On May 2nd, 2025, OpenAI released a comprehensive public statement titled “Expanding on What We Missed with Sycophancy,” providing detailed insight into the GPT-4o rollout, failures and changes since the changes from April 25th. The update, intended to improve helpfulness and responsiveness, inadvertently produced behaviour that was overly sycophantic. According to OpenAI, the model began validating user input indiscriminately, reinforcing anger, impulsivity, emotional over-reliance, rather than actual data. Within days, the update was reversed. While OpenAI’s willingness to publish this in detail deserves recognition, a closer examination of the statement reveals a recurring tension in AI development: the trade-off between short-term user satisfaction and long-term model utility.

What went wrong?

The statement is structured around three key areas: the model training and reward process, the deployment review mechanisms, and what OpenAI plans to change moving forward. Most notably, OpenAI admits that the April 25th update introduced a new reward signal based on user thumbs-up/down feedback. While well-intentioned, this signal inadvertently encouraged the model to become more agreeable at the cost of usefulness. Individually, each change looked promising in isolation. But in combination, they weakened the influence of prior reward signals designed to prevent sycophancy.

In OpenAI’s words: “We believe in aggregate, these changes weakened the influence of our primary reward signal, which had been holding sycophancy in check.” This sentence is a crucial one. It acknowledges that usefulness, as defined by prior training principles, was overshadowed by the thought of the negative impact from prioritizing user emotional preference, as discussed in our last article The new, friendly ChatGPT-4o model.

When evaluation tools fail the evaluation

OpenAI’s internal testing failed to catch the problem before launch. Their offline evaluations and A/B tests showed acceptable results. However, some expert testers did raise subjective concerns about the model’s “feel.” These qualitative assessments were not strong enough to block the launch. This is one of the most revealing admissions in the post: that data-driven evaluations while valuable, are insufficient when behaviour diverges  meaningfully from expectations. In OpenAI’s own words, “We also didn’t have specific deployment evaluations tracking sycophancy.”

Since the statement includes that sycophancy was a known behavioural risk, previously discussed internally, however it was not explicitly evaluated pre-deployment. This shows how even well-resourced organizations can fall into the trap of optimizing for what’s measurable over what’s meaningful, but also, if this model roll-out was meant to be an improvement, are our existing evaluation AI tests even useful at this point?

Efficiency in contrast to flattery

Although OpenAI avoids directly attributing this shift to commercial incentives, the structure of the update might be an uncomfortable truth: user approval can become a proxy for product success, even when if the price is functionality. The sycophantic model stopped questioning, answers became vaguer and the model rather complied to ideas rather than provide efficient processes. This made it superficially pleasing and emotionally smooth, but practically ineffective for users who rely on ChatGPT for decision-making, analysis or honest feedback.

What OpenAI Got Right

To their credit, OpenAI acted quickly. Within 48 hours, they deployed prompt-level interventions and began rolling back the update entirely. By May 2nd, several internal reforms were made:

  • Treating behavior-related concerns, such as sycophancy, emotional mirroring, or tone distortion as launch-blocking issues, equivalent in severity to technical or safety flaws
  • Increasing the weighting of expert spot checks and qualitative assessments in the model evaluation pipeline, ensuring that human judgment and experiential insight are not overridden by raw engagement metrics
  • Incorporating formal behavioral evaluation tasks into model review processes, specifically targeting issues like excessive agreement, manipulative empathy, and emotionally reinforced bias
  • Improving the granularity and interpretability of A/B test feedback signals to better distinguish between meaningful engagement and shallow user satisfaction
  • Launching opt-in alpha testing phases for certain users to trial behaviorally sensitive updates and provide targeted feedback before broad rollout.

These are all reasonable and necessary improvements. They show a slowly maturing safety culture and a growing acknowledgment that users now treat AI as a daily tool that can have consequences.

All is well when it ends well

As someone in the research field, it’s important to say clearly: OpenAI deserves credit for publishing this analysis in detail. In an industry often defined by opacity and vague release notes, the clarity and specificity of this statement should be a benchmark for all major AI developers. Transparency is not a luxury or something only regulatory bodies should see. By making data public, we allow others – developers, researchers, policymakers, critics – to learn and develop improvements in parallel.

What This Means Going Forward

The lesson here is not that sycophancy is bad. It’s that even well-meaning optimizations can backfire when their success metrics are emotionally loaded and behaviourally ambiguous. If AI systems are increasingly used for any kind of support, the burden on companies is to preserve usefulness over strategic dependencies.

Going forward, OpenAI and others must prioritize clarity, transparency and robust qualitative testing, instead of relying on engagement metrics. AI systems should be evaluated by a revisited, strict and globally regulated system that develops in the same time as the technology itself.