Top AEO Experts in 2026 (24 People Actually Moving Answer Engine Optimization)

The top AEO experts in 2026 are the people who study whether an answer engine names your brand as the answer, not just whether it cites a page below it. Answer Engine Optimization has its own research questions: answer share versus citation share, how a prompt gets decomposed, and which source becomes the spoken answer in ChatGPT, Perplexity, Gemini, Claude, Copilot, and Google AI Overviews and AI Mode.

This is my list of the best AEO experts and top answer engine optimization researchers to follow in 2026, ranked by original research output, not follower count. My top tier is Metehan Yesilyurt, Malte Landwehr, Tomek Rudzki, and David Konitzny, who study how answer engines decide who gets named. The full list is 24 experts across research, enterprise strategy, tooling, and content, including Lily Ray, Marie Haynes, Mark Williams-Cook, Crystal Carter, Mike King, Dan Petrovic, and Cindy Krum. Almost all publish on LinkedIn, with X as the secondary channel, and every profile links to both so you can verify the work yourself.

TL;DR

The top AEO experts in 2026 share one trait: they measure whether a brand is named as the answer, not just cited. Original experiments, retrieval-level analysis, and exposure of how assistants assemble responses.

The top tier: Metehan Yesilyurt, Malte Landwehr, Tomek Rudzki, and David Konitzny. All four reverse-engineer how answer engines pick the answer.

The full list covers 24 experts across research, enterprise strategy, tooling, and content: Lily Ray, Marie Haynes, Mark Williams-Cook, Crystal Carter, Mike King, Dan Petrovic, and more below.

Follow them where they publish: most are active on LinkedIn, and the ones who run newsletters are the fastest way to keep up with answer engine changes.

If you want to act on their research, pair it with a tracking layer. See my best AEO tools guide.

What makes someone a real AEO expert in 2026

The bar moved. In 2023 it was enough to explain what ChatGPT is. In 2026, a real expert in Answer Engine Optimization does at least three of these:

Original research. They run their own experiments on ChatGPT, Perplexity, Gemini, Google AI Mode, and AI Overviews, and publish the raw findings rather than summarizing someone else's thread.

Answer-level focus. They separate being named as the answer from being cited as a source, because the two are different wins and only one of them is AEO.

Scale. Their analyses cover millions of prompts, answers, or fan-out queries, because single-run anecdotes mislead in a non-deterministic system.

Shipped impact. Their work changes products, tools, or how teams write for answer engines, not just impressions.

Honesty about limits. They say what they do not know. Answers change run to run, and anyone selling certainty is selling something else.

Every expert here clears at least three of those five.

Top AEO experts in 2026 at a glance

The top tier: researchers reverse-engineering the answers

1. Metehan Yesilyurt

Role: AI search researcher at Peec AI, studying when an answer engine names a brand versus only citing it.

My angle is the answer itself: whether an engine speaks your brand name inside its response, or only links the source underneath it. I publish experiments on how assistants choose what to say, how often the same prompt returns a different answer, and why a single test tells you almost nothing. That work feeds Peec AI, where the team ships those findings as software thousands of brands use to check whether they are the answer or a footnote.

What to steal from my approach: track answer share and citation share as two different numbers. Being cited is not the same as being the answer, and most dashboards blur them into one score.

Honest limit: I am one person publishing fast in a field that changes weekly. Some answer patterns age quickly, and I revise when the engines change. That is the nature of the work.

Follow: LinkedIn · X @metehan777 · metehan.ai

2. Malte Landwehr

Role: CPO and CMO at Peec AI, formerly VP of SEO at idealo. Runs product for an answer tracking platform.

Malte joined Peec AI from a VP role at idealo, where he ran a 30-plus person business unit, and bet on answer visibility before most buyers had a name for it. Peec went from $500K to $5M ARR and raised over $21M in his first year running product and marketing. His instinct is to treat the answer layer as something you can measure and forecast rather than guess at.

The prompt panel method is the part of his work to copy for AEO: fix a set of buying questions, ask them on a schedule, and read the delta instead of a screenshot. He also writes tool reviews on Medium that stay noticeably clear of the usual vendor spin.

What to steal from his approach: build a fixed panel of buying questions and read the delta. For AEO specifically, ask the questions a buyer would ask an assistant, not the keywords you would type into Google.

