incentives. Before you increase the size of some payout, perhaps put some thought into the time and effort required to earn that payout.

(This post was refurbished from my own 2022 post on behavioraleconomics.com)

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Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ; Dallas, TX; Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland. With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116, a bill

New Reports Show How AI, Power, and Investment Trends Are Reshaping the Data Center Landscape

Today we provide a comprehensive roundup of the latest industry analyst reports from CBRE, PwC, and Synergy Research, offering a data-driven perspective on the state of the North American data center market. To wit, CBRE’s latest findings highlight record-breaking growth in supply, soaring colocation pricing, and mounting power constraints shaping site selection. For its part, PwC’s analysis underscores the sector’s broader economic impact, quantifying its trillion-dollar contribution to GDP, rapid job growth, and surging tax revenues. Meanwhile, the latest industry analysis from Synergy Research details the acceleration of cloud spending, AI’s role in fueling infrastructure demand, and an unprecedented surge in data center mergers and acquisitions. Together, these reports paint a picture of an industry at an inflection point—balancing explosive expansion with evolving challenges in power availability, cost pressures, and infrastructure investment. Let’s examine them. CBRE: Surging Demand Fuels Record Data Center Expansion CBRE says the North American data center sector is scaling at an unprecedented pace, driven by unrelenting demand from artificial intelligence (AI), hyperscale, and cloud service providers. The latest North America Data Center Trends H2 2024 report from CBRE reveals that total supply across primary markets surged by 34% year-over-year to 6,922.6 megawatts (MW), outpacing the 26% growth recorded in 2023. This accelerating expansion has triggered record-breaking construction activity and intensified competition for available capacity. Market Momentum: Scaling Amid Power Constraints According to CBRE, data center construction activity reached historic levels, with 6,350 MW under development at the close of 2024—more than doubling the 3,077.8 MW recorded a year prior. Yet, the report finds the surge in development is being met with significant hurdles, including power constraints and supply chain challenges affecting critical electrical infrastructure. As a result, the vacancy rate across primary markets has plummeted to an all-time low of 1.9%, with only a handful of sites

Minnesota PUC Says No to Amazon’s Bid to Fast-Track 250 Diesel Generators for Data Center

Amazon is facing scrutiny and significant pushbacks over its plan to install 250 diesel backup generators for a proposed data center in Becker, Minnesota. Much of the concern had been due to the fact that the hyperscaler was seeking an exemption from the state’s standard permitting process, a move that has sparked opposition from environmental groups and state officials. Aggregate Power that Matches Nuclear Power Generation Amazon’s proposed fleet of diesel generators would have a maximum power output almost equivalent to the 647 MW that is produced by Xcel Energy’s nuclear plant in Monticello, one of the two existing nuclear generation stations in the state. Meanwhile, as reported by Datacenter Dynamics, according to a real estate filing published with the Minnesota Department of Revenue, the land parcel assigned for the Amazon data center in Becker was previously part of Minneapolis-based utility Xcel’s coal-powered Sherco Site. Amazon argues that the diesel generators in question are essential to ensuring reliable and secure access to critical data and applications for its customers, including hospitals and first responders. However, opponents worry about the environmental impact and the precedent it may set for future large-scale data center developments in the state. The Law and Its Exception Under Minnesota state law, any power plant capable of generating 50 megawatts or more that connects to the grid via transmission lines must obtain a Certificate of Need from the Public Utilities Commission (PUC). This certification ensures that the infrastructure is necessary and that no cheaper, cleaner alternatives exist. Amazon, however, contends that its generators do not fall under this requirement because they are not connected to the larger electric grid; power generated would be strictly used by the data center suffering an outage from its primary power source. That power would be generated locally, and not transmitted over

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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