On September 15, 2026, a two-year-old company called Profound announced a $180 million Series D at a $1.8 billion valuation, less than seven months after raising $96 million. Profound sells software that helps brands show up when people ask an AI what to buy. TechCrunch reported that its revenue tripled in six months and that it has more than 1,000 enterprise customers, Comcast, Estée Lauder and Walmart among them.
In 1955, Charlie Loudermilk and a partner started renting out chairs in Atlanta. The business became Aaron Rents, a name Loudermilk picked for its spot at the top of the phone book. There is no Aaron. By the end of 2016 the company ran 1,864 stores of its own, plus 699 franchised ones.
Seventy-one years apart, same business plan. Get to the top of whatever list the customer reads first.
The consensus take is that the alphabet game died with the phone book, that Google killed it and AI will bury whatever is left. That take is comfortable and wrong. The phone book is dying on a published schedule, but the list never went anywhere, and the research on how large language models read lists says the top slot still carries the most weight, just as it did when Illinois plumbers were naming themselves AAAA.
The First Directory Had 50 Names. One Company Took Four Lines.
On February 21, 1878, the New Haven District Telephone Company printed the world’s first telephone directory. It was one page. It listed 50 subscribers and no phone numbers, because an operator connected every call by name. Nine listings were residences, three were physicians and two were dentists. The New Haven Flour Company appeared four times, once for each of its locations.
By November the company had 391 subscribers and a real booklet to list them in. A surviving copy sold at auction in 2008 for $170,500.
The Yellow Pages have a better origin story than they have evidence. The legend says a printer in Cheyenne, Wyoming ran out of white paper in 1883 and used yellow. Tedium went looking for the source, and the oldest reference it could find was a 1971 Casper Star-Tribune article citing the Wall Street Journal. What holds up is that Reuben H. Donnelley produced the first official Yellow Pages for Chicago in 1886, and that the format listed businesses alphabetically inside each category heading.
That one design choice created an American naming tradition.
AT&T turned the book into a habit. The “Let Your Fingers Do the Walking” campaign debuted in a one-minute spot on the Bell Telephone Hour on January 19, 1962, and AT&T credited it with pushing Yellow Pages revenue to a record the following year. When the Bell System later tried to register the walking-fingers logo, the application was refused because the symbol had become a generic marker for yellow pages of any kind. AT&T built a brand so well that it stopped owning it.
There Is No Aaron
Loudermilk’s move was blunt. Tony Martino’s was tidier. Martino opened a transmission shop in Philadelphia in the late 1950s and called it AAMCO, for Anthony A. Martino Company, and his obituary says he picked the name to top the Yellow Pages listings. He sold his interest in 1967. In 1972 he started an auto-painting franchise and, as he put it, “turned my initials around and used them again.” MAACO starts with an M. By 2008 it had painted more than 17 million vehicles.
The other end of the alphabet had its own entrepreneurs. In 1944 a former radio evangelist named Curtis Howe Springer staked mining claims on federal land in the Mojave, built a health resort around a spring, and named the place Zzyzx, which he promoted as the last word in the English language. He presented himself as a doctor and a minister and was licensed as neither. He and his followers were evicted in 1974. The site is now a Cal State desert research station, and anyone who drives I-15 between Las Vegas and Los Angeles has passed the exit.
Barry Minkow started a carpet-cleaning business in Reseda in 1982 and named it ZZZZ Best. According to The Hustle, the four Z’s stood for the number of children he hoped to have. The company went public, passed $200 million in market value, and collapsed in 1987, costing investors and lenders about $100 million. The carpet cleaning was real, and reportedly good. The insurance restoration business, which grew to 86% of revenue, was fiction.
In Las Vegas, A Little White Wedding Chapel and A Special Memory Wedding Chapel both lead with the indefinite article. Nobody has put the reason in writing, so I won’t either.
Somebody Finally Called Every Plumber in Illinois
The alphabet game got its definitive study in 2014, when Duke economist Ryan McDevitt published “‘A’ Business by Any Other Name” in the Journal of Political Economy. The average plumbing firm whose name starts with A or a number drew five times as many service complaints as other firms and charged higher prices. The A-name plumbers also advertised more, in the Yellow Pages and on Google, and the advertising itself went hand in hand with complaints.
