The $2 Million Witness and a Search for Truth

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I have been called for jury duty three times in my life, but this is the first time I was actually picked for a trial. The case was a medical malpractice lawsuit that had been working its way through the courts for a dozen years, and the trial would last two weeks.

I really had no idea what to expect. The first thing that popped into my mind was the classic ‘12 Angry Men,’ and I wondered if I might end up being juror number 8 – the one who convinces everyone to change their minds about the outcome.

Scene from 12 Angry Men (1957)

In this case, a family was seeking damages in the millions for their daughter who suffered a serious injury during birth in 2014. I found it surprising that a malpractice case had gotten this far, but once the trial began, I realized why.

For two weeks, we heard testimony from the family, doctors, nurses, and many experts. It was a complicated case with allegations of negligence, a difficult-to-prove charge, especially so far removed from the day it happened.

After the case, the judge told us the county is backed up with medical malpractice cases. Dozens of them.

I was curious and did a bit of research: About 85,000 malpractice claims are filed annually. Of those, roughly 7–8% go to trial, and defendants (doctors/hospitals) win the majority of cases that do go to trial – somewhere around 75–80% of the time. Plaintiffs tend to do better in settlements. Total payouts run roughly $4–5 billion per year across the country.

Many legal scholars and physicians argue the pre-trial filters are largely ineffective, that the certificate of merit requirement in particular has become a rubber stamp because plaintiff attorneys can always find an expert doctor willing to sign off.

During cross-examination, several experts testified that they charge as much as $750 per hour, spend roughly 20+ hours per case, get paid extra for trial appearances – like thousands extra – and some handled dozens of cases per year. I did the math on my juror notepad. The number I came up with for one expert was somewhere north of $2 million a year.  Sitting there, I wondered if I was hearing real evidence or the opinions of a hired gun.

I wanted to know more, so after the case, I began researching. There’s a cottage industry of physicians and other medical professionals who earn substantial income – sometimes their primary income – by testifying as experts. A peer-reviewed article published in ScienceDirect states directly that the litigation climate “has spawned and generated a new and thriving cottage industry, the expert witness for hire,” noting that individuals advertise their credentials and opinions in law journals, hoping to attract cases and lucrative associations. On the money side, according to the R Street Institute, four hours of expert testimony per week could generate over $100,000 in income, and expert witnesses can earn between $500 and $1,500 per hour, according to SEAK Inc.

Medical societies have tried to police this with ethics guidelines, but enforcement is weak and inconsistent. The AMA Code of Medical Ethics Opinion 9.07 states that physicians cannot allow financial concerns to influence the nature of testimony and calls on medical professional societies and state licensing boards to sanction those who give false or misleading testimony. But the enforcement reality is telling: there is an ethical conflict for professional associations that discipline their members, as such associations are charged with ensuring their members’ competence, yet also advocate for constituents who want to protect them from lawsuits. A real case illustrates the gap: in 2002, the North Carolina Medical Board disciplined a surgeon by revoking his medical license for giving improper expert witness testimony, but on appeal, the state supreme court reversed the decision on the grounds that the expert had rendered his opinions in good faith.

The legal standard for expert testimony, the Daubert standard federally (with state variations), is supposed to filter out junk science, but judges are generalists who often lack the technical background to critically evaluate competing medical opinions. A peer-reviewed study published on PubMed found that about half of all judges have had no formal education in handling scientific evidence, yet 91% felt comfortable in their gatekeeper role. That contrast alone is damning. Under Daubert, the responsibility for assessing scientific acceptability in U.S. courts has shifted from expert witnesses to judges, who are usually without scientific training.

As long as an expert has credentials and a plausible-sounding theory, it usually gets in front of the jury. The Florida Bar Journal notes that a review of case law after Daubert shows that rejection of expert testimony is the exception rather than the rule, and that exclusion is viewed as a drastic remedy to be invoked only under the most compelling circumstances. A 1998 article in the Fordham Urban Law Journal concluded that junk science in the courtroom “emanates from testimony by expert witnesses hired not for their scientific expertise, but for their willingness, for a price, to say whatever is needed to make the client’s case.”

In some respects, a medical malpractice trial can come down to which experts are more believable to the jury. The verdict in these cases impacts the lives of both the plaintiff and the defendant. And when faced with a competing chorus of credentialed opinions, it’s very hard to determine the truth. 

There’s a line in “12 Angry Men” where Henry Fonda’s character says, “It’s always difficult to keep personal prejudice out of a thing like this. And wherever you run into it, prejudice always obscures the truth. I don’t really know what the truth is. I don’t suppose anybody will ever really know… No jury can declare a man guilty unless it’s sure.”

While that story was about a murder, I came away with the same feeling after this trial.  Like the movie, my jury also found for the defendants.

Perhaps the question isn’t whether juries can handle the truth. It’s whether the system is designed to deliver it.

References

Cottage industry / expert witness for hire ScienceDirect — Expert Witness Testimony: An Update https://www.sciencedirect.com/science/article/abs/pii/S0733861905701390 (Note: this page is paywalled but the link is valid and the citation is legitimate)

Expert witness earnings / hourly rates R Street Institute — The Perils of Junk Science in the Courtroom https://www.rstreet.org/commentary/the-perils-of-junk-science-in-the-courtroom/

AMA ethics code / enforcement conflict AMA Journal of Ethics — Ethical Challenges for the Medical Expert Witness https://journalofethics.ama-assn.org/article/ethical-challenges-medical-expert-witness/2016-03

North Carolina license revocation case / enforcement gap PubMed/NIH — The Expert Witness in Medical Malpractice Litigation https://pmc.ncbi.nlm.nih.gov/articles/PMC2628518/

Judges lacking scientific training / Daubert gap PubMed/NIH — Black Robes and White Coats: Daubert Standard and Medical Expert Witnesses https://pmc.ncbi.nlm.nih.gov/articles/PMC11471272/

Rejection of testimony is the exception / Daubert in practice Florida Bar Journal — The Daubert Expert Standard: A Primer https://www.floridabar.org/the-florida-bar-journal/the-daubert-expert-standard-a-primer-for-florida-judges-and-lawyers/

Fordham Urban Law Journal / hired for willingness to say anything The direct Fordham quote is cited within the R Street piece above with a link to the original: https://ir.lawnet.fordham.edu/cgi/viewcontent.cgi?article=1978&context=ulj

I’m an AI cheerleader, but Sulzberger is right

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A.G. Sulzberger, the chairman and publisher of The New York Times, stood up at the WAN-IFRA World News Media Congress in Marseille last week and delivered a blistering takedown of AI companies, I wasn’t the automatic audience for it. I went in skeptical.