Honest limit: Malte runs product and marketing at a vendor in this space, so read his answer tracking comparisons with that context. He discloses it, which is more than most do.

Follow: LinkedIn · X @MalteLandwehr · maltelandwehr.de

3. Tomek Rudzki

Role: GEO lead at Peec AI and co-founder of ZipTie.dev. Author of AI Survival for SEO.

Tomek ran one of the largest answer retrieval studies published anywhere: 5 million query fan-outs across ChatGPT, Perplexity, and Grok, mapping what an assistant searches for when a buyer asks one question. That is the layer most AEO advice skips, and it is why his explanations of why a brand gets left out actually hold up.

He co-founded ZipTie.dev, one of the first platforms to monitor AI answers, and now runs GEO at Peec AI, where the job is to get an answer tracking company named in answers. The findings get published either way, including the ones that did not work.

What to steal from his approach: the prompt a buyer types is not what the engine searches. Answer engines fan out into sub-queries, so optimize for the fan-out and you show up in the assembled answer.

Honest limit: like Malte, he now works at a vendor, and his deepest answer data comes from that position. He balances it by publishing methodology openly.

Follow: LinkedIn · X @TomekRudzki · tomekseo.com

4. David Konitzny

Role: AI Search and SEO Strategy Lead at Kosch Klink Performance, reverse-engineering how assistants choose answers.

David treats the assistant as a box he can prise open. He has spent hundreds of hours taking apart how ChatGPT search behaves, reading raw network responses instead of theorizing, and his find of the metadata.search_model_queries array in the GPT-5 response laid bare the exact sub-queries the model runs. Answer optimization work across the industry was rebuilt on top of it.

He keeps documenting Bing grounding, Copilot ad slots, and ChatGPT's ad plumbing as they land, usually before anyone else spots them. His position is unfashionably plain: this is AI search, and the retrieval layer under an answer still runs on the SEO fundamentals most teams were ready to abandon.

What to steal from his approach: open the network tab before you write. You can see the exact queries an answer engine runs, which is a far better brief than a guess about what a buyer meant.

Honest limit: much of his published work focuses on ChatGPT and the Microsoft stack, so pair it with multi-engine answer research for the full picture.

Follow: LinkedIn · X @DavidKonitzny

The researchers and strategists shaping how teams work

5. Lily Ray

Role: VP of SEO and AI Search at Amsive, founder of Algorythmic. Tests what earns an answer in AI Overviews.

Lily is the person most agencies cite when a client asks what actually decides an AI Overview answer. She runs SEO and AI Search at Amsive and founded Algorythmic, and her work on E-E-A-T and Google's quality systems is the reason serious answer optimization programs start with trust signals instead of tricks.

What to steal from her approach: quality signals that earned trust in classic search still gate whether an engine repeats your claim as an answer. AEO without E-E-A-T is a trick, not a strategy.

Follow: LinkedIn · X @lilyraynyc · lilyray.nyc

6. Marie Haynes

Role: Founder of Marie Haynes Consulting, author of SEO in the Gemini Era. Tracks Google quality systems and agentic answers.

Marie has decoded Google's quality systems longer than almost anyone in the industry, tracing Penguin, E-E-A-T, and the DOJ disclosures before turning that lens on AI Overviews, AI Mode, and agentic answers. Her newer focus is the point where an assistant stops quoting you and starts acting for the user, through protocols like MCP and WebMCP.

What to steal from her approach: read the quality systems, not the AEO checklist. If an engine has to trust the entity behind an answer, entity clarity beats keyword placement every time.

Follow: LinkedIn · X @Marie_Haynes · mariehaynes.com

7. Mark Williams-Cook

Role: Founder of AlsoAsked, digital director at Candour. Maps the question networks engines decompose prompts into.

Mark builds the map answer engine work runs on. AlsoAsked, which he founded, charts the question clusters that assistants fan a single prompt into, and his breakdown of the leaked Google Content Warehouse data and click models earned him a following well beyond SEO. If you want to understand how a query becomes an answer, start with his work.

What to steal from his approach: build content around the question network, not a single keyword. AEO rewards the site that answered the whole cluster, because that is what the engine decomposes the prompt into.

Follow: LinkedIn · X @myhead · alsoasked.com

8. Joshua Squires

Role: Director of AI Search at Amsive. Runs answer optimization delivery for client accounts at scale.

