AI Capability Benchmarks: What Marketers Need to Know

Your vendor just told you their AI is approaching AGI-level performance. Your board wants to know if that means anything. And most marketing teams have no framework for answering either question. In this Research Radar Brief, Dr. Eva Wolf reviews 3 recent AI capability papers — none of which study marketing directly — that together provide a rigorous vocabulary for evaluating AI tools, cutting through benchmark hype, and deciding how much autonomy to give AI in your workflows. A note on this episode: all three papers were screened for direct AI + marketing relevance and scored below the show's standard threshold. They are covered here because the script excerpt and pipeline context indicate Dr. Eva Wolf made an editorial decision to include them for their practical vendor-evaluation value. The findings are presented as the original authors intended — as frameworks and theoretical proposals, not empirical marketing evidence. What you'll learn: - Why task-specific AI benchmark scores can be misleading when evaluating tools for marketing work - How a tiered AGI capability model (Emerging to Superhuman) can help you parse vendor claims more precisely - Why the amount of human oversight you apply to an AI system is a separate choice from how capable that system is - How to use a 10-faculty cognitive breakdown to scope what an AI tool can and cannot reliably do - Why training-data volume can inflate benchmark scores without reflecting real adaptability Papers covered: 1. Levels of AGI for Operationalizing Progress on the Path to AGI Source: Conference paper — International Conference on Machine Learning (ICML 2024), likely peer-reviewed Access: Full text reviewed Authors: Morris, Sohl-Dickstein, Fiedel, Warkentin, Dafoe, Faust, Farabet, Legg (Google DeepMind) Link: https://arxiv.org/abs/2311.02462 2. On the Measure of Intelligence Source: Preprint — arXiv (2019). Not peer-reviewed. Access: Full text reviewed Authors: Francois Chollet Link: https://arxiv.org/abs/1911.01547 3. Measuring Progress Toward AGI: A Cognitive Framework Source: Preprint — arXiv (2026), Google DeepMind. Not peer-reviewed. Access: Full text reviewed Authors: Burnell et al. Link: https://arxiv.org/abs/2605.28405 Full show notes, transcript, and citations: https://bigplans.media/episodes/ai-capability-benchmarks-agi-frameworks-marketers-2026-09-22 Disclaimer: This is a first-pass research briefing produced by an AI-generated avatar trained on Dr. Eva Wolf's research framework. It is not a substitute for reading the full papers. Preprints have not undergone peer review and findings may change. Papers 1 and 3 are authored by Google DeepMind researchers; potential organizational perspective or incentive biases are noted but not fully addressed in the available texts. Always read the original research before making business decisions. -- This is a first-pass research briefing, not a final academic review. Read the original papers before making major marketing or business decisions. AI & Marketing Research Radar is produced by BigPlans Media. Subscribe wherever you listen to podcasts.

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I’m Dr. Eva Wolf, and I help marketers, educators, consultants, and business owners turn AI marketing research into practical strategy, smarter workflows, and real business opportunities.