The speech, titled “AI, Journalism, and the Uncertain Future of the Public Square,” makes a distinction that a lot of the AI discourse collapses. Sulzberger isn’t arguing that AI is bad. He said so explicitly: “There’s nothing inherently bad about AI technology.” His argument is about the specific choices AI companies are making.  Choices that, in his words, “violate settled law, threaten the viability of creative work, and appear likely to cause a great deal of unnecessary harm.”

That’s a narrower and more defensible claim. And it’s one I find hard to argue with, even from where I’m sitting.

He frames it this way: AI is built on four ingredients: talent, compute, energy, and data. The first three are paid for, because of course they are. No tech CEO would dare suggest engineers work for free. Nobody’s stealing chips from an Nvidia factory. The fourth ingredient is “data,” which is tech-speak for books, music, films and journalism.  It is simply taken without permission or compensation. The argument shifts depending on who’s asking: sometimes it’s fair use, sometimes it’s national security, sometimes it’s just “innovation requires it.” None of those arguments hold up. But the taking continues regardless.

Here’s what that looks like in practice: The New York Times was the single largest source of proprietary data in a major dataset used to train multiple large language models. The Times didn’t agree to that. They weren’t paid for it. And when they sued, they got back what Sulzberger described as two-and-a-half years of litigation and a bill north of $20 million.

What makes this especially galling is that the AI companies aren’t even pretending the journalism is worthless. A Microsoft vice president said “premium content meaningfully improves response quality.” OpenAI has acknowledged it would be “impossible to train today’s leading AI models without using copyrighted materials.” You are what you eat — they just prefer not to pay for the meal.

This isn’t a theoretical threat. Traffic to the country’s largest news websites has fallen more than 45 percent over the last four years. AI-driven search results now deliver news without directing users to the outlet that produced it. Referral traffic from leading AI tools runs 96 percent lower than Google search. The value exchange that made the internet era survivable, where platforms got content and publishers got audience, has essentially collapsed.

Sulzberger compared what’s happening now to Napster, the old, pirated music platform. Spotify, whatever its critics in the music industry think, at least makes the argument that it has a symbiotic relationship with artists and sends them money. The AI companies don’t bother with that argument. They just take. And when pushed, they disclaim responsibility for what their products produce. Microsoft launched Copilot with the note that it was “for entertainment purposes only.” Premium content. Entertainment purposes. Both true, apparently, depending on which executive is talking.

Sulzberger called it a “tsunami.” He’s not wrong. And I say that as someone who has been something of a cheerleader for the wave.

“We cannot afford to be as naive this time. News organizations are collectively smaller and weaker than two decades ago. Tech giants are bigger and stronger — and far more willing to use their size and power. Meanwhile, the A.I. wave itself may be bigger and faster as the technology continues to improve. Even if things are feeling fine now, remember that these early swells herald an approaching tsunami,” he said.

He urged publishers to stand up for their rights, fight back legally, push legislators, and work together with other creative industries. Good advice. Principled advice. Also advice that sounds considerably more doable when your organization has already spent $20 million on lawsuits and has the balance sheet to keep going.

The New York Times can do this. Most news organizations cannot. A local paper in the Midwest, a regional TV station, a mid-size digital outlet – they don’t have the resources to litigate against OpenAI. They don’t have the lobbyists to shape AI legislation. And the suggestion that they should “deal carefully” with AI companies assumes they’re at the table in the first place. Most of them aren’t being offered a deal at all. Their work is being taken anyway, without so much as a courtesy email.

Sulzberger acknowledged this gap, at least in part. He noted that less than half of one percent of the nearly $350 billion invested in AI last year has gone toward compensating the creators whose work powers these models. That’s not an oversight — it’s a choice. And the organizations with the least leverage are the ones being squeezed the hardest.

The honest answer to “what should smaller newsrooms do?” isn’t a satisfying one. Collective action helps, which is why the coalition of publishers developing shared AI licensing standards is worth watching. But it’s slow, and the technology isn’t waiting around.

What also worries me is that after talking to some digital producers in mid-size markets, I find that AI adoption is underway in some newsrooms with little consideration of the long-term consequences.

And what I keep coming back to is Sulzberger’s phrase about producing journalism “so distinctive it has its own gravity.”

That’s the part of the speech that doesn’t require a legal team. It requires a commitment to original reporting, which is the kind of work that can only exist because a journalist went somewhere, talked to someone, built trust over time, and brought back something no algorithm could have produced.  OpenAI credited the news organizations whose information it cited in just one percent of responses. The other 99 percent? Presented as if the AI thought of it.

Sulzberger is right that the industry has been too quiet and too passive. He’s right that the behavior of AI companies around intellectual property is hard to defend. I say all of this as someone who genuinely thinks AI is one of the most important developments of our professional lifetimes.

At the close of his speech, Sulzberger invoked the old Silicon Valley mantra that “information wants to be free.” Many in journalism bought into it during the last disruption, to their lasting regret. He reminded the audience that the tech philosopher Stewart Brand, who coined the phrase, actually said more: “Information wants to be expensive, because it’s so valuable – the right information in the right place just changes your life.”

Something so valuable needs to be protected.

The AI backlash is real. Newsrooms are caught in the middle.

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A New York Times/Siena poll released this week found that 35 percent of Americans think artificial intelligence is “mostly bad.” Only 16 percent think it’s “mostly good.”

