{"id":155924,"date":"2026-07-07T15:06:36","date_gmt":"2026-07-07T23:06:36","guid":{"rendered":"https:\/\/xira.com\/p\/2026\/07\/07\/token-maxxing-this-pricing-movie-is-a-remake\/"},"modified":"2026-07-07T15:06:36","modified_gmt":"2026-07-07T23:06:36","slug":"token-maxxing-this-pricing-movie-is-a-remake","status":"publish","type":"post","link":"https:\/\/xira.com\/p\/2026\/07\/07\/token-maxxing-this-pricing-movie-is-a-remake\/","title":{"rendered":"Token\u00a0Maxxing: This Pricing Movie\u00a0Is A Remake"},"content":{"rendered":"

There has been a lot of\u00a0buzz\u00a0around AI token pricing lately, especially as agents create a multiplier effect, using an unpredictably large number of\u00a0AI resources known as\u00a0tokens<\/a>.<\/p>\n

Uber\u00a0burned through\u00a0its 2026 AI budget<\/a>\u00a0as\u00a0it\u00a0rolled out Claude Code to their developers.\u00a0The term\u00a0Token\u00a0Maxxing<\/a>\u00a0surfaced in the software engineering community,\u00a0where\u00a0coders were measured on how much AI they used in their job\u00a0without regard to efficiency or thought. Tokens have been subsidized\u00a0by foundation model businesses in some instances\u00a0to get users hooked, but that is ending.\u00a0\u00a0<\/p>\n

The legal industry\u00a0is beginning to feel some of this uncertainty, too. Legora\u00a0recently moved away from a\u00a0per-seat model and\u00a0now charges for its Agent Pro solution\u00a0based on consumption<\/a> and resource intensity.\u00a0This\u00a0pricing model follows the\u00a0lead of larger players like Anthropic and shifts the cost risk onto\u00a0users of Legal AI. That creates issues\u00a0with budgeting, forecasting, and using AI solutions efficiently.<\/p>\n

The concerns\u00a0are\u00a0understandable. As models become more capable, users\u00a0and agents\u00a0process more information, submit larger prompts, upload more documents, and run increasingly sophisticated workflows. Every improvement seems to encourage more consumption.<\/p>\n

But before we assume this is an entirely new problem, it is worth remembering that the legal industry has already lived through something remarkably similar\u00a0on\u00a0more than one occasion.<\/p>\n

The legal community has\u00a0seen this movie before.\u00a0\u00a0<\/p>\n

Comparisons have been made to\u00a0legal research pass-through charges\u00a0to clients. I want to go\u00a0way\u00a0back to the\u00a0Lexis\u00a0Search Unit.<\/p>\n

Law<\/strong>\u00a0Firms<\/strong>\u00a0Hated\u00a0<\/strong>the Lexis \u201c<\/strong>Search Unit<\/strong>\u201d<\/strong><\/p>\n

In the 1980s, online legal research was transforming the practice of law.\u00a0The original Lexis service was expensive, but it delivered enormous value\u00a0by surfacing cases that would otherwise have been missed when\u00a0researching\u00a0issues in print.\u00a0The core pricing was time-based\u00a0with some ancillary charges. For example,\u00a0printing\u00a0cases cost\u00a02 cents per line.\u00a0But the\u00a0fundamental pricing model was relatively\u00a0easy to understand. Lawyers knew that spending more time online would cost more money.<\/p>\n

Then Lexis incorporated\u00a0an additional variable in\u00a0its\u00a0pricing model called\u00a0Search\u00a0Units.<\/p>\n

Search\u00a0Units were very similar to\u00a0AI tokens. They reflected\u00a0the intensity of the computing resources consumed during a search. From\u00a0the Lexis view,\u00a0the model reflected their cost structure. But\u00a0how\u00a0could a user understand the resources their search would\u00a0take before submitting it?\u00a0The model transferred risk\u00a0to users without offering ways to manage it. This\u00a0was maddening, and the legal community hated it.<\/p>\n

Sound familiar? How does a\u00a0law firm know how many tokens an AI agent will consume?\u00a0<\/p>\n