The 2009 working paper shows the work. It describes pulling every plumbing listing in Illinois, matching each one to Better Business Bureau complaints, phoning the listings for price quotes, and running more than 5,000 Google searches to capture the paid results. Its examples of A-names in actual use include A-AAAA Sewer & Drains, AAAA Scott’s Plumbing, A Aabbey Plumbing and A Abest Rooter. In the city of Chicago, 35% of new home-service businesses picked a name starting with A or a number, against 10% of new businesses across all categories. Plumbers who ran an A-name and a regular name at the same time drew more than fifteen times the complaints. In metro Chicago the A-name carried an 8.4% price premium.
The paper also points out that “Always Over Budget Plumbers” would sort ahead of its under-budget competitor and might still lose the call, and that patients don’t choose a urologist by dialing the first name in the book. That’s why the A-names pile up in the home-service trades. The business comes to your house, so its address stops mattering and its name does the selling.
The economics are simple. A good plumber gets repeat customers and referrals. A bad plumber doesn’t, so he lives on first-time callers in a hurry, and a first-time caller in a hurry starts at the top of the page. The extra A’s were a quality signal all along. They just pointed the wrong way.
Jeff Bezos Opened the Dictionary to A
The early web was a directory with blue links. Yahoo launched in January 1994 as a hand-built guide to sites sorted into categories. That October, Jeff Bezos paged through the A section of a dictionary looking for a company name, because website listings of the day were alphabetized. His first choice, Cadabra, had a flaw. His lawyer heard it over the phone as “cadaver.” Relentless.com, another name he registered, still sends visitors to Amazon.
DMOZ, the volunteer-run Open Directory Project, wrote the problem into its editing guidelines. Listings were mostly sorted alphabetically, the guidelines said, and people tried to get to the top unfairly, so editors were told to remove punctuation and filler letters from the front of titles. The example of what to fix was a site that submitted itself as “aaa Website.”
Wall Street paid for letters, too. Michael Cooper, Orlin Dimitrov and Raghavendra Rau looked at companies that switched to dotcom names between June 1998 and July 1999 and published the results in the Journal of Finance in December 2001. The rename produced cumulative abnormal returns of roughly 74% over the ten days around the announcement, and the jump looked about the same whether or not the company did much on the internet.
Relevance ranking ended alphabetical order, and the gaming moved to wherever the ranking lived. For years the move was the exact-match domain. Buy the search phrase as your web address and ride it up the results. On September 28, 2012, Google’s Matt Cutts announced the fix on Twitter as a “minor weather report.” The update noticeably changed 0.6% of English-language queries in the U.S.
I built one of those directories. In 2000 I wrote the ASP for usetheexperts.com, a consumer-to-local-business directory, at Web Business Development in Chatsworth. I have told the story of that site before, in “You Know Angie’s List. You Probably Never Heard of UseTheExperts.com”. Every directory has to answer one question before it answers any other. When two businesses match the search, which one goes first? On a SQL-backed ASP page, the cheapest answer is ORDER BY BusinessName, and the cheapest answer is exactly what every AAAA plumber was counting on. Web Business Development also had a shopping cart domain, 123cart.com. In a phone book, in a web directory and in an ORDER BY, a 1 sorts ahead of an A.
The Phone Book Has a Death Date, and It’s in a 10-K
Thryv, the country’s largest publisher of printed directories, put the date in its annual report. The final publication of its printed directories will happen in December 2028. Its legacy Marketing Services business, which includes the books, had about 233,000 clients at the end of 2024 and about 171,000 a year later. Britain got there first. Yell printed its last Yellow Pages in January 2019, with the final copies going to Brighton, where the British edition started in 1966.
So nobody needs to be named AAAA anymore. The trick just never needed paper.
Whitespark’s 2026 Local Search Ranking Factors survey puts keywords in a Google Business Profile name at number three among the factors for map results. Adding those keywords is against Google’s guidelines, and Whitespark notes that Google rarely suspends a profile for it and more often just deletes the extra words. The modern AAAA doesn’t sit at the front of the name. It sits in the middle, on a listing called something like Joe’s Plumbing Emergency Plumber Las Vegas 24/7.