Sit with that for a second. The technology that every media company is rushing to implement — the thing your leadership is mandating, your vendors are pitching and your competitors are announcing — is viewed unfavorably by more than a third of the country.

When the Hard Fork hosts put that number to Google CEO Sundar Pichai this week, he didn’t spin it. He said people’s anxiety is legitimate, that humans aren’t built to process change at this pace, and that the industry has more work to do.

He’s right. But he’s also the CEO of a company that sells AI products. His problem is winning back public trust. Your problem, if you run a newsroom, is more complicated than that.

News organizations are caught in a specific bind that Pichai doesn’t have to navigate.

You are simultaneously trying to cover AI fairly — to report on job displacement, on bias, on regulatory battles, on the legitimate concerns that 35 percent of your audience holds — while also implementing the technology inside your own walls. You are asking your staff to use tools that many of them distrust, to produce work for an audience that’s increasingly skeptical of it, and to do it without losing the credibility that makes your journalism worth anything in the first place.

That is a tension most AI coverage glosses over. The trade publications write about the tools. The think pieces write about the ethics. Almost nobody writes about what it actually feels like to be a news director in 2026 trying to thread that needle every day.

I’ve been in those rooms. Here’s what I’ve seen work and what hasn’t.

The newsrooms that are getting this wrong treat AI implementation as a technology project. They bring in a vendor, run a pilot, announce the tool in a staff meeting and wonder why adoption is slow and morale is worse. They skip the conversation that actually needs to happen first: what are we afraid of, and what are we trying to protect?

The newsrooms getting it right treat it as a journalism question. What does this tool let us do that serves our audience better? What does it not touch — and why? Where is the line between AI-assisted work and AI-generated work, and how do we explain that line to our audience clearly?

The second approach takes longer up front. It involves harder conversations. But it produces something the first approach never does: staff who are invested in the outcome rather than waiting to be replaced by it.

Pichai used an example in the interview that stuck with me. He described asking an AI agent to look ahead at his calendar and color-code meetings by category. It took seconds. It probably would have taken him or an assistant 20 minutes to do manually.

That is the version of AI that most newsroom staff can accept — a tool that handles the administrative noise so they can do more actual journalism. The version they can’t accept, and won’t, is one that feels like it’s being used to justify cutting the people doing the journalism.

The distinction isn’t about the technology. It’s about the intent, and whether leadership communicates it clearly.

The 35 percent number isn’t going to fix itself. Public trust in AI will shift based on whether the technology demonstrably improves people’s lives — and whether institutions like news organizations handle it in ways their audiences can respect.

That means being transparent about where and how you’re using it. It means not hiding AI-assisted work inside a byline that implies otherwise. It means giving your staff a voice in how it gets implemented, not just a training session after the decision is already made.

Pichai said anxiety is healthy; in a democracy, you want citizens engaged and making their preferences known. That’s true. It’s also true inside your newsroom. The journalists who are worried about what AI means for their jobs — they’re not wrong to be worried. The news directors who listen to that and respond thoughtfully will build something durable. The ones who don’t will have a different set of problems in about 18 months.

My Journey to Overcoming Prostate Cancer

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Four years ago today, I went under the knife to have my prostate removed.

I had told the world about it in a blog post just days before the surgery — partly to process what I was feeling, but mostly because I wanted men to know about the PSA test that had caught what was quietly growing inside me. I had no major symptoms. I felt fine. I was not fine.

Today, I am cancer-free.

I wish I could tell you the journey from surgery to that declaration was straightforward. It wasn’t. What followed April 21, 2022 was more than a year of physical recovery, emotional reckoning, and uncertainty — and then, just when I thought I was through it, six weeks of radiation treatment.

This post is about all of that. But more than that, it’s a message to every man who hasn’t gotten a PSA test, and to everyone who loves one.

The Surgery Was Just the Beginning

There is something they don’t fully prepare you for when it comes to recovery from prostate surgery: it is cumulative. The physical toll doesn’t just come from the procedure itself — it builds. Week after week, your body is healing from something it never asked for. The disruptions to daily function, the slow return to normal, the fatigue that doesn’t announce itself but just settles in — it compounds.

And then there’s the emotional weight. A prostate cancer diagnosis — even one with a good prognosis — forces a conversation with your mortality that you can’t unhave. I had started that conversation before the surgery. I thought I’d finished it. I hadn’t.

Living with uncertainty has its own texture. You get through the surgery. You recover. And then you wait for the numbers to tell you whether it worked. For me, those numbers told a more complicated story. A year after my surgery, I began six weeks of radiation treatment.

Six weeks. Every weekday. A clinical ritual that becomes its own kind of life.

Radiation: Round Two

If surgery feels like the dramatic chapter, radiation is the long one. It is not dramatic. It is steady and relentless and, in its own way, harder to process because there is no single moment where it feels over. You simply finish. Ring the bell. And then you wait again.

I finished. I waited. And eventually, the news I had been holding space for finally arrived:

Cancer-free.

Two words that land differently than almost anything else I have ever heard. There is relief in them, yes — but also something more complicated. Gratitude. A renewed sense of purpose. And a responsibility to keep using my voice the way I have since the beginning of this journey.

Cancer-free does not mean done. Every six months, I still get tested. The PSA number that started all of this remains the number I watch most closely. That vigilance isn’t fear — it’s the same proactive mindset that caught this in the first place. I intend to keep it.

What I Want Every Man to Hear

Prostate cancer is the second most common cancer in men. It is also one of the most survivable when caught early. The PSA test is the tool that makes early detection possible.

A PSA test is a simple blood draw. It measures prostate-specific antigen in your blood. Normal levels vary by age, but a number above 4.0 typically triggers further investigation — an MRI, and potentially a biopsy. My PSA climbed from the high 3s to 4.7 over a single year. That climb set in motion a chain of events that almost certainly saved my life.

Here is what I want you to know:

I had no symptoms. I felt healthy. I was active. Nothing on the outside told me anything was wrong. Prostate cancer is often silent — it grows slowly and without announcement, until it doesn’t.

