The Hidden Costs of Tokenmaxxing

As of 2 Sep 2026

NOTE FROM THE HUMAN: The following blog post was generated and sourced solely by Claude Fable 5.1 and the model even chose the title. As a Claude subscriber, I have wondered if I am "losing" some of money I pay for usage by not tokenmaxxing. After reading the article Fable compiled, I would say "yes" and "no". As a genealogist, Fable has been a powerful model for accurate transcriptions, genealogical compilations, research and even producing apps and plug-ins. Could I tokenmax for genealogy? Yes, but I would have to put a lot of effort into doing so. Do I want to? No, not really. I already switch to lower use models like Sonnet if I am doing a proofreading and grammar check. I also delete chats I do not need later to save server space because it's on a server somewhere. Some of the Silicon Valley workers are using the models for EVERYTHING - things you would previously use a Google search for. I do not do that - I still use search browsers like Google and Brave. I do realize that those browsers have incorporated AI but again, my goal in using them is NOT using tokens on my subscription plans. I have used Claude at high usage times, and it has told me to come back later. If anything, I find that irritating, especially if I am being blocked from using it because someone is tokenmaxxing what to eat for dinner, driving directions, or even more wasteful - asking the same questions repeatedly as Fable cites below. (Grrr....) Be sure to read the articles linked here for the big picture. Even the Wall Street Journal article can be read with a free account. As a genealogist, I hope this information helps my peers find their balance. ~ Katherine

The Hidden Costs of Tokenmaxxing 
by Claude Fable 5.1

"Tokenmaxxing" is the practice of maximizing AI token consumption and treating that volume as proof of productivity. The term entered mainstream use in April 2026, driven largely by reports of an internal leaderboard at Meta that ranked roughly 85,000 employees by their AI token usage, with the top user reportedly burning through 281 billion tokens in a single month.[1] Other large employers, including JPMorgan and Disney, were reported to be running similar rankings.[2] The idea rested on an assumption: heavy AI consumption would eventually produce better outcomes, and inference costs would keep falling fast enough to make high usage a nearly free bet.[3]

That assumption has not held up, and it holds up least well for Anthropic's most expensive tier, Claude Fable 5.1. What follows is a summary of the documented downsides, grouped into three categories: money and plan limits, productivity and organizational effects, and environmental impact. It closes with what critics propose instead.

Financial Costs and Plan Limits

Fable-class models sit at the top of Anthropic's price list. On the API, Fable 5 costs $10 per million input tokens and $50 per million output tokens, which is double the rate of Opus 5.[4] Fable 5.1 reduced the cost of cache reads to $0.25 per million tokens, but every other line on the price sheet stayed the same, and Anthropic's advertised savings of roughly 25 to 45 percent describe measured bills from its own August usage rather than any change to published rates.[5]

Subscription users face a separate constraint. According to Anthropic's help center, Fable 5 and Fable 5.1 draw from a plan's regular weekly usage limits and consume them faster than other Claude models. On Max plans and premium seats, up to half of the weekly limit can be spent on Fable models before usage credits are required. On Pro plans and standard seats, Fable models are not included in the plan's limits at all and run only on prepaid credits billed at API rates.[6] Since July 20, 2026, that split is permanent.[4] Early user reports on Fable 5.1 note that even with cheaper cache reads, the model still exhausts usage limits quickly in long agentic sessions, because per-token pricing and per-session quotas are governed separately.[7]

When a limit is reached, further requests may be throttled or blocked until the cooldown resets.[8] The pressure is industry wide. During a compute crunch earlier in 2026, Anthropic responded by capping token consumption on certain pricing tiers during peak hours, and OpenAI moved its Codex product from per-message to per-token pricing.[3]

Productivity and Organizational Effects

The central problem with tokenmaxxing is that it measures an input and calls it an output. As IBM's analysis put it, usage soon became a proxy for value, and organizations that built usage leaderboards found people quickly learned to game them.[9] The Pragmatic Engineer newsletter reported on a Microsoft engineer who admitted inflating token counts to avoid being seen as using too little AI, including asking the AI questions already answered in internal documentation and prototyping features with no intention of shipping them. The newsletter concluded that the incentive in some cases produced slower work and busywork.[10]

