Kevin LaCroix recently highlighted on The D&O Diary Blog a recent WSJ opinion piece titled “Quarterly Reports are Written for AI” by Hebrew University Business School Professor Keren Bar-Hava, which describes how the use of AI has changed the way analysts review MD&A. Kevin’s blog notes:

Professor Bar-Hava studied 108 MD&A reports from 27 top U.S. firms during the period 2021-24. She found that, by contrast to the earlier studies noted above, in which better-performing companies tended to have simpler, shorter reports, a different pattern has emerged. She found that positive tone has steadily increased, even when financial performance declined. Words like “growth,” “resilient,” “opportunity” have become more common. Terms signaling uncertainty, such as “might” or “could,” have declined.

Even “more strikingly,” she observed, the “most positive reports often came from the worst-performing firms.” Professor Bar-Hava says this is “no coincidence,” it is, rather, “strategy.” Tone, she says, has “become a tool to manage how algorithms ‘feel’ about performance.” It also “creates a risk” – that is, “the growing gap between what’s said and what’s true.”

What is happening, Professor Bar-Hava explains, is that companies are responding to “AI-induced disclosure pressure – the incentive to write in a way that performs well under algorithmic scrutiny.” The result “isn’t always more transparency.” It may be the opposite; the result may be “performative optimism crafted to influence machines, not people.”

Professor Bar-Hava identifies three levels on which the “AI-induced disclosure pressure” operates:

– Exposure pressure. AI flags vague or evasive language. Companies feel compelled to sound confident, even when the outlook is uncertain.

– Competitive pressure. Algorithms benchmark tone across peer firms. If a competitor sounds stronger, you look weak by comparison.

– Reputational pressure. AI feeds analyst dashboards, investor platforms and news summaries. One poorly framed sentence can ripple fast.

Professor Bar-Hava rightly notes the potential implications of these developments for possible liability under the securities laws. She notes that the SEC in the past has issued rules intended to improve narrative disclosure, “encouraging clarity, conciseness, and plain English.” But tone, she says, is “now a powerful drive of perception,” and it remains unregulated. That, she says, is a “blind spot.” AI driven tone scores are “influencing market behavior.” And if markets are being gamed, she says, “investors are misled.”

Professor Bar-Hava suggest that “tone” should be treated as “a material disclosure element.” We should monitor linguistic choices, as we do accounting choices, especially as “algorithms become the first line of interpretation.” Otherwise, we risk “building a world where clarity is polished but meaning is lost.”

The final paragraph of Professor Bar-Hava’s article makes an important point, which is that corporate boards “must understand that they’re writing for two audiences, people and machines.” Machines she says, don’t read between the lines, “they read the lines.” If we care about truth in reporting, “we must care how it sounds, not merely what is says.”

It is a somewhat disturbing thought that we are moving down a path of having AI write our disclosures for analysis by AI. I feel that such a trend could only be welcomed by plaintiffs’ lawyers and SEC Enforcement lawyers. As Kevin notes in his blog, “if a company is using AI to improve the way the company’s MD&A is scored under AI-driven analysis, the board must try to ensure that there is no gap between what’s said and what’s true.”

– Dave Lynn