The fraud followed the same categories. In June 2019, the Wall Street Journal reported that Google Maps carried roughly 11 million falsely listed businesses on any given day. The estimate came from a survey of fraud specialists, and the fakes clustered in categories Google internally calls “duress verticals,” among them contractors, electricians, towing, car repair, movers and lawyers. Google disputed the scale and said it had removed more than 3 million fake business profiles in 2018.
Those are McDevitt’s plumbers with better tooling.
How a Model Actually Picks the Next Word
A large language model doesn’t have a phone book. It has weights, and it has whatever text you put in front of it.
The weights come from pretraining. The model reads trillions of tokens and adjusts billions of parameters to get better at one job, predicting the next token, so whatever showed up often in that text becomes cheap for the model to say. Research on where facts live points to the feed-forward layers in the middle of the network. In 2022, Kevin Meng, David Bau and colleagues showed they could change a small set of weights in GPT-J and teach it that the Eiffel Tower is in Rome, and the model carried the new fact into other contexts. In one sample from their paper, the edited model placed the tower across from St. Peter’s Basilica. The weights are the printed book. Like the Yellow Pages, they come out on a schedule and go stale until the next edition, and anything newer has to be handed to the model at runtime.
Then the model writes. Text is chopped into tokens, each token becomes a vector, and a stack of attention layers, the architecture from the 2017 paper “Attention Is All You Need,” mixes them together. At the end, the model assigns a score to every token in its vocabulary, and a softmax turns those scores into probabilities. Greedy decoding takes the top one. Sampling draws from the distribution instead, shaped by a temperature setting and often limited to the smallest group of tokens that covers most of the probability, which is nucleus sampling. The chosen token is appended, and the whole thing runs again for the next one.
There is no alphabetical order anywhere in that loop. The model doesn’t even see letters. I ran McDevitt’s plumber names, plus a sixteen-A towing company I made up, through the tokenizers OpenAI uses for GPT-3, GPT-4 and GPT-4o. In all three, “AAAA” is a single token, and so is a run of eight A’s. AAAAAAAAAAAAAAAA Towing arrives as four tokens, two of them identical. Whatever those letters bought in a printed directory, the model sees one chunk.
One piece of the old rule survives. In all three vocabularies, token ID 0 is the exclamation point, exactly the kind of leading punctuation DMOZ told its editors to strip. And when two scores tie exactly, PyTorch’s argmax returns the first index. NumPy does the same. So under greedy decoding, a perfect tie goes to the lowest ID. Exact ties between floating-point scores almost never happen. Don’t rename your company “!”.
The phone book also had listings nobody ever called. In early 2023, researchers Jessica Rumbelow and Matthew Watkins found a cluster of strange tokens in the GPT vocabulary, including SolidGoldMagikarp. Asked to repeat it, ChatGPT said “distribute.” Several of the strange tokens turned out to be usernames from r/counting, a Reddit forum whose members had spent roughly a decade counting toward infinity, one post at a time. The names made it into the tokenizer’s vocabulary but barely into the model’s training, so the model had a listing it knew nothing about. In the GPT-4 and GPT-4o vocabularies the string isn’t a token anymore. It breaks into five ordinary pieces.
The Model Reads the List From the Top, Too
Most AI answers about businesses and products don’t come from the weights alone. A retrieval system runs a search, pulls back a handful of documents, and pastes them into the model’s context in some order. Then the model reads a list.
It reads that list the way people read the Yellow Pages. In “Lost in the Middle,” published in Transactions of the ACL in 2024, Nelson Liu and colleagues at Stanford showed that models use information best when it sits at the start or the end of the context and worst when it’s buried in the middle. The authors connect that U-shaped curve to the serial-position effect in human memory. McDevitt’s working paper, for its part, cites a 1997 eye-tracking study for the point that many consumers scan Yellow Pages listings from the top down. Same bias, twenty-seven years apart, one reader made of silicon.