Josh runs AI search at Amsive alongside Lily Ray, and his job is the unglamorous one: turning research into something a delivery team can repeat across dozens of accounts. The output is playbooks for e-commerce and publisher clients who need to be the answer, not just cited, plus essays on preparing for assistant-driven search that avoid the vendor gloss. He publishes mostly on LinkedIn.

What to steal from his approach: AEO at agency scale is an operations problem, not a research problem. Turn the findings into a repeatable delivery process or the insight never ships.

Follow: LinkedIn · Amsive articles

9. Crystal Carter

Role: Head of AI Search and SEO Communications at Wix. Turns answer engine research into marketing practice.

Crystal works the platform side of answer visibility: she leads AI Search and SEO Communications at Wix, which reaches more than 300 million users, and partners directly with Google, Microsoft, and Semrush. Her talks are about getting working marketers to understand why an assistant names one brand and not another, which is why she is a regular from Google Search Central Live to MozCon.

What to steal from her approach: translate answer engine research into language a marketing team can act on. The teams that win AEO are the ones whose whole department understands the goal, not just the SEO lead.

Follow: LinkedIn · X @CrystalOnTheWeb · crystalcarter.ai

10. Kevin Indig

Role: Organic growth advisor to Meta, Ramp, and Upwork. Author of the Growth Memo newsletter.

Kevin's Growth Memo is where the industry gets its numbers. He ran growth at Shopify, G2, and Atlassian before going independent, and now turns out the studies everyone quotes, from AI Overviews traffic impact to why the same optimization produces different answers for different brands. His skill is wiring answer visibility to referral data and revenue, which is what gets an AEO budget approved.

What to steal from his approach: connect answer engine work to referral and revenue data. Answer presence that does not move business metrics will not survive a budget review.

Follow: LinkedIn · X @Kevin_Indig · growth-memo.com

11. Mike King

Role: Founder and CEO of iPullRank, author of The Science of SEO. Explains passage retrieval and relevance engineering.

Mike imported information retrieval theory into this field. His relevance engineering frame explains why an engine surfaces a passage at all, and he spends his time reading the papers most practitioners skip. iPullRank's SEO Week has become the venue where serious answer engine work first appears, and his analysis of the leaked Google API docs set the bar for the genre.

What to steal from his approach: think in retrieval, not rankings. An answer engine retrieves passages and assembles a response, so structure every page so a single section can stand alone as the answer.

Follow: LinkedIn · X @iPullRank · ipullrank.com

12. Dan Petrovic

Role: Managing Director of DEJAN, the Australian AI SEO agency. Tests what models associate with a brand.

Dan tests what a model genuinely believes about a brand. He fine-tunes his own Gemma models, applies mechanistic interpretability to the associations inside them, and has run 100,000 inference passes to chart how a model perceives a company. The grounding research he publishes across Google, OpenAI, and Anthropic is required reading, backed by two Google awards for exposing holes in their systems.

What to steal from his approach: test what the model believes about your brand, not what you claim about it. Mech interp shows the associations an assistant actually holds before it answers.

Follow: LinkedIn · X @dejanseo · dejan.ai

13. Olivier de Segonzac

Role: Co-founder and managing partner of RESONEO. Tracks what AI crawlers see before an answer is written.

Olivier publishes some of the hardest reverse engineering in the space: the LLM Crawler Report, which shows what ChatGPT, Perplexity, and Google AIO bots can and cannot see when they crawl a page, plus deep work on Google AI Mode's grounding URLs and Perplexity's Vespa Cloud stack. Search Engine Land picks up his findings, and peers like Dan Petrovic build on them.

What to steal from his approach: check what the crawler can actually read before you optimize the page. If the answer engine bot never sees your content, nothing else you do in AEO matters.

Follow: LinkedIn · X @5eg · resoneo.com

14. Wil Reynolds

Role: Founder of Seer Interactive. Applies warehouse-scale data to answer intent and buyer behavior.

Wil is the field's designated skeptic. He built Seer Interactive on warehouse-scale analysis of search and PPC data, and now points the same instrumentation at how assistants behave, which makes him the person to read when AEO advice outruns the evidence. His conference talks tend to be the correction.

What to steal from his approach: ask what the answer engine is actually optimizing for the user. The speculators chasing AEO tricks are usually answering a question no buyer asked.