My father was a prostate cancer survivor. That family history elevated my risk. I had been watching my PSA carefully for years because of him and because I lost a dear colleague and friend, Gary Papa at WPVI, to prostate cancer in 2009. Those two realities made me vigilant. That vigilance is why I am here, writing this, four years later.

The American Cancer Society and many physicians recommend that men discuss PSA testing with their doctor starting at age 40 if they are at higher risk — that includes Black men and men with a first-degree relative who had prostate cancer. For average-risk men, the conversation typically starts at 50. But the keyword is conversation — know your baseline, understand your numbers, and don’t let anyone wave this test away.

What I Want Everyone Else to Hear

If you have a father, a husband, a brother, a son, a friend — this is for you, too.

Be the person who brings it up. Men, statistically, do not go to the doctor as often as they should. We minimize. We delay. We tell ourselves we feel fine. Sometimes the most important thing someone can do is ask: “When did you last get a PSA test?”

That question, asked by the right person at the right moment, can save a life.

And if you want to do more than ask: support prostate cancer research. Organizations like the Prostate Cancer Foundation (pcf.org) and ZERO — The End of Prostate Cancer (zerocancer.org) fund research, provide resources, and advocate for the men who need it most. A donation — any size — moves this work forward.

Four Years. And Counting.

None of this happens without the people around me.

My wife, Megan, was there through all of it — the surgery, the slow recovery, the radiation, the waiting. There are no words adequate for what that kind of presence means, so I’ll just say: I am grateful beyond measure. My four kids — Jean, Daniel, Helen, and Robert — gave me something to push toward on the days when pushing was hard. My mom, my friends, and my colleagues at ABC7 showed up in ways big and small that I will never forget. You don’t fully understand what a support system is until you need one. I needed one. I had a great one.

What I’ve learned from all of this is that surviving is not a passive act. You don’t just get through something like this and go back to who you were. You decide who you’re going to be on the other side.

I decided to be someone who keeps moving.

Last season, I set a goal to cycle 1,000 miles. I hit it. This year, I’m going further — I’m attempting a century ride, 100 miles in a single day, before the season is out. It’s the kind of goal that would have seemed abstract to me before all of this. Now it feels like a declaration.

bob monek

I’ve also kept pushing intellectually. I earned a certificate in AI and Business Strategy from Johns Hopkins because I believe that staying curious and staying current is its own form of health. The mind needs its miles, too.

Being proactive saved my life once. I intend to keep living that way.

Four years cancer-free. Grateful. Moving. Looking forward.

If this post resonated with you, please share it. Tag a man in your life who needs to read it. And visit pcf.org or zerocancer.org to learn more or support the research that makes stories like mine possible.

You can read my original post — written just before my surgery in 2022 — here.

I Just Got My AI Certificate. Here’s What I Actually Learned.

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To borrow a phrase from Howard Stern, I am something of a king of all media. Radio, television, digital — I’ve worked in all three, and I’ve had to learn the rules of each one from scratch.

It started in college, when I was fortunate to land a part-time gig as a DJ at Laser 104.1 in Allentown, Pennsylvania.  I had no idea what I was doing. I figured it out. An internship at FOX 29 in Philadelphia got me into television, which turned into a career. Eight years in, I went back to school at Temple University to earn my MBA, while working overnight shifts producing the morning news at 6abc. I wasn’t the most rested student, but I graduated.

Then came the internet. Nobody handed me a manual for that either. I self-taught: reading voraciously, experimenting constantly, and talking to anyone who knew more than I did. That’s how I’ve always operated.

So when AI started reshaping the media business — not gradually, but all at once — I decided I wasn’t going to learn it from the sidelines. I enrolled in Johns Hopkins University’s AI for Business Strategy course. This week, I received my certificate of completion.

Before anyone rolls their eyes — yes, I know what “AI certificate” sounds like. It sounds like a LinkedIn flex. It isn’t that. It’s the result of months of real coursework: essays, lectures, readings from the World Economic Forum and MIT and McKinsey, and a final project where I built a full AI proposal for a school district from the ground up. Twelve weeks. Real work.

Here’s what I actually walked away with.

The thing nobody tells you about AI

The biggest surprise wasn’t the technology. It was realizing how little most business leaders — including me — understand about what AI actually does inside an organization.

We talk about AI as if it were a feature you add. A button you push. It’s not. AI doesn’t just automate tasks. It reorganizes how work gets done. The McKinsey framing that stuck with me: companies are moving toward “minimum viable organizations” — lean structures in which AI handles structured, repeatable work, and humans focus on oversight, judgment, and context.

That changes everything.

What the course actually covered

The curriculum was broader than I expected. We started with the AI landscape — the history, the current state, who the major players are and why. Then it got practical fast: how businesses are actually deploying AI, how to optimize it, and, critically, what can go wrong.

The week on AI bias and risk was the one that hit me hardest. In journalism, we already live inside the trust crisis. Audiences can’t tell what’s real anymore. An AI that performs “slightly better than a human” at spotting misinformation isn’t good enough — that was the core of an essay I wrote for the course. The bar for AI in media has to be higher than the human baseline, because the stakes of getting it wrong are higher.

We also covered generative AI in depth — not just what it is, but how to use it responsibly for actual business purposes. And the final weeks got into scaling AI projects and managing them at the enterprise level. What does it look like when you’re not just piloting something, but running it at scale across an organization?

The final project brought it all together. I built a full vendor proposal — a fictional AI company called EduAI Solutions — pitching an AI-powered learning platform to a real school district. Every section had to hold up: the executive summary, the implementation strategy, the data privacy compliance, the cost structure. It was the most useful assignment I’ve done in any course, because it forced me to think like someone responsible for the outcome, not just someone writing about it.

What this means for journalism

I came in thinking AI was something I needed to manage in my newsroom. I left understanding it’s something I need to lead through.