There was another subtle\u00a0component\u00a0to the\u00a0Search\u00a0Unit model.\u00a0As more documents were added to the database, searches became more computationally intensive.\u00a0Lexis added new documents every day, and when the same search was run\u00a0a few weeks later, it would cost more\u00a0Search\u00a0Units.\u00a0There was a built-in inflation factor.\u00a0<\/p>\n

The same is likely to be true with\u00a0token-based\u00a0pricing. The more advanced the LLM, the more tokens it consumes, and the same applies\u00a0to\u00a0more\u00a0powerful agents.\u00a0<\/p>\n

Law firms\u00a0saw\u00a0their bills\u00a0rise under\u00a0Search\u00a0Units, and they should expect the same with\u00a0token-based\u00a0pricing.<\/p>\n

The\u00a0<\/strong>c<\/strong>oming\u00a0<\/strong>d<\/strong>emand for\u00a0<\/strong>p<\/strong>redictability<\/strong><\/p>\n

History\u00a0often rhymes and even\u00a0repeats itself. Rising consumption will create a backlash.\u00a0Assuming there is value in AI, the backlash won\u2019t be about cost. It\u00a0will be about the uncertainty of budgeting and planning. This is\u00a0especially\u00a0true\u00a0when\u00a0the investment community\u00a0is\u00a0focused on\u00a0the benefits of AI initiatives.\u00a0<\/p>\n

Usage-based pricing based\u00a0on\u00a0tokens will hit a breaking point.\u00a0Before we explore that, let\u2019s consider another question.<\/p>\n

Could\u00a0tokens\u00a0become the new\u00a0billable\u00a0hour for agentic work?<\/strong><\/p>\n

The billable hour has been the proxy for value in the legal profession for decades.\u00a0On the surface,\u00a0AI agents based\u00a0on consumption models\u00a0parallel\u00a0the\u00a0billable\u00a0hour.<\/p>\n

If clients can think of consuming a\u00a0lawyer\u2019s\u00a0time by the hour, what is wrong with\u00a0paying for an AI agent that\u00a0approximates\u00a0the work of a staff member? Could firms assign an hourly rate for AI agents\u00a0or\u00a0pass through token costs with a modest markup?\u00a0<\/p>\n

Perhaps. Law firms\u00a0can estimate a lawyer\u2019s billable hours and set expectations for a client.\u00a0But the inability to estimate\u00a0the cost of\u00a0an\u00a0AI agent creates challenges.\u00a0Changing outside counsel guidelines isn\u2019t always a simple task either.\u00a0If law firms can\u00a0offer\u00a0predictability for their AI agents, perhaps they can\u00a0convince clients to pay for their\u00a0direct\u00a0costs.\u00a0<\/p>\n

A more likely possibility\u00a0<\/strong>is a change in\u00a0<\/strong>vendor\u00a0<\/strong>pricing models<\/strong><\/p>\n

The Lexis\u00a0Search\u00a0Unit has many\u00a0parallels\u00a0to tokens.\u00a0So,\u00a0how did Lexis remedy the\u00a0Search\u00a0Unit problem?<\/p>\n

In the early 1990s,\u00a0Lexis began\u00a0offering\u00a0new\u00a0packages\u00a0with\u00a0clearer,\u00a0more\u00a0predictable pricing.\u00a0They modeled usage and\u00a0proposed\u00a0pricing\u00a0based\u00a0on\u00a0historical\u00a0growth patterns. The hook was a discount based\u00a0on a commitment to continue growing usage.\u00a0These\u00a0\u201ccommitment\u00a0pricing\u201d\u00a0packages were\u00a0among the first multi-year agreements for legal research, and they helped normalize custom pricing\u00a0that\u00a0varies\u00a0widely\u00a0from firm to\u00a0firm.\u00a0Most of all, it solved the budgeting issue by offering\u00a0predictability.<\/p>\n

I think it\u2019s reasonable to assume that\u00a0once enterprise usage patterns are established, token-based pricing will be replaced by predictable models that enable\u00a0enterprises to budget.<\/p>\n