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You're listening to Evita, an AI-generated research briefing avatar trained on the research framework and methodology of Dr. Eva Wolf, marketing professor, AI researcher, and founder of Big Plans Media. Every day, Evita scans emerging research in AI, marketing, consumer behavior, psychographics, and business strategy to identify the most relevant developments, opportunities, and risks worth watching. These daily radar reports are designed to help busy professionals stay informed without having to read hundreds of research papers themselves. And every Friday, join Dr. Eva Wolfe Live for her personally recorded weekly AI marketing radar roundup, where she breaks down the biggest stories, explains what actually matters, and shares practical insights and strategic implications for marketers, educators, entrepreneurs, and business leaders. Now, here's today's radar report. Here's the signal I can't ignore today. Your vendor just told you their AI is approaching AGI level performance. Your board wants to know if that means anything, and you have no framework for answering either of them. That's the actual problem today's papers solve. None of these papers studied marketing. I want to be upfront about that. No ads, no consumers, no campaign outcomes. What they studied is harder to face. What AI can actually do, how we measure it, and why most benchmarks are lying to you. And I'm covering them because every one of these papers gives you a filter, a real one, for the AI pitches landing in your inbox right now. We screened today's payload. Three papers cleared the full text bar and made the radar. Quick caveat. This is a first pass research briefing, not a final academic review. I'll tell you what the papers suggest, what they don't prove, and which ones deserve a deeper read. Okay, let's get into it. Paper one. Here's the business question. When your vendor says their AI is superhuman, what does that actually mean? And should you care? Because right now, superhuman AI and general AI are being used interchangeably in sales decks. They are not the same thing, not even close. Morris and colleagues built a five-level taxonomy for classifying AI systems, from narrow task performance all the way up to what they call artificial general intelligence. The framework is designed to give researchers and frankly anyone evaluating AI systems a consistent vocabulary, so we stop conflating very different capability claims. Here's what that means in plain English. An AI can be superhuman at one task, beat every human at chess, ace a coding benchmark, and still be level two on a five-level scale. It's not AGI. It's a very good narrow tool. That distinction matters enormously when someone is selling you an enterprise platform. The catch? This is a conceptual taxonomy, not an empirical study. It doesn't test AI systems against the framework. It builds the map, but it doesn't place anything on it yet. That's the part I keep coming back to. The framework is genuinely useful, but right now the vendors will use it however they want to, because there's no enforcement mechanism. The taxonomy exists, the audit process doesn't. Plain English payoff. Superhuman and general are not the same level, and any vendor conflating them is either confused or selling you something. Okay, here's where this becomes commercially interesting. Money move. Build a one-page vendor evaluation rubric using Morris et al. Before any AI procurement meeting, ask the vendor to place their product on the scale and justify it. The teams that can answer that question clearly are worth talking to. Action step. Pull your last three AI vendor decks. Find every claim that uses the word intelligent, advanced, or superhuman. Flag each one. Ask your team, which level on a five-point capability scale does this actually describe? Evidence check. This paper is a conceptual framework, not empirical research. No systems were tested. It's a vocabulary tool, not a verdict. Radar Verdict, Watch List. The framework is solid and genuinely useful, but it needs an empirical layer before it becomes a procurement standard. This next one looks like it's purely about cognitive science. Stay with me, the payoff is practical. Paper 2. Here's the business question. The AI tool your team just bought scored 95% on its benchmark. Does that tell you anything useful about what it'll actually do in the real world? Chalet's answer is a hard no. This paper, and it's a foundational one, widely cited in AI research, argues that almost every standard AI benchmark measures skill acquisition, not intelligence. The distinction matters. A system trained on enough examples of a task will ace the benchmark. That doesn't mean it can reason about novel problems. It means it memorize patterns that look like reasoning. Cholet proposes a different standard, the ARC benchmark, designed to require genuine generalization from minimal examples. The premise is that if you want to know whether a system can think, you test it on problems it has never seen, not problems it was implicitly trained to recognize. Here's why that's expensive for you. Every AI platform you evaluate will show you benchmark scores. Those scores are almost certainly measuring exposure, not capability. That is not a technical footnote. That is a procurement risk. The catch. Cholet's framework is also contested. Other researchers argue that his definition of intelligence is too narrow and that the ARC benchmark has its own blind spots. This is an active debate, not a settled verdict. Hmm, and honestly, that's actually the point. The fact that experts are still arguing about what intelligence means while vendors are telling you their product has it, that's what should bother you. Plain English payoff. A high benchmark score tells you an AI practiced that test. It does not tell you the AI can handle your actual use case. Here's the business hiding inside the research. Money move. Replace what's your benchmark score with show me performance on a task we give you with data you haven't seen. That's not a harder ask. It's the right ask. Agencies and consultants who build this kind of capability audit into their AI evaluation process are going to be worth a lot more to clients this year. Action step. Before your next AI tool demo, prepare one novel task. Something specific to your business that the vendor couldn't have trained on. Watch what happens. That's your real benchmark. Evidence check. Cholet's paper is influential but not universally accepted. The ARC benchmark itself has been critiqued. Use this as a thinking frame, not a final standard. Radar verdict. This one needs the full paper. The argument is layered, the debate is live, and the implications for how you evaluate any AI tool are significant. Okay, last one, I almost put this in the show notes instead of the brief. I'm glad I didn't. Paper three. Here's the business question. If AI systems have hidden capabilities that even their developers haven't unlocked yet, what does that mean for the tools you're deploying right now? This is the DeepMind preprint on capability elicitation. The core finding is this. AI systems often have latent capabilities, things they can do that don't show up under standard evaluation. The method of prompting, the framing of the task, the context provided, all of it affects what the system can actually do. Which means benchmark evaluations may be systematically underestimating or in some cases misrepresenting what these models are capable of. Let me translate that into business terms. The AI tool you tested in procurement may behave very differently once your team is prompting it in production. Not because the tool changed, because the inputs changed. That is not a technical edge case. That is an operational risk. The catch? This is a preprint. It hasn't cleared peer review. The framing around hidden capabilities is also contested. Some researchers argue this overstates unpredictability in well-scoped systems. Here's what bothers me about this one. We're deploying these tools at scale in customer-facing contexts, and we're doing it based on evaluations that may not reflect how the system behaves once real humans are prompting it in real conditions. That gap is real whether or not the preprint survives peer review. Plain English payoff. The AI you tested in demo conditions is not necessarily the AI your team is running in production, and the gap between those two things is worth auditing. Okay, here's where this becomes commercially interesting. Money move. Offer a post-deployment capability audit, a structured review of how a client's team is actually prompting their AI tools versus how those tools were evaluated at procurement. That gap is almost always there. Charging to close it is a legitimate service. Action step. Pick one AI tool your team uses daily. Spend 30 minutes documenting the prompts that work best versus the prompts that fail. You're building your own elicitation map. That's more useful than any vendor benchmark. Evidence check. Preprint, not yet peer-reviewed. The core observations are plausible and grounded in real evaluation work, but treat the stronger claims with caution until this clears review. Radar verdict. At first glance, these papers look separate, but together they show something that should change how every marketing team talks about AI internally. The pattern is this. That advantage is still available. Most teams aren't asking those questions yet. That's the piece I care about. Here's the playbook from today. One, pull your vendor decks. Find every capability claim. Ask which level on a five-point scale it describes. If they can't answer, that's your answer. 2. Before your next AI demo, bring a novel task, something vendor specific to your business that couldn't have been in any training set. Watch what the tool actually does. 3. Document the gap between how your team prompts your current AI tools and how those tools were evaluated at procurement. That gap is almost certainly there. Closing it is free. Ignoring it is expensive. Evidence check on all of that? One of today's papers is a preprint, one is a contested but influential theoretical framework, and one is a conceptual taxonomy without empirical validation. Use them to sharpen your thinking and tighten your questions, not as final verdicts. I'm telling you, the teams that build an AI evaluation practice this quarter will have a durable advantage. Not because the research is settled, because everyone else is skipping this step entirely. Links to all three papers are in the show notes. Read the originals before making major decisions. Want the human expert take? Join Dr. Eva Wolf every Friday for the AI Marketing Radar Roundup, where she extracts no-nonsense, money-making tips, practical strategy, and real business opportunities from the week's research. Subscribe on Apple Podcasts, Spotify, YouTube, and wherever you listen to podcasts. This is Evita for Big Plans Media, and I'll be back in the next radar brief.