Paul Stollery, Hard Numbers

Beneath the Surface

About the show

Beneath the Surface is a podcast about generative engine optimisation, AI search and reputation in the age of the algorithm. We’ve grown used to algorithms deciding what we see. Since Google and the days of the eight blue links, information has been filtered, ranked and prioritised for us. But there was still a sense of where it came from. You could scroll. You could check the sources. You could look beneath the surface. But that’s changed. This series explores what’s happening underneath AI-generated answers: what is shaping them, who is shaping them, and why. From misinformation and disinformation to corporate influence, reputation risk and the hidden sources behind tools like ChatGPT, Gemini and Claude, each episode looks at how generative search is changing visibility, trust and truth online. For marketers, communicators and decision-makers, Beneath the Surface helps make sense of the new information landscape – and how to shape reputation in the age of the algorithm.

Newest episodes

This smear campaign would work even better on ChatGPT | Beneath the Surface Ep.3

Oct 19, 2026 38 min

A competitor paid for a smear campaign against Lenovo. It reached Forbes and Congress. Today, the same disinformation would shape what ChatGPT says. Charlotte West, Head of Global Corporate Communications at Lenovo, explains how a niche website called China Tech Threat spent years publishing a disproportionate number of articles about Lenovo. The site was run by two people with telco backgrounds. Its articles were edited and republished on Forbes through the contributing editors platform, where Charlotte says there was no editorial scrutiny, and the woman fronting the site was being called on as an expert in Washington, testifying at hearings and helping shape bills. Lenovo went to Forbes directly to make its case and was shrugged off. Then a Bloomberg investigation found that Dell had been funding a Washington public affairs firm, DCI, which was using China Tech Threat as a mouthpiece. Bloomberg also found Micron using the same firm and the same outlet, focused on semiconductors. Lenovo's own check of the website's metadata showed that DCI employees were authoring the pieces. After the Bloomberg story, Forbes removed the articles. Charlotte's lesson for reputation management: correct, correct, correct. Don't let it go. Then the AI part. Paul walks through an Ahrefs study of nearly 10 million ChatGPT queries that lists the 100 most cited websites. Reddit is number one, Wikipedia takes three of the top four, then Amazon, TechRadar, The Sun, Times of India, and at number 10, Forbes. Wired, The Guardian and Business Insider also make the top 20. If the China Tech Threat campaign happened today, it would not just be shaping conversations in Washington. It would be shaping what ChatGPT says about Lenovo. Shayoni Lynn, founder of Lynn, a behavioural science communications advisory that has been countering misinformation since 2021, explains the difference between misinformation, disinformation and malinformation, why 2016 was the platform shock that turned disinformation...

ChatGPT cites Russian propaganda. It can also swing elections | Beneath the Surface Ep.2

Jul 13, 2026 31 min

Ask ChatGPT about the war in Ukraine, and there's a one-in-five chance it answers with Russian propaganda. This episode is about how that happens – and why it matters far beyond Ukraine. Julia Petryk runs Calibrated, a PR agency in Kyiv, and has lived and worked in Ukraine since the start of the war: sleepless nights under Russian missiles and drones, blackouts so total she dug a reflective scarf out of her wardrobe just to cross the road safely, a winter at minus fifteen with no heating after Russia hit her neighbourhood's plant. She knows better than almost anyone that Russia's war isn't just fought with missiles – it's fought with information. Julia describes what that looks like from the inside: AI as an accelerator of propaganda, the Doppelganger campaign that cloned real news websites to pump out fake stories, and an uneven fight where Russia spends billions on disinformation while Europe spends millions to counter it. And that propaganda is turning up somewhere you might not expect: inside your chatbot. Research from the Institute for Strategic Dialogue found that when you ask ChatGPT, DeepSeek and Grok about the war, almost a fifth of responses cite Russian state-attributed sources. We break down – in plain language – how chatbots actually source their answers, the difference between the base model and a live search, and why "garbage in, garbage out" means Vladimir Putin has basically ended up doing GEO. Then the question that really matters: can a chatbot actually change your mind – even about how you vote? We speak to Hause Lin, a research scientist at MIT who ran one of the largest studies of its kind: around 2,400 US voters, each having a roughly six-minute conversation with a chatbot arguing for Trump or Harris right before the 2024 election. The result was a 2.88 percentage-point shift – one in 27 people switched their vote, several times more effective than the political advertising campaigns spend billions on. In Canada and Poland the effect was five...

Is ChatGPT lying about you? | Beneath the Surface Ep.1

Apr 21, 2026 37 min

Why do LLMs 'lie'? And what can you do about it? ChatGPT told the world that Brian Hood was a convicted criminal. He wasn't. He was the whistleblower. Brian Hood spent years trying to expose a bribery scandal at a subsidiary of Australia's Reserve Bank. He raised it with the board. He raised it with the deputy governor. He was made redundant and escorted to the front gate. When prosecutions finally came, eleven people were found guilty. Brian was the key witness. Then, in 2023, ChatGPT wrote his story – and got the ending completely wrong. It said he'd been charged, found guilty, and sentenced to 30 months in jail. Brian nearly became the first person in the world to sue OpenAI for defamation. In this episode, he tells us what happened next. But Brian's story is really a window into a much bigger question: how do large language models actually decide what to say? These platforms don't retrieve facts the way a search engine does. They predict what sounds right – and when the pattern fits but the fact doesn't, you get confident fiction. We break down the mechanics in plain language: what tokens are, how pattern matching works, why a system trained on the entire internet can still fabricate a criminal conviction, and what the term "hallucination" actually gets wrong. Then we go deeper. If these systems can get things wrong by accident, what happens when people try to make them get things wrong on purpose? We speak to a senior figure from GCHQ's National Cyber Security Centre about the state-backed groups and organised criminals actively trying to poison what AI platforms tell you. He explains how little data it takes to contaminate a model's training, why the long tail problem from SEO is even more dangerous in the age of generative AI, and what the emerging discipline of defence actually looks like. If you work in communications, reputation, or GEO – generative engine optimisation – this is the foundation. You can't influence what these platforms say about your bran...