The Lexis\u00a0commitment\u00a0pricing packages\u00a0illustrate\u00a0how this could evolve.\u00a0Lexis\u00a0offered other sweeteners\u00a0that\u00a0added value, and I think vendor negotiations for AI solutions could do the same. \u00a0<\/p>\n

The hidden\u00a0<\/strong>benefit<\/strong>\u00a0for vendors<\/strong><\/p>\n

Predictability in pricing\u00a0benefits\u00a0customers. Not only do vendors benefit\u00a0from\u00a0predictable revenues,\u00a0which provide investors with\u00a0comfort, but they also flip the incentive from \u201cmaxxing\u201d\u00a0consumption\u00a0to driving efficiency.\u00a0Once\u00a0revenue is locked in,\u00a0they\u00a0are\u00a0motivated\u00a0to\u00a0save, creating an opportunity\u00a0to reduce costs and improve margins.\u00a0<\/p>\n

As\u00a0Lexis\u00a0locked in revenues with commitment pricing, it also\u00a0benefited from\u00a0Moore\u2019s Law<\/a>\u00a0as computing costs fell.\u00a0<\/p>\n

AI vendors will\u00a0have an\u00a0incentive to deploy specialized, token-efficient models for specific tasks.\u00a0To the extent there is a Moore\u2019s Law for\u00a0AI, they\u2019ll benefit from that.\u00a0Deep Research might think less before providing answers, and\u00a0AI agents may come with built-in\u00a0throttles and guardrails to\u00a0make them more efficient or prevent a\u00a0digital \u201cfool\u2019s errand.\u201d<\/p>\n

Token consumption is a remake of the\u00a0<\/strong>S<\/strong>earch\u00a0<\/strong>U<\/strong>nit movie<\/strong><\/p>\n

Vendors, starting with OpenAI and Anthropic, need to grow revenue to stop losing money. Charging for tokens is one way they can do that\u00a0in the short term.<\/p>\n

Lawyers did not buy\u00a0legal research\u00a0because they loved\u00a0Search\u00a0Units. They bought legal research because they needed answers. Similarly,\u00a0lawyers will reluctantly pay for\u00a0tokens to achieve better results with AI solutions.<\/p>\n

History rhymes.\u00a0This is a transitional pricing model.\u00a0There will be a sequel to the token consumption movie.<\/p>\n

Budget predictability is the problem to be solved with\u00a0token-based\u00a0pricing.\u00a0Vendors will\u00a0eventually\u00a0have to\u00a0fix\u00a0this for customers while maintaining\u00a0a fair margin. Over the long term,\u00a0successful vendors\u00a0won\u2019t\u00a0charge for tokens.\u00a0They\u2019ll be the ones\u00a0who\u00a0get\u00a0customers\u00a0to\u00a0think about\u00a0value.<\/p>\n

AI was used in the creation of this article<\/em><\/p>\n

\n

Ken Crutchfield has over forty years of experience in legal, tax, and other industries. Throughout his career, he has focused on growth, innovation, and business transformation.\u00a0His consulting practice advises investors, legal tech startups and others. As a strategic thinker who understands markets and creating products to meet customer needs, he has worked in start-ups and large enterprises. He has served in General Management capacities in six businesses. Ken has a pulse on the trends affecting the market. Whether it was the Internet in the 1980s or Generative AI, he understands technology and how it can impact business. Crutchfield started his career as an intern with LexisNexis and has worked at Thomson Reuters, Bloomberg, Dun & Bradstreet, and Wolters Kluwer. Ken has an MBA and holds a B.S. in Electrical Engineering from The Ohio State University.<\/em><\/strong><\/p>\n

The post Token\u00a0Maxxing: This Pricing Movie\u00a0Is A Remake<\/a> appeared first on Above the Law<\/a>.<\/p>\n

\"\"
\n\t\t\t\t\t\t\tHand holding coins from lines, triangles and particle style design. Illustration vector\t\t\t\t\t\t<\/figcaption><\/figure>\n

There has been a lot of\u00a0buzz\u00a0around AI token pricing lately, especially as agents create a multiplier effect, using an unpredictably large number of\u00a0AI resources known as\u00a0tokens<\/a>.<\/p>\n