Follow: LinkedIn · X @wilreynolds · seerinteractive.com

15. Aleyda Solis

Role: Founder of Orainti, creator of SEOFOMO and LearningAIsearch. Systematizes answer optimization frameworks.

Aleyda turns research into process. She founded Orainti, runs the SEOFOMO newsletter, and built LearningAIsearch to teach answer optimization as a repeatable practice rather than a pile of hacks. Her checklists are what a team actually reaches for when it has hundreds of pages to make answer-ready.

What to steal from her approach: turn AEO into a repeatable checklist your team can run per page. Frameworks beat heroics when you have hundreds of pages to make answer-ready.

Follow: LinkedIn · X @aleyda · aleydasolis.com

16. Eli Schwartz

Role: Growth advisor and author of Product-Led SEO. Frames answer optimization as product strategy.

Eli argues that search should be built as a product decision, not a marketing bolt-on, and that framing is exactly what AEO needs. He advised Zapier, Tinder, and Coinbase on organic growth, and his newsletter applies a revenue filter to answer visibility: what drives demand, what you can measure, and what is theater.

What to steal from his approach: treat AEO as a product decision about who you are for. Being the answer in a category you do not sell into is noise, not growth.

Follow: LinkedIn · X @5le · productledseo.com

17. Olaf Kopp

Role: Co-founder and Head of SEO and AI Search at Aufgesang. Researches entity clarity and LLM readability.

Olaf was writing about LLM optimization in 2023, before the industry had the words for it, on top of a decade of semantic search and entity research. He built the first public index of SEO-relevant patents and papers, and the vocabulary he coined, like LLM readability and brand context optimization, is now standard in answer optimization circles well beyond the German market.

What to steal from his approach: write for entity clarity. An answer engine has to resolve what your brand is and what it does before it can name you, so define the entity explicitly.

Follow: LinkedIn · X @Olaf_Kopp · kopp-online-marketing.com

18. Myriam Jessier

Role: AI Search Strategist at PRAGM, monthly contributor to Search Engine Land. Works on multimodal answers.

Myriam covers the part of answer optimization almost everyone else skips: how assistants read video, pull text out of images via OCR, and form brand impressions from audio, packaging, and visuals. A Search Engine Land contributor and a regular on the BrightonSEO and SMX stages, her material is the reference for brands whose proof lives outside a blog post.

What to steal from her approach: make every asset an answer engine can read. If your proof lives in an image, a video, or a PDF, an assistant cannot quote it until you make it machine readable.

Follow: LinkedIn · X @myriamjessier · myriamjessier.com

19. Andrea Volpini

Role: Co-founder and CEO of WordLift. Builds knowledge graphs that make brands nameable by assistants.

Andrea has been building knowledge graphs and entity tooling since long before LLMs made structured data fashionable, and that patience paid off when entity clarity became a hard requirement for getting named by an assistant. His experiments linking knowledge graphs, schema, and model output are among the most technically grounded in the field.

What to steal from his approach: give the answer engine structured facts, not prose it has to interpret. Knowledge graph and schema clarity is how you become quotable and nameable.

Follow: LinkedIn · X @cyberandy · wordlift.io

20. Bernard Huang

Role: Co-founder of Clearscope. Works on content trust when the reader is an autonomous agent.

Bernard bootstrapped Clearscope to seven-figure ARR with customers like Nvidia, HubSpot, and Adobe, and ran one of the better public webinar series on what actually moves search. His current focus is the next awkward question: what content strategy looks like when an autonomous agent, not a person, is the one reading and deciding.

What to steal from his approach: write for the agent as reader. When an assistant summarizes your page into a recommendation, coverage and clarity beat persuasion every time.

Follow: LinkedIn · X @bernardjhuang · clearscope.io

21. Ann Smarty

Role: Founder of Smarty Marketing, co-founder of Smarty AI. Builds brand signals that shape AI answers.

Ann has spent nearly two decades teaching brands how to earn attention, from Internet Marketing Ninjas to her current work on the signals that make an assistant mention you. She was among the AEO names asked to define 2026 search strategy, and her strength is translating off-site signals, community, and authority into practice a team can run.

What to steal from her approach: answer presence is earned off-site too. The sources an engine cites about you shape what it says about you, so brand signal work is AEO work.