Two-thirds of U.S. newsrooms have already integrated AI into at least one workflow. The roles being created — AI Ethics Editors, Automated Content Managers, Data Journalists — are no longer niche. They’re becoming core. And the journalists who thrive won’t just be good storytellers. They’ll need data literacy, an understanding of how large language models work and where they fail, and the judgment to know when to trust the machine and when to override it.

That’s a different journalist from the one I trained to be. It’s the one I’m working to become.

ABL: Always Be Learning

Here’s the thing I’ve told younger journalists for years: the moment you think you’ve figured it out, you’re done. The industry moves too fast. The audience moves too fast. You have to stay a student.

Every transition in my career has required me to start over as a learner. Radio to TV. TV to digital. The people who get left behind in this business aren’t the ones who admit they don’t know something. They’re the ones who pretend they do.

I chose Johns Hopkins specifically because the course is big-picture focused. Not “here’s how to prompt ChatGPT.” It’s about strategy — how AI changes the structure of organizations, how leaders need to think about deploying it, and what the risks look like at scale.

The next frontier I’m focused on is agentic AI — systems that don’t just answer questions but take actions, make decisions, and complete multi-step tasks on their own. That’s where this technology is heading fast, and it has enormous implications for media organizations. I’m already working to understand it.

Getting this certificate at this stage of my career wasn’t about proving something to anyone else. It was about staying useful — to my team, to my company, to myself. The executives and media leaders who will matter in the next five years aren’t the ones who handed AI questions off to someone else. They’re the ones who got in the room, got their hands dirty, and figured out what they were looking at.

I don’t have all the answers. But I know which questions to ask now. And I know where to go next.

Bob Monek is a veteran broadcast journalist and media executive who has worked in radio, television, and digital media. He completed the AI for Business Strategy certificate program at Johns Hopkins University in April 2026.

What ONA 2026 Taught Me About AI, Newsrooms, and the Leadership Gap

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I went to my first Online News Association conference four years ago, and I came back energized. Conferences can make you feel this way — like the ideas alone are enough to change something.

Then life took over. Deadlines and the pace of a daily newsroom became overwhelming. ONA became something I kept meaning to get back to.

This year I finally did. What I discovered at ONA in Chicago, not surprisingly, is the conversation has dramatically changed — from whether to use AI to something more difficult: how to use it without breaking the human systems that make journalism work.

That’s the right question. And it’s overdue.

Four years ago, AI in the newsroom was peripheral — something experimental, easy to ignore. This year, it was the center of gravity. Every room, every hallway, every lunch. Not “should we use AI” — that debate is over. The harder question is how to use it without losing what makes journalism worth doing in the first place.

From Evangelism to Pragmatism

What stood out most wasn’t any single tool or framework, though there were plenty worth bringing home. It was the tone.

There’s less evangelism now and more pragmatism. People are talking about AI the way they talk about any other production tool — what does it actually do well, where does it fall down, and who’s responsible when it gets something wrong?

That shift matters. AI isn’t the hard part anymore. Alignment is.

The 80/20 Reality

The Associated Press put a number on something many newsrooms are starting to feel: 80% of a process can be automated, but at least 20% has to remain human.

Editing. Fact-checking. Judgment.

That ratio isn’t just technical — it’s a policy position. And having something that concrete to hand to a skeptical newsroom is more useful than any demo.

The Most Useful Work Isn’t Flashy

Some of the best ideas came from smaller organizations doing unglamorous work.

City Bureau is using generative AI to synthesize civic meeting notes. Sahan Journal has built custom GPTs to create personalized media kits for sales calls — not an editorial use case, a revenue one, and it works.

And then there was El Vocero de Puerto Rico’s cautionary story about an AI agent they’d named Victor. Over time, Victor started producing bad data and sending emails nobody had asked for.

Victor got fired.

The lesson wasn’t that AI fails. It’s that you have to keep managing it long after launch — which is not how most organizations treat a tool once it’s deployed.

Culture Is the Bottleneck

The sessions on culture and leadership hit closest to home.

CNN talked about putting leaders visibly at the front of AI adoption — not as evangelists, but as practitioners. The fastest way to reduce fear in a newsroom is to show that the people above you are figuring it out too, not just mandating it from a distance.

Reuters has reorganized around cross-functional squads: editorial, product, engineering, and data science working together on specific problems instead of handing work off in sequence.

That’s not a workflow tweak. It’s a structural change. And it requires a level of trust most organizations don’t build quickly.

The Gap That’s Already Here

The tools are here. They’re getting better, quickly.

What’s lagging is the culture and leadership needed to use them well — and that’s not a technology problem. It’s a people problem. Which means it’s slower, harder, and ultimately more important than any product release.

One number from a Thomson Reuters Foundation study has stayed with me: 81% of journalists in the Global South already use AI daily or weekly. Only 13% operate under any formal newsroom policy.

That’s not a regional anomaly. It’s a preview of what happens when technology outruns leadership.

And right now, it is.

My Oscar Predictions: Confidence, Confusion, and the Inevitability of Being Wrong

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Oscar season is when I briefly convince myself that reading Variety over coffee and skimming the New York Times arts section at night qualifies as rigorous film scholarship. For a few weeks each year, I develop confident opinions about categories I forgot existed the previous spring, casually reference “industry buzz,” and begin sentences with “it feels like…” — awards-season shorthand for “I have absolutely no proof, but I am emotionally invested.”

Part of this confidence comes from experience. For roughly the past fifteen years, Oscar Sunday hasn’t meant sitting on the couch with snacks and a ballot. It has meant working — watching red-carpet arrivals in real time, producing coverage as winners are announced, and helping feed the bottomless appetite for Oscars content across ABC7NY.com and the wider ABC Owned Television Stations. After enough ceremonies spent toggling between live feeds, social-media spikes, and last-second headline rewrites, you begin to believe you understand how the night unfolds. Or at least you learn how to project calm while quietly bracing for the unexpected.