Uber\u00a0burned through\u00a0its 2026 AI budget<\/a>\u00a0as\u00a0it\u00a0rolled out Claude Code to their developers.\u00a0The term\u00a0Token\u00a0Maxxing<\/a>\u00a0surfaced in the software engineering community,\u00a0where\u00a0coders were measured on how much AI they used in their job\u00a0without regard to efficiency or thought. Tokens have been subsidized\u00a0by foundation model businesses in some instances\u00a0to get users hooked, but that is ending.\u00a0\u00a0<\/p>\n

The legal industry\u00a0is beginning to feel some of this uncertainty, too. Legora\u00a0recently moved away from a\u00a0per-seat model and\u00a0now charges for its Agent Pro solution\u00a0based on consumption<\/a> and resource intensity.\u00a0This\u00a0pricing model follows the\u00a0lead of larger players like Anthropic and shifts the cost risk onto\u00a0users of Legal AI. That creates issues\u00a0with budgeting, forecasting, and using AI solutions efficiently.<\/p>\n

The concerns\u00a0are\u00a0understandable. As models become more capable, users\u00a0and agents\u00a0process more information, submit larger prompts, upload more documents, and run increasingly sophisticated workflows. Every improvement seems to encourage more consumption.<\/p>\n

But before we assume this is an entirely new problem, it is worth remembering that the legal industry has already lived through something remarkably similar\u00a0on\u00a0more than one occasion.<\/p>\n

The legal community has\u00a0seen this movie before.\u00a0\u00a0<\/p>\n

Comparisons have been made to\u00a0legal research pass-through charges\u00a0to clients. I want to go\u00a0way\u00a0back to the\u00a0Lexis\u00a0Search Unit.<\/p>\n

Law<\/strong>\u00a0Firms<\/strong>\u00a0Hated\u00a0<\/strong>the Lexis \u201c<\/strong>Search Unit<\/strong>\u201d<\/strong><\/p>\n

In the 1980s, online legal research was transforming the practice of law.\u00a0The original Lexis service was expensive, but it delivered enormous value\u00a0by surfacing cases that would otherwise have been missed when\u00a0researching\u00a0issues in print.\u00a0The core pricing was time-based\u00a0with some ancillary charges. For example,\u00a0printing\u00a0cases cost\u00a02 cents per line.\u00a0But the\u00a0fundamental pricing model was relatively\u00a0easy to understand. Lawyers knew that spending more time online would cost more money.<\/p>\n

Then Lexis incorporated\u00a0an additional variable in\u00a0its\u00a0pricing model called\u00a0Search\u00a0Units.<\/p>\n

Search\u00a0Units were very similar to\u00a0AI tokens. They reflected\u00a0the intensity of the computing resources consumed during a search. From\u00a0the Lexis view,\u00a0the model reflected their cost structure. But\u00a0how\u00a0could a user understand the resources their search would\u00a0take before submitting it?\u00a0The model transferred risk\u00a0to users without offering ways to manage it. This\u00a0was maddening, and the legal community hated it.<\/p>\n

Sound familiar? How does a\u00a0law firm know how many tokens an AI agent will consume?\u00a0<\/p>\n

There was another subtle\u00a0component\u00a0to the\u00a0Search\u00a0Unit model.\u00a0As more documents were added to the database, searches became more computationally intensive.\u00a0Lexis added new documents every day, and when the same search was run\u00a0a few weeks later, it would cost more\u00a0Search\u00a0Units.\u00a0There was a built-in inflation factor.\u00a0<\/p>\n

The same is likely to be true with\u00a0token-based\u00a0pricing. The more advanced the LLM, the more tokens it consumes, and the same applies\u00a0to\u00a0more\u00a0powerful agents.\u00a0<\/p>\n

Law firms\u00a0saw\u00a0their bills\u00a0rise under\u00a0Search\u00a0Units, and they should expect the same with\u00a0token-based\u00a0pricing.<\/p>\n

The\u00a0<\/strong>c<\/strong>oming\u00a0<\/strong>d<\/strong>emand for\u00a0<\/strong>p<\/strong>redictability<\/strong><\/p>\n