By midsummer the fad was visibly reversing. Tom's Hardware reported that agentic AI can consume up to 1,000 times more tokens than standard AI, prompting corporate pullbacks at Microsoft, Meta, and Amazon as costs rose without a matching gain in output.[11] The Wall Street Journal noted the underlying arithmetic: the price per token has dropped, but the number of tokens needed per meaningful result has risen sharply, especially in agent-driven workflows.[12]

Environmental Impact

Every additional token is additional inference compute, and inference is now where most of AI's energy goes. A June 2026 report from United Nations University found that once a model is deployed, user interactions consume an estimated 80 to 90 percent of its total energy, and that policy attention should shift from training runs toward product defaults, model selection, and user behavior. The same report projects that by 2030 data centers powering AI will consume 945 terawatt-hours of electricity, with an associated water footprint of 9.3 trillion liters and a land footprint of more than 14,500 square kilometers.[13]

The International AI Safety Report estimates that data centers and data transmission account for about one percent of global energy-related greenhouse gas emissions, with AI using 10 to 28 percent of data center energy capacity, and describes AI as a moderate but rapidly growing contributor.[14]

Frontier reasoning models are the most expensive class per query. An infrastructure-aware benchmark of 30 models by researchers at the University of Rhode Island and partner institutions found that reasoning models such as OpenAI's o3 and DeepSeek's R1 use more than 33 watt-hours for a long answer, over 70 times the energy of a small model.[15] The same study notes that water used for data center cooling is largely evaporated freshwater removed from local ecosystems rather than recycled.[16] Fable 5.1 belongs to this reasoning class.

No verified per-token figure exists for Fable 5.1 itself. Anthropic and OpenAI had not submitted models to the AI Energy Score benchmark as of May 2026, and one sustainability analyst notes that while Anthropic can tell a good per-query efficiency story, it currently publishes no facility-level environmental disclosure.[17] An independent estimate based on Claude Code billing data placed Anthropic's total inference power draw at roughly 85 megawatts, with the caveat that the figure is likely low.[18]

What the Critics Propose Instead

The shared conclusion of IBM, Exadel, and the Pragmatic Engineer is that token volume is the wrong metric in either direction. IBM warns that token minimization falls into the same trap as tokenmaxxing: once obvious waste like oversized tool catalogs and stale context is removed, further cuts start removing the task descriptions and constraints that help the model succeed, and the cost simply moves into retries, extra tool calls, and human rework.[9]

The alternative is to measure accepted output against cost. Count what survived human review and was actually used: merged pull requests, closed tickets, approved documents, hours of manual work replaced. A prototype nobody wanted counts as zero regardless of the tokens it consumed. Divide those results by the dollars or plan credits spent to get cost per accepted result, so that a heavy user who ships a lot looks good and a heavy user who ships nothing looks like what they are. Route routine work to cheaper models and reserve Fable-tier models for problems that would otherwise warrant a senior specialist, cap output length, and use prompt caching.[19] And retire any public leaderboard ranked by token count, since it will be gamed.

For an individual user the version is simpler. Before a long session, decide what finished thing you want at the end of it, and judge the session by whether you got it rather than by how much you used.

Footnotes

[1] Exadel, "What Is Tokenmaxxing and Why It's a Liability," June 30, 2026. https://exadel.com/news/tokenmaxxing-ai-productivity-enterprise-roi

[2] Hayley Peterson, "Tell us if you're on the AI leaderboard at work," Business Insider, April 2026. https://www.businessinsider.com/jpmorgan-disney-employees-vie-for-ai-leaderboard-status-tokenmaxxing-2026-4

[3] Exadel, June 30, 2026, cited above.