The bias shows up wherever a model ranks things. Yupeng Hou and colleagues found that LLM recommenders can be swayed by an item’s popularity and by where it sits in the prompt, and they reduced the effect by shuffling the candidates, ranking several times and merging the results. Chujie Zheng and colleagues tested 20 models on multiple-choice benchmarks and found they put extra probability on particular answer letters, a bias the authors trace mainly to the letter tokens themselves. In a later fine-tuning study, Llama-2-7B leaned toward C and Llama-2-13B toward D. Every model has its own AAA. It isn’t always A.
California’s election officials have been using the fix for more than a decade. The Secretary of State holds a randomized alphabet drawing 82 days before each election, with staff pulling letters lottery-style, and the drawn alphabet sets the order of names on the ballot. Statewide candidates then rotate through all 80 Assembly districts. The state has reason to bother. Marc Meredith and Yuval Salant studied California city council and school board races and found that candidates listed first won office four to five percentage points more often than they would have without the order effect. The machine learning papers and the state of California reached the same answer. Shuffle the list.
A $1.8 Billion Industry Meets a Benchmark
The business Profound is in goes by generative engine optimization, or answer engine optimization if you ask Profound. The first term was coined in a paper by researchers at Princeton and IIT Delhi and presented at KDD in 2024. That paper tested content rewrites and reported that adding statistics, quotations and citations raised a source’s visibility in AI answers by up to roughly 40% on its main metric. It also found that keyword stuffing, the old SEO standby, didn’t help.
In 2025, researchers from Parameter Lab, TU Darmstadt, the University of Mannheim, the University of Tübingen and NAVER built C-SEO Bench to test those rewrites much more broadly. They covered question answering and product recommendation across six domains and more than 16,000 documents, on GPT-4o-mini, Claude 3.5 Haiku, o3 and o4-mini, and the paper was accepted to the NeurIPS 2025 Datasets and Benchmarks track. Instead of counting how many words a model spends on a source, they measured how early the model cites it, which is closer to what a business actually cares about.
Out of 54 test cases, three showed a statistically significant improvement. Adding statistics, one of the headline tactics from the Princeton paper, lowered a document’s rank in 19 of the 24 settings where it was tested, and on Claude 3.5 Haiku’s product recommendations, 26 of 30 cases got significantly worse. The authors trace the gap with the earlier results mostly to the metric, since a model can say a lot about a source without ranking it first.
Then they tested the oldest move there is. They moved a document to the first slot. On GPT-4o-mini’s retail recommendations, that alone improved its citation rank by 2.77 places, while the best rewrite, a prepended summary modeled on the llms.txt proposal, managed 0.36. The top slot was worth more than seven times the best rewrite, and documents moved to the bottom slots got worse. The authors conclude that ranking in retrieval, which is to say ordinary SEO, still dominates.
They also ran the arms race. As more competitors adopted the same rewrite, each one’s gain shrank toward zero, a dynamic the authors call “congested and zero-sum.” McDevitt’s plumbers could have told them. When everyone is AAAA, nobody is.
The benchmark only tested polite, white-hat tactics. The dirty tricks live in a separate literature, built on smaller experiments, and the two that matter most here both worked. Aounon Kumar and Himabindu Lakkaraju showed in 2024 that an optimized string of text added to a product page could push a fictitious coffee machine toward the top of an LLM’s recommendations, in a test catalog of ten machines. In October 2025, Anthropic, the UK AI Security Institute and the Alan Turing Institute showed that as few as 250 poisoned documents could plant a backdoor in models from 600 million to 13 billion parameters, even though the largest trained on more than 20 times the data of the smallest. That backdoor only made the model produce gibberish, and the authors say it’s unlikely to pose a significant risk in frontier models. Nobody has shown that 250 documents can make a frontier model recommend a plumber. What matters is that the number didn’t grow with the model.
Two Pages for Joe’s Plumbing
Take Joe, whose listing says Emergency Plumber Las Vegas 24/7, and a question somebody types into an assistant: how much does it cost to replace a water heater in Las Vegas? The system runs a search on something close to that phrase, keeps the top handful of pages, pastes them into the context in rank order, and the model writes. It reads from the top.
Joe already has a page called Plumbing Services, and somebody has sold him on statistics. The page now says that water heaters last eight to twelve years, that the Department of Energy puts water heating at about 18% of a home’s energy use, and that his 4.9-star team has served more than 10,000 customers. Three numbers, none of them the answer. The search engine doesn’t rank the page for the question, because the page isn’t about the question, and if it slipped into the fourth slot anyway, there is nothing in it for the model to quote about price. The statistics are decorating a page the retriever never picks.