Those years also come with memories that make it difficult to treat the Oscars as a purely dignified cultural institution. There was the night the broadcast veered into live-television infamy when Will Smith walked onstage and smacked Chris Rock — a moment that turned a relatively boring ceremony into a breaking-news event heard around the world. There was the unforgettable envelope mix-up in 2017, when La La Land briefly won Best Picture before graciously, and somewhat publicly, returning the trophy to Moonlight. And of course, there was Ellen DeGeneres’s 2014 selfie — perhaps the most efficient demonstration in history of how Hollywood glamour and smartphone logistics can collide.

My cinematic worldview, however, was formed long before I was filing Oscars stories on deadline. I grew up on Disney, which probably explains my enduring faith in orchestral swells and emotional clarity. Pixar later deepened the imprint by making me cry over toys, robots, and, on one memorable occasion, real estate. My favorite film of all time remains Casablanca, a movie so elegantly constructed it makes modern plotting feel like it was completed during a lunch break. Paul Newman is still my favorite actor — effortless, intelligent, impossibly cool — though George Clooney in Michael Clayton came dangerously close to redefining what a perfectly calibrated movie performance looks like. And Katharine Hepburn remains the gold standard, proof that wit and authority never go out of style.

All of which is to say: my Oscar picks arrive with both passion and baggage.

This year I managed to see all but one of the Best Picture nominees, an accomplishment I intend to reference casually for the foreseeable future. I also saw exactly one of the short films — The Singers, which is genuinely worth seeking out and, more importantly, allows me to speak with suspicious confidence about a category where most people are bluffing.

If I’m being completely honest, none of this year’s Best Picture nominees truly floored me. I admired elements, respected the ambition, occasionally enjoyed myself — but I never quite experienced that rare electric jolt that makes you feel you’re watching something you’ll carry around for decades. Personal taste and strategic prediction don’t always align. I didn’t particularly care for Sinners, yet Michael B. Jordan’s performance feels impossible to ignore. Sometimes you simply have to respect the craft even when the movie itself leaves you unmoved. Sean Penn, meanwhile, appears to have delivered the kind of supporting performance that arrives pre-packaged with historical significance — the sort that inspires solemn nodding and decisive ballot marking.

That same film, One Battle After Another, was one of the year’s more satisfying surprises for me: ambitious without being exhausting, confident without being self-important. Watching it steadily accumulate awards-season victories has felt less like suspense and more like observing a well-organized transfer of power.

The best actress race offers a gentler internal conflict. I was genuinely charmed by Kate Hudson in Song Sung Blue, a reminder that movie-star magnetism remains both real and undervalued. But Jessie Buckley’s dominance this season has been so overwhelming that resisting her now would feel less like independent thinking and more like arguing with gravity.

And so, after months of watching, reading, overanalyzing, and filling out prediction grids with the seriousness of a tax return, I arrive at the same conclusion as much of the industry. When the final envelope is opened, the night will likely belong to One Battle After Another — a title that doubles as an apt description of awards season itself: a long, suspenseful march toward an outcome we all pretend to be surprised by.

Earned, Not Assumed: Why AI Augmentation Depends on Who Gets Access to Training

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The global labor market is undergoing a structural transformation driven in significant part by artificial intelligence. The World Economic Forum Future of Jobs Report 2025 projects that 92 million roles may be displaced globally by 2030, while 170 million new roles could emerge. However, AI’s impact is not best understood as simply job creation or destruction. From a task-based labor economics perspective, technological change operates at the level of discrete tasks rather than entire occupations. AI decomposes jobs into component activities, automating those that are routine, codifiable, and data-intensive, while recomposing remaining work around judgment, oversight, and complex problem-solving. In this sense, AI is less about eliminating work than about reorganizing how value is created within occupations—reshaping skill premiums and redefining the boundaries between human and machine contributions.

Similarly, McKinsey & Company stresses that AI is reshaping organizational design and career paths, moving beyond marginal productivity gains. They highlight the concept of “reconfiguring work,” where organizations build “minimum viable organizations”—a lean, technology-amplified operating model designed around AI-native workflows. In this model, AI handles an increasing share of structured and repeatable tasks, while human work shifts toward oversight, judgment-intensive functions, and areas requiring contextual interpretation.

Broadcast and digital journalism, a field I have navigated for over three decades, serves as an illustrative microcosm for these shifts. It demonstrates both the rapid creation of technical roles and the friction of workforce adaptation. A separate analysis from Microsoft Research examining occupational exposure to generative AI suggests that journalism-related roles involve tasks highly susceptible to automation, particularly drafting, summarization, and transcription. Rather than forecasting wholesale job elimination, the study highlights task-level vulnerability, indicating that portions of newsroom workflows may be restructured as AI systems assume responsibility for routine content production.

Emerging AI Roles in Journalism

Recent studies show that over 65% of U.S. newsrooms have integrated AI, experimenting with or deploying AI tools in at least one workflow, driving the creation of highly specific roles. Digital platforms leverage algorithms for personalization and fact-checking, creating a critical need for AI Ethics Editors to ensure integrity. Broadcasters utilizing AI for transcription and translation are hiring Automated Content Managers to handle these dynamic workflows. Meanwhile, the ability to scan massive datasets for market trends has elevated the Data Journalist from a niche specialty to a core newsroom necessity.

Evolving Demands for AI Skills

Journalists now require technical fluency—prompt engineering, data literacy, and understanding LLM constraints—alongside traditional skills. As AI automates copy generation, uniquely human skills command a premium. Empathy in interviewing, high-level investigative intuition, and ethical decision-making are becoming more vital than ever.

Research by Northwestern professor Nick Diakopoulos highlights this shift. While editorial job postings declined significantly post-ChatGPT (from 28,566 to 18,156), listings requiring AI skills tripled. While the timing coincides with the emergence of generative AI, the contraction in editorial roles likely reflects a confluence of technological, economic, and platform-market forces rather than direct substitution alone. Still, the growing listing of AI roles reveals four emerging roles: “AI-doers” (building tools), “AI-users” (applying tools), “AI-strategizers” (planning), and “AI-reporters” (covering AI). Crucially, demand also surged for human capabilities like ethics, critical thinking, and fact-checking—skills that directly complement AI’s weaknesses.