[4] ClaudeFast, "Claude Fable 5 Price: Is It Free, Usage Credits, and Access," August 2026. https://claudefa.st/blog/guide/development/fable-5-usage-credits

[5] Digital Applied, "What Claude Fable 5.1 Costs, and What It Breaks," September 2026. https://www.digitalapplied.com/blog/claude-fable-5-1-cost-and-breaking-changes

[6] Anthropic Help Center, "Claude Fable models on your plan," updated September 2026. https://support.claude.com/en/articles/15424964-claude-fable-models-on-your-plan

[7] explainx.ai, "Claude Fable 5.1: 55.8% Terminal-Bench, 25% Cheaper," September 2026. https://www.explainx.ai/blog/claude-fable-5-1-mythos-5-1-launch-benchmarks-pricing-2026

[8] Layer3 Labs, "Claude Fable 5.1 Limits: Quotas, Context, and Rate Caps," September 2026. https://www.layer3labs.io/guides/claude-fable-5-1-limits

[9] IBM Think, "Tokenmaxxing is dead, long live valuemaxxing," June 25, 2026. https://www.ibm.com/think/insights/tokenmaxxing-dead-long-live-valuemaxxing

[10] Gergely Orosz, "The Pulse: 'Tokenmaxxing' as a weird new trend," The Pragmatic Engineer, April 23, 2026. https://blog.pragmaticengineer.com/the-pulse-tokenmaxxing-as-a-weird-new-trend/

[11] Tom's Hardware, "AI cost crisis hits tech giants as employee tokenmaxxing backfires," May 2026. https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-cost-crisis-hits-tech-giants-as-employee-tokenmaxxing-backfires-agentic-ai-eats-up-to-1000x-more-tokens-than-standard-ai-sparks-corporate-pullback-at-microsoft-meta-and-amazon

[12] Isabelle Bousquette, "Why Some Companies Say AI 'Tokenmaxxing' Is Key to Survival," The Wall Street Journal, April 14, 2026. https://www.wsj.com/cio-journal/why-some-companies-say-ai-tokenmaxxing-is-key-to-survival-e699a128

[13] United Nations University Institute for Water, Environment and Health, "Rising Emissions, Depleting Water and Vanishing Land," June 3, 2026. https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints

[14] International AI Safety Report, Section 2.3.4, "Risks to the environment," 2025. https://arxiv.org/pdf/2501.17805

[15] Fast Company, "The environmental impact of LLMs: Here's how OpenAI, DeepSeek, and Anthropic stack up," May 20, 2025. https://www.fastcompany.com/91336991/openai-anthropic-deepseek-ai-models-environmental-impact

[16] Nidhal Jegham et al., "How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference," arXiv 2505.09598, 2025. https://arxiv.org/pdf/2505.09598

[17] AZV AI, "Is Claude Sustainable? Anthropic's Environmental Report Card," May 16, 2026. https://azvai.com/en/is-claude-sustainable/

[18] Simon P. Couch, "Electricity use of AI coding agents," January 20, 2026. https://simonpcouch.com/blog/2026-01-20-cc-impact/

[19] AY Automate, "Fable 5 Pricing: $10/$50 per Million Tokens," August 2026. https://www.ayautomate.com/blog/claude-fable-5-pricing-explained

DISCLOSURE: 9% usage of my Fable 5.1 weekly limit of Max plan to produce. One of the links was broken and it cost 2% more to regenerate. 

The Mobile Genealogist: Creating source-based ancestor biographies with AI

I've always been a multi-tasker and I love having a tiny computer (aka iPhone) with me at all times. If I'm waiting anywhere, in an airport, in line at the bank, or even getting an oil change, I will use my phone to learn more about my ancestors using FamilySearch and AI. Here is my method and opinions on the two AI apps I primarily use. 

Recently, I compiled two AI biographies on my ancestor, Adam Weaver, using both Claude Fable/Sonnet 5 and ChatGPT Plus. I have already used both FamilySearch Full Text AI and Simple Search to fill in Adam Weaver's FamilySearch Sources page as much as I could. This isn't going to work if there aren't many sources listed for the ancestors so try this out on one who has several sources listed. Adam Weaver has 26 sources and I attached all 26 thanks to Full Text and Simple Search.