Here is the page I would write for him instead, with numbers I made up. It lives at joesplumbing.com/water-heater-replacement-cost-las-vegas, its title is Water Heater Replacement Cost in Las Vegas, and its first paragraph, before anything else, reads:
A standard 40 or 50 gallon gas tank water heater replacement in Las Vegas runs $1,600 to $2,400 installed, including the unit, the permit, haul-away and same-day installation across most of Clark County. Tankless conversions run $3,800 to $5,500. Prices updated September 2026. Nevada State Contractors Board license 0012345.
Below that, what moves the price: venting, an expansion tank, code upgrades, garage versus attic. Then the questions people actually type, answered in a paragraph each, and LocalBusiness and FAQ structured data so a machine can pull the fields without parsing the prose.
That page ranks because it is about exactly that query. Ranking puts it in the first slot or the second, which is where the NeurIPS benchmark found the whole effect and where Liu’s curve says the model reads most carefully. The model finds a direct, dated, checkable answer sitting where it reads first. Notice that this page has a statistic in it too. The difference is that the number is the answer instead of decoration on a page about something else.
Half the work happens off Joe’s site. The same price range, license number, hours and service area go on his Google Business Profile, on Yelp, on the Better Business Bureau listing, on Nextdoor, and into the r/vegas thread where somebody asked who replaced their water heater. Assistants read several sources at once, and some of them read those sites more than they read Joe. A claim that shows up the same way from five directions is one an assistant can act on. A claim that lives only on Joe’s own page is one more listing from an interested party, which is what the phone book was full of.
And if every plumber in the valley publishes the same page, the search engine still has to pick one. The choice falls back on the ordinary signals, links, reviews, freshness, plus which name the model had already heard before the search ran. That contest is older than the web. Joe’s page doesn’t end it. It gets him into it.
What $1.8 Billion Buys
None of this says Profound’s customers are wasting their money. Knowing whether ChatGPT mentions your brand, and what it says when it does, is worth measuring, and TechCrunch describes Profound as starting out as an analytics platform before it expanded into research and marketing strategy. I’d buy the dashboard.
Rewriting your pages for the robots is a different purchase, and the NeurIPS benchmark is a good reason to ask for the numbers first. The variable that moved everything in that study was order. Order gets decided upstream, by whatever system retrieves the documents, and in the AI search setups these papers model, that’s a search engine doing ordinary ranking.
I work on that layer. A storefront assistant like Cara, which I build for ShopSnap, can’t answer from the weights, because no model was trained on a given store’s catalog. Retrieval hands the model a set of products before it writes a word, and the order of that set is a ranking decision whether or not anyone on the team calls it one. The model will lean toward the top. So the order has to be picked on purpose, by relevance you can defend, or shuffled and ranked more than once the way Hou’s team did it. Otherwise the sort key is choosing products, and a sort key is how the phone book ended up full of AAAA.
Charlie Loudermilk got to the top of the list with a name that cost him nothing. There is still no Aaron.
About the Author
Gal Ratner has spent nearly thirty years building and shipping production software on the Microsoft and .NET stack, for clients including Microsoft, Sony, Rockstar Games, 2K Games, Best Buy, and Allegiant Air. In 2000 he wrote the ASP behind usetheexperts.com, an early consumer-to-local-business directory, and he was the sixth employee at Break.com during its run as one of the largest entertainment sites on the web. He is Chief Architect at Prana Entertainment and the founder and CTO of Inverted Software and WhiteStar Labs, an enterprise software and AI consultancy based in Las Vegas, and a finalist for the Los Angeles Business Journal’s CTO of the Year.
His current work is agentic AI in production, including MCP servers, the Microsoft Agent Framework, RAG pipelines on SQL Server 2025 vector search, the Cara shopping assistant for ShopSnap storefronts, and the open-source diagnostics tooling behind PLogger. He writes about AI the way a practitioner sees it, bullish on what the tools can actually do and allergic to the marketing wrapped around them.
galratner.substack.com · linkedin.com/in/galratner · galratner.com
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