The Challenges of Upskilling and Reskilling

Despite the clear need for AI fluency, the media industry faces significant structural hurdles in reskilling a workforce already stretched thin by the 24-hour news cycle:

  • The Hidden Costs of Training: The financial burden extends beyond software licensing to implementation time, workflow redesign, and productivity disruption. Many newsrooms accumulate what can be described as “integration debt”—the deferred organizational costs that arise when AI systems are layered onto legacy processes without structural redesign. When tools are adopted faster than governance standards, editorial protocols, and workforce capabilities evolve, inefficiencies compound, requiring future investments in retraining, oversight, and workflow correction.
  • The New Digital Divide: A two-speed transformation is emerging. Large national networks possess the capital to build proprietary AI infrastructure and dedicated oversight teams, while under-resourced local newsrooms lack comparable investment capacity. This widening “AI divide” risks exacerbating existing inequalities in reporting depth, investigative capacity, and technological resilience across the industry.
  • Union Resistance: Automation anxiety is increasingly manifesting in labor negotiations. Organizations such as The NewsGuild and the Writers Guild of America have pushed for contractual guardrails governing AI deployment, seeking transparency, attribution protections, and limits on automation. While these efforts aim to protect workers, negotiations can slow implementation timelines as management and labor debate control, accountability, and long-term job security.
  • The Curriculum–Skill Gap: AI capabilities are evolving more rapidly than formal training programs. While academic institutions often emphasize ethical frameworks and media theory, employers increasingly seek operational competencies such as prompt design, model evaluation, and workflow integration. This misalignment leaves mid-career professionals navigating a fragmented retraining landscape without standardized pathways.

Conclusion

The same newsrooms introducing AI ethics editors and data journalists are struggling to train the veteran reporters and producers sitting beside them. The dominant pattern currently appears to be augmentation combined with role hybridization, but optimism must be earned, not assumed. The tripling of AI skill requirements signals opportunity, but journalists who fail to upskill risk being quietly filtered out of the hiring market. Augmentation is only a win for those with access to training.

AI integration represents not incremental optimization but an active restructuring of value creation. Newsrooms cannot rely solely on external hiring to fill emerging skill gaps; as McKinsey & Company notes, recruitment alone is neither cost-efficient nor strategically sustainable. Instead, organizations must invest in disciplined strategic workforce planning while cultivating superagency—an organizational condition in which journalists possess both the technical literacy and institutional authority to actively direct, interrogate, and refine AI systems within their domains. By strengthening human-in-the-loop capabilities such as ethical reasoning, investigative judgment, and contextual analysis, newsrooms can ensure that AI enhances journalistic rigor rather than eroding it.

References

Corden, Jez. “Microsoft reveals 40 jobs about to be destroyed by (and safe from) AI.” Windows Central. https://www.windowscentral.com/artificial-intelligence/microsoft-reveals-40-jobs-about-to-be-destroyed-by-and-safe-from-ai

Diakopoulos, Nick. “The Impact of Generative AI on Journalistic Labor.” Generative AI in the Newsroom. https://generative-ai-newsroom.com/the-impact-of-generative-ai-on-journalistic-labor-e87a6c333245

Fu, Angela. “As AI enters newsrooms, unions push for worker protections.” Poynter. https://www.poynter.org/business-work/2023/artificial-intelligence-writers-guild-unions-journalism-jobs/

McKinsey & Company. “The critical role of strategic workforce planning in the age of AI.” McKinsey People & Organizational Performance Practice, February 2025.

McKinsey & Company. “Generative AI and the future of work in America.” McKinsey Website. https://www.mckinsey.com/mgi/our-research/generative-ai-and-the-future-of-work-in-america

Research.com. “2026 AI, Automation, and the Future of Journalism Degree Careers.” Research.com. https://research.com/advice/ai-automation-and-the-future-of-journalism-degree-careers

World Economic Forum. “Future of Jobs Report 2025.” World Economic Forum Website. https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf

AI Ethics in Journalism: Beyond Human Baseline

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The “human baseline” approach posits that the ethical success of artificial intelligence is achieved when its decision-making mirrors or marginally improves upon that of a competent human.  In the classic “trolley problem,” this implies that if an AI can consistently choose the “lesser of two evils” with more precision than a panicked human, it has cleared the ethical bar.

However, as the media and journalism industry increasingly integrates generative AI and automated editorial systems, it is becoming clear that a “slightly better than human” standard is insufficient. In the context of information dissemination, a human-level baseline for AI is not a gold standard; it is a liability.

While comparing AI to the human baseline in moral dilemmas reveals the machine’s capacity for consistency, it fails to account for the unique accountability required in journalism.  

Because audiences in 2026 are caught in a “breaking verification” crisis where trust is the ultimate currency, an AI that is merely “slightly better” than a biased human is ethically insufficient. To be truly ethical, AI in media must move beyond mimicking human choice to provide a level of transparency and evidentiary rigor that transcends a journalist’s capability.

Our newsrooms are facing a speed-versus-verification dilemma.   The human baseline for a journalist is breaking the story vs. being 100% accurate.   AI’s logic is fundamentally different.   AI shifts control from individual journalists to automated systems optimized for engagement and scalability.   Therefore, an AI that performs ‘slightly better’ than a journalist at producing content quickly may be ethically inferior if its underlying logic lacks the transparency and evidentiary rigor that defines journalistic integrity.

Because so much information is published in many ways across many platforms, audiences are having a difficult time distinguishing fact from fiction. 

“‘Breaking verification’ will replace ‘breaking news’ in 2026, and trust will decide who survives,” according to Vinay Sarawagi, co-founder and CEO of The Media GCC.

Audiences need to see evidence and sources to back up what they see online, because seeing is no longer believing.   If AI only does as well as humans at spotting fakes, it’s not enough. To solve the trust crisis, the AI must be exponentially better at citing sources.