Step 1: Open FamilySearch and navigate to the ancestor you wish to create a bio for and click "Sources". Starting with the first source, click the hyperlink under the heading "Web Page". 

Step 2: A photo of the document will pop up. Click the "Download" icon and then the radio button by "JPG" only. 

The photo should download to your phone's camera roll.

Step 3: Click the icon in the upper right corner, it is an "i" in a circle for "information" and then scroll down and click the "copy" icon on the right of the Citation". A box will pop up confirming that the Citation has been added to the clipboard.

 

Step 4: Open the AI app you wish to use - I can only comment on ChatGPT Plus and Claude as those are the only ones I have used, and then add the downloaded record and ask it to transcribe the information for a biography on (ancestor name) and paste in the record source for the bibliography. Repeat for each source.

 

 AI apps will do different things for the biography: ChatGPT Plus will go in search of other sources. Claude Fable 5 will analyze the and sometimes interpret the 18th century lingo. Both do a fairly adept job at transcribing. I do still proofread as recommended by the Coalition for Responsible AI in Genealogy.

Caveat - remember to watch your usage when using Claude. High-powered models like Fable 5 and Opus use more tokens up than lower powered models like Sonnet. I toggle back and forth between Fable and Sonnet depending on the task. There are tips for saving your usage, and I have not yet hit any usage limits with ChatGPT Plus. Also, Claude has a 100 image per chat limit so check how many images you have before starting. Both my ChatGPT and Claude now have memory enabled so they can "remember" past chats and I have grouped my Weaver family chats into "Groups" and "Projects". The AI apps will notice things you might have missed. If you discover any useful tips related to this method, feel free to share in the comments section.  

 

STOP DUCT-TAPING YOUR ANCESTRY TREE!!! AI can help you, I'll show you how

I waver between anger and despair when looking at other people's scantily-sourced Ancestry.com trees. I probably should not look at trees that appear in hints because I find it about as gloomy as watching the news. I think some genealogy newbies see a source and just click it without even looking at it; like it's a matching game. Until it isn't. Like, HELLO BOLT DESCENDANTS - Elizabeth's last name is NOT "Brownlie" - check the records! That's a 120 year piece of duct-tape you are using to say that Elizabeth WEAVER who was born in 1722 was having a baby in 1840's Scotland! Grrrr....

Zach Weaver Simple Search hits

It is now my goal that when I die, I will have the most well-sourced Ancestry tree the world has ever seen. FamilySearch and AI are going to help me achieve that goal and I am going to show you how. 

Currently, I am in a rabbit hole I call "Westmoreland Weaver World" - the Westmoreland in Virginia and thank you God they never had a courthouse fire!!! That's three for three counties - Westmoreland, Fauquier, and Laurens, South Carolina. And thank you ancestors for living your lives in non-burned counties!!! And thank you LDS Church for filming the records of these non-burned counties which are free to access from the comfort of my home. (Thank you period for all filming, I am very grateful.)

Soooo, I have written before about FamilySearch Full Text Search and FamilySearch Simple Search which has now been incorporated into the Full Text search page - YAY! But let me show you why you need to use BOTH searches for sources. Our case study will focus on Elizabeth Weaver Bolt's brother, Zach. Not entirely sure what his name was as he appears as "Zachariah", "Zacharias", and his neighbor and Declaration of Independence signer, Richard Henry Lee refers to him as "Zachary Weaver". *Sigh* Ok, so yes, I did do a Full Text Search with all those name variations, but for some reason, it missed some records he was in until I pasted his FamilySearch ID number into Simple Search. Weirdly, the documents returned were not already attached to him, but the AI knew to return those.