Follow: LinkedIn · X @seosmarty

22. Bartosz Góralewicz

Role: CEO of ZipTie.AI, founder of Onely. Measures the traffic impact of answer engine changes.

Bartosz made his name in JavaScript SEO, where his experiments shaped how Google renders JS, then moved early into answer visibility by co-founding ZipTie. He is the person who insists on measuring the traffic effect of answer changes by vertical, so the conversation runs on numbers rather than a screenshot someone shared.

What to steal from his approach: measure the traffic impact of answer engine changes by vertical. AEO decisions should be backed by numbers, not by a screenshot someone posted.

Follow: LinkedIn · X @bart_goralewicz · ziptie.ai

23. Cindy Krum

Role: Founder and CEO of MobileMoxie. Author of the fraggles theory behind passage-level answer retrieval.

Cindy's fraggles theory, that Google ranks page fragments rather than whole documents, anticipated the chunk-level retrieval behind today's answers years before it was obvious. It is the cleanest mental model in AEO: an assistant almost never reads your page end to end, it grabs the passage that matches the question.

What to steal from her approach: write in self-contained chunks. Every section should answer its own question cleanly, because that fragment is what gets retrieved and spoken.

Follow: LinkedIn · X @Suzzicks · mobilemoxie.com

24. Britney Muller

Role: Founder of Data Sci 101, former senior SEO scientist at Moz. Explains how models select an answer.

Britney explains how models actually work, which is rare in a field full of confident guessing. A former senior SEO scientist at Moz, she taught herself data science and built Data Sci 101 to teach marketers the same, and her consulting ties AI strategy to the underlying mechanics rather than the marketing around it.

What to steal from her approach: learn how the model actually works before you try to influence it. AEO built on a real mental model of retrieval survives every algorithm change.

Follow: LinkedIn · X @BritneyMuller

How to actually use this list

Following answer engine experts is step zero, not a strategy. Here is the loop that works.

First, read their research and extract testable claims, not conclusions. Second, test the claims on your own brand and prompts, because AI answers vary by niche, language, and run. Third, track whether you are named as the answer, not just cited, with a proper measurement layer. My best AEO tools guide covers the tracking side, and the GEO experts list covers the generative side.

And remember the rule every person on this list repeats: never trust one run. Answer share moves run to run, so read trends, not snapshots.

Frequently asked questions

Who are the top AEO experts in 2026?

The top tier in 2026 is Metehan Yesilyurt, Malte Landwehr, Tomek Rudzki, and David Konitzny, based on original research output: all four reverse-engineer how answer engines decide which source becomes the answer. The wider list of 24 includes Lily Ray, Marie Haynes, Mark Williams-Cook, Crystal Carter, Mike King, Dan Petrovic, and Cindy Krum.

What is the difference between AEO and GEO?

AEO, Answer Engine Optimization, focuses on being the direct answer an AI engine gives. GEO, Generative Engine Optimization, focuses on shaping how a model retrieves, describes, and cites your brand over time. AEO is the answer slot, GEO is the retrieval and citation layer behind it.

Where should I follow AEO experts?

LinkedIn is the primary channel for almost every expert on this list, with X as the secondary channel. Many also run newsletters worth subscribing to: Marie Haynes's Search News You Can Use, Kevin Indig's Growth Memo, Aleyda Solis's SEOFOMO, and Eli Schwartz's Product-Led SEO.

Why do so many top AEO experts work at Peec AI?

Peec AI built a dedicated research team to reverse-engineer how answer engines decide which brands to name, which attracted researchers like Metehan Yesilyurt and Tomek Rudzki, alongside Malte Landwehr running product. Concentrating researchers in one place is why the company's findings show up across the industry.

How do I verify if someone is a real AEO expert?

Ask three questions. Do they publish original experiments with methodology, or only opinions? Do their findings get cited and rebuilt by other practitioners? Do they disclose limits and conflicts, like vendor affiliations? Anyone who fails all three is a commentator, not an expert.

Do I need an expert or a tool for AI visibility?

Both, in that order of thinking. Experts tell you how answer engines work and what to test. Tools tell you whether you are being named as the answer for your brand. Start with the research from this list, then add a tracker so you measure trends instead of guessing.

Full transparency: this is my site and I put myself first. I ranked myself number one, so read the list knowing that, and judge the work rather than the placement.