In 2005, Wallach and Allen argued that the principal goal of the discipline of artificial morality is to design artificial agents to act as if they are moral agents. They distinguish between operational morality, in which an AI simply follows pre-programmed human safety rules, and functional morality, in which a system can independently navigate moral dilemmas.  In journalism, an AI that merely mirrors an editor’s baseline choices is operating within a limited framework.   If the media is to serve the public’s best interests, a journalist AI must move toward a functional morality that transcends basic human instinct and provides the transparency and accountability the public expects.

From a strategic standpoint, “slightly better” is a recipe for disaster.   If AI-generated content results in a libel suit or negatively impacts a company’s stock price, the defense that AI is slightly more accurate than an average human is a losing argument.  As the media shifts into what is being termed the ‘Answer Economy’, the traditional value proposition of a newsroom is being disrupted. When AI models synthesize reports into a single summary, the value of a news organization is no longer just the ‘answer’ or the scoop itself, but the auditable trail of evidence that allows that answer to be verified (Seo Ai Club, 2026). If an AI only meets the human baseline for producing a plausible-sounding summary without providing this rigorous, machine-readable proof of its sources, it fails to meet the ethical demands of a 2026 audience.

Note: This is an essay originally written for a course on AI and business strategy at Johns Hopkins University.

References

Wallach, Wendell and Allen, Colin. “Artificial Morality: Top-down, Bottom-up, and Hybrid Approaches.” Ethics and Information Technology volume 7, no. issue 3 (September 2005): 149-155. https://link.springer.com/article/10.1007/s10676-006-0004-4.

Li, Haoran et al. “Artificial Intelligence and Journalistic Ethics: A Comparative Analysis.” Journal of Journalism and Media volume 6, no. issue 3 (August 2025): 105. https://www.mdpi.com/2673-5172/6/3/105.

Mee, S. et al. “Moral judgments of human vs. AI agents in moral dilemmas.” Scientific Reports volume 13, no. issue 1 (February 2023). https://pmc.ncbi.nlm.nih.gov/articles/PMC9951994/.

Simon, Felix. “How AI reshapes editorial authority in journalism.” Digital Content Next (June 2025). 

Reuters Institute. “How will AI reshape the news in 2026? Forecasts by 17 experts around the world.” Reuters Institute for the Study of Journalism (January 2025). 

Seo Ai Club. “The Answer Economy: A Comprehensive Analysis of Answer Engine Optimization Tracking Software and Strategic Market Leadership.” Seo Ai Club (January 2025). 

Character vs Reputation: The True Measure of Success

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I recently listened to an episode of Freakonomics Radio titled “If You’re Not Cheating, You’re Not Trying,” featuring an interview with disgraced cyclist Floyd Landis. The conversation eventually turned to John Wooden’s famous maxim: “Be more concerned with your character than your reputation, because your character is what you really are, while your reputation is merely what others think you are.”

Landis, perhaps unsurprisingly for a man whose career was defined by a massive deception, rejected Wooden’s idealism. He argued that in the “real world,” reputation is the only thing that functions. It’s the currency that buys you the contract, the sponsorship, and the adoration. To Landis, character is just a consolation prize you cling to once your reputation has been torched.

I understand his cynicism. But I fundamentally disagree with it.

Landis views reputation and character as two separate assets you can trade, like stocks. But Wooden’s point was deeper: Reputation is merely the shadow cast by character. You can manipulate the shadow for a while – stand in the right light, distort the angle, make yourself look larger than you are – but eventually, the sun moves. The shadow always snaps back to the reality of the object casting it.

In my media career, I’ve seen this physics play out repeatedly. We live in an industry obsessed with the “shadow” – the ratings, the viral potential, the race to be first. I’m certainly not perfect; I’ve made mistakes in my career. But I’ve learned that the “reputation” of a news organization isn’t built on its speed; it’s built on its credibility. It’s built on the boring, invisible machinery of character: fact-checking, sourcing, and the refusal to cut corners when no one is watching.

A journalist can fake their way to a scoop once. They can build a reputation for being “first.” But if that reputation isn’t grounded in the character trait of accuracy, the fall is inevitable. When the correction comes – and it always does – the reputation doesn’t just dip; it evaporates.

Consider the case of Janet Cooke, a Washington Post writer whose heartbreaking profile of an 8-year-old heroin addict won a Pulitzer Prize. The unraveling of her reputation began, ironically, with a celebration of it.

Her former employer, the Toledo Blade, initially rushed to publish a tribute to their former staffer. But the tone shifted when editors compared the Associated Press biography—based on Cooke’s own resume—against their internal personnel files. While Cooke claimed to be a magna cum laude Vassar graduate with a master’s degree, the Blade’s records told the truth: she had only attended Vassar for a year and held a standard bachelor’s degree. Because the character didn’t match the reputation, the entire structure collapsed. Her prize-winning article, “Jimmy’s World,” was exposed as a complete lie, and the Pulitzer was returned.

I’m seeing a similar tension now as I study the business strategy and ethics of Artificial Intelligence. The temptation in the AI space is to let the “reputation” of the technology—the hype, the valuation, the promise of an AI future—outpace the “character” of the build (safety, bias, alignment).

Landis would argue that we should ride the hype wave because “that’s how the world treats you.” But history suggests that tech bubbles built on reputation without underlying substance always burst. The companies that last are the ones where the internal reality matches the external promise.

Warren Buffett famously said, “It takes 20 years to build a reputation and five minutes to ruin it.” Unlike Landis, Buffett doesn’t see reputation as a mask to wear; he sees it as a fragile byproduct of integrity.

Bob Iger, the retiring CEO of Disney, reinforces this in his memoir, The Ride of a Lifetime: “True authority and true leadership come from knowing who you are and not pretending to be anything else.” For Iger, character and decency are not merely “soft skills,” but strategic advantages that define a company’s success.

Floyd Landis believes he was punished for playing the game. I would argue he was punished for mistaking the shadow for the man. Ultimately, the spotlight always falls on a person’s character, not their reputation.