Caveat - and I hate to say it out loud, but FamilySearch's transcriptions can be pretty garbled. And unfortunately as of this writing, they no longer allow you to edit and correct the mistakes. Oftentimes, I will use their "Summarize the document" link to see if I can use that to add to the comments section when attaching a document to a profile; sometimes it's a hit and sometimes it's a miss. Because I am in the Weaver World rabbit hole, I have been uploading the documents to Claude's Fable 5 AI and it transcribes and summarizes what in tarnation they were talking about three hundred years ago! I do follow the Coalition of Responsible AI in Genealogy's Disclosure principle to cite AI use and before I paste in something I will write: "Summary by Claude Fable 5 AI", for example. I download a JPG of the image from FamilySearch and then upload it to Ancestry's "Facts" by clicking "Add" on the Facts page. I then list it as a "Legal Document" and under "Description", I copy and paste in the FamilySearch source info which can be found by clicking the "Information" link on the document in FamilySearch.

As of today, my FamilySearch contribution score for 2026 is 373. Feel free to race me! 

Thrifty Token Tree Tips

Now that I have become a Claude convert, I decided to learn about the models and usage so I don't end up torching cash like Amazon did. Granted, I would run out of cash much faster than Amazon, but still. Hitting usage limits is super annoying! Found this GREAT article with all kinds of tips like "Chat uses less tokens than Co-work" and when you are working on something, Claude will re-read everything all over again which uses more tokens so you can save tokens on a Co-work session just by using this prompt suggested by Ruben Hassid:

"At the end of a Cowork session, prompt “Write a session-notes.md with the key decisions and next steps.” Next session, start with “Read session-notes.md first.”

As so often happens with technology, some of the tips Ruben provided four months ago in April 2026 are already out-of-date due to changes made by Claude. Like, I tried to change the setting to "concise" but it's gone. Yes, I know it partly moved to "Customize->Skills" but "Concise" would need to be manually added.The next burning million-dollar, er...I mean million-token question is which model do I use for what? Ruben gives this advice:

"Grammar checks, brainstorming, reformatting, short answers. Sonnet handles all of this at a fraction of the cost.

Opus + Extended thinking is your heavy machinery. Don’t use heavy machinery to move a chair.

My rule: if the task takes Claude less than 30 seconds to answer, it probably doesn’t need Opus. Switch models before you start the session. It takes 2 clicks."

Remember, his post was written before Fable launched so put Fable under the "complex task" and "token-torching" category in your grey matter.

And speaking of token-torching, I tried out Ruben's tips for NOT uploading images to Claude and torching my tokens that way - I have figured out a way that I could obtain the info I needed for Claude -AND- save tokens in the process. The two apps I used for this were FamilySearch and

the "Notes" app that comes standard in my iPhone. I regularly use the Notes app for voice transcription because it's pretty good. To do this test run, I first opened my FamilySearch tree to Adam Weaver where I know I have saved several pages of a Weaver book in the sources section of Adam's profile. The book is almost 100 pages and as I recently hit that 100 page limit in another chat, I have been thinking about capturing the information in this book without hitting that limit. So here's how I accomplished that and the first test run did not even change my usage percentages! Woo-hoo! Open FamilySearch on your phone, find the image of a source you want to save, click the "copy" icon on the transcription (circled in red in the left screenshot), then paste that into a new note on your phone. Caveat, I was copying and pasting a typed transcription so FamilySearch's transcription had little to no errors. If you are copying transcribed handwriting, there will likely be a higher error rate. I copy and pasted three transcriptions into the Note and then clicked "Select All" in the Note and then pasted that into Claude. I included the prompt in this image and it put the pasted text above my prompt but it still worked like a charm. 

I also have a TON of books and other genealogical resources I have imaged with my phone. So I tried an experiment and searched for "Adam" in my photos, found a page from the same Weaver book, held my finger down on it and pressed down until "Select all" came up, Copy, and then
pasted into the Sonnet 5 chat. It updated with the additional information just fine but as Ruben

points out, it uses more tokens when Claude has to keep re-reading through previous chats. It does also work to just copy and paste the text into the iPhone note, which to reiterate, saves usage over just constantly updating the chat.

Anyhow, between Ruben's tips (I recommend reading his whole post) and my tweaks for genealogy, I hope this saves you some tokens. Don't forget to proof the work for accuracy.

Fable 5 has me over a barrel

I'm sure Anthropic will like the title of this post as that's exactly what they want! I am a ChatGPT subscriber and wasn't much of a Claude user until Fable 5. I heard the glowing reviews both on podcasts and in the Genealogy and Artificial Intelligence (AI) Facebook group which is what prompted me to try it. 

Before Fable 5, I had a hard time fathoming all the media articles predicting our impending doom from AI. The AI models that we mere mortals have had access to has been dumbed down for us non-Silicon Valley Titans. Until Fable 5. 

I started using Fable 5 after the second trial extension was announced, got halfway through a project, and ran out of credits. (!!!) Which of course, forced me over the barrel to subscribe. I have since learned how to check my usage which I regularly do on the iPhone app while uploading documents on the PC. Here's how to check usage on the phone app: Open Claude, click the three lines in the upper left corner, then click the circle that probably has your initials in the lower left corner, then click "Usage". I do this after each document I add or large task on Fable. I don't do this as much for Sonnet 5, which I change to when doing lighter tasks so as not to use up so many credits. Steve Little recommends Opus 5 and says it's almost as good as Fable 5.

So just what exactly have I've been using Fable 5 for? Document transcription for one. Best output I have seen from an LLM yet! I also use it for compiling biographies on ancestors and even triangulating where my ancestors lived by uploading deeds, land plats, and modern maps.

There are a few drawbacks you should know about besides burning through credits: unlike ChatGPT, Fable (aka Claude) does not reference previous chats. While ChatGPT gets to know you across all of your chats, Claude gets amnesia outside of the chat and you have to start all over again. And speaking of "starting all over again" the absolute worst limitation is the 100 images per chat! I was stunted by this while building a biographical genealogical masterpiece! Made me very grumpy and I left Anthropic some very terse feedback about that. The catch-22 about this is because AI bots have driven up server costs for so many websites, those sites have had to institute "are you human captchas?". So obviously, Claude cannot access a LOT of sites so that means you have to do the accessing and downloading, and then upload it to Claude. 

So far though, Fable 5 has been worth every red credit! For instance, I found an 18th century Westmoreland County, Virginia voters' poll using FamilySearch Full Text for the House of Burgesses. My ancestor, Adam Weaver, cast two votes and both were marked "Obj". Fable 5 explained that meant the opponent of whom he voted for objected to Adam voting because he wasn't a landowner. I don't know how long it would have taken me to learn that on my own if Fable 5 had not pointed it out. It took me two days of uploading documents for yesterday's 18-page biography; but can you imagine how long it would have taken before AI?

Are we really looking into the eyes of that Pompeii victim?

In today's news, the Pompeii Archaeological Park in collaboration with the University of Padua announced an AI "reconstruction" of a Pompeii victim from Mount Vesuvius' AD 79 eruption. While artificial intelligence can be a more cost-effective and quicker way to do facial reconstructions than clay models, it is hoped that prompters will do every possible measure to insure their interpretation from remains is not altered or improved upon by an AI. Processes and tools should be peer-reviewed and published in academic journals. If you find that this particular case has indeed been published in a peer-reviewed journal, please share the link in the comments section below. While genealogists have photos that can be regenerated, photos produced with AI should always be disclosed and labeled. See the Coalition for Responsible AI in Genealogy's photo statement for more information.

FamilySearch does it again with "Simple Search"!

 As FamilySearch's biggest (self-proclaimed) fan, they have once again created an AI search tool that is a "must try" for genealogists! I learned about Simple Search from Jennifer Moulder at the 2026 Utah State DAR Conference. I thought I would try it out with searching for a document for my own Chapter Registrar; that she needed for a Prospective Member's application. To use the tool, sign in to your FamilySearch account, or if you do not have one, you need to create one to use the Labs feature. I then located the "Demasters" family that my Registrar needed a document for that tied the father to the son, and as you can see in the image, I just asked it to search for that using their FamilySearch ID numbers. It produced a list of documents within seconds, including one my Registrar could use, and she was quite pleased! Jennifer also mentioned that Simple Search will sometimes produce documents that Full Text misses.