History\u00a0often rhymes and even\u00a0repeats itself. Rising consumption will create a backlash.\u00a0Assuming there is value in AI, the backlash won\u2019t be about cost. It\u00a0will be about the uncertainty of budgeting and planning. This is\u00a0especially\u00a0true\u00a0when\u00a0the investment community\u00a0is\u00a0focused on\u00a0the benefits of AI initiatives.\u00a0<\/p>\n

Usage-based pricing based\u00a0on\u00a0tokens will hit a breaking point.\u00a0Before we explore that, let\u2019s consider another question.<\/p>\n

Could\u00a0tokens\u00a0become the new\u00a0billable\u00a0hour for agentic work?<\/strong><\/p>\n

The billable hour has been the proxy for value in the legal profession for decades.\u00a0On the surface,\u00a0AI agents based\u00a0on consumption models\u00a0parallel\u00a0the\u00a0billable\u00a0hour.<\/p>\n

If clients can think of consuming a\u00a0lawyer\u2019s\u00a0time by the hour, what is wrong with\u00a0paying for an AI agent that\u00a0approximates\u00a0the work of a staff member? Could firms assign an hourly rate for AI agents\u00a0or\u00a0pass through token costs with a modest markup?\u00a0<\/p>\n

Perhaps. Law firms\u00a0can estimate a lawyer\u2019s billable hours and set expectations for a client.\u00a0But the inability to estimate\u00a0the cost of\u00a0an\u00a0AI agent creates challenges.\u00a0Changing outside counsel guidelines isn\u2019t always a simple task either.\u00a0If law firms can\u00a0offer\u00a0predictability for their AI agents, perhaps they can\u00a0convince clients to pay for their\u00a0direct\u00a0costs.\u00a0<\/p>\n

A more likely possibility\u00a0<\/strong>is a change in\u00a0<\/strong>vendor\u00a0<\/strong>pricing models<\/strong><\/p>\n

The Lexis\u00a0Search\u00a0Unit has many\u00a0parallels\u00a0to tokens.\u00a0So,\u00a0how did Lexis remedy the\u00a0Search\u00a0Unit problem?<\/p>\n

In the early 1990s,\u00a0Lexis began\u00a0offering\u00a0new\u00a0packages\u00a0with\u00a0clearer,\u00a0more\u00a0predictable pricing.\u00a0They modeled usage and\u00a0proposed\u00a0pricing\u00a0based\u00a0on\u00a0historical\u00a0growth patterns. The hook was a discount based\u00a0on a commitment to continue growing usage.\u00a0These\u00a0\u201ccommitment\u00a0pricing\u201d\u00a0packages were\u00a0among the first multi-year agreements for legal research, and they helped normalize custom pricing\u00a0that\u00a0varies\u00a0widely\u00a0from firm to\u00a0firm.\u00a0Most of all, it solved the budgeting issue by offering\u00a0predictability.<\/p>\n

I think it\u2019s reasonable to assume that\u00a0once enterprise usage patterns are established, token-based pricing will be replaced by predictable models that enable\u00a0enterprises to budget.<\/p>\n

The Lexis\u00a0commitment\u00a0pricing packages\u00a0illustrate\u00a0how this could evolve.\u00a0Lexis\u00a0offered other sweeteners\u00a0that\u00a0added value, and I think vendor negotiations for AI solutions could do the same. \u00a0<\/p>\n

The hidden\u00a0<\/strong>benefit<\/strong>\u00a0for vendors<\/strong><\/p>\n

Predictability in pricing\u00a0benefits\u00a0customers. Not only do vendors benefit\u00a0from\u00a0predictable revenues,\u00a0which provide investors with\u00a0comfort, but they also flip the incentive from \u201cmaxxing\u201d\u00a0consumption\u00a0to driving efficiency.\u00a0Once\u00a0revenue is locked in,\u00a0they\u00a0are\u00a0motivated\u00a0to\u00a0save, creating an opportunity\u00a0to reduce costs and improve margins.\u00a0<\/p>\n

As\u00a0Lexis\u00a0locked in revenues with commitment pricing, it also\u00a0benefited from\u00a0Moore\u2019s Law<\/a>\u00a0as computing costs fell.\u00a0<\/p>\n

AI vendors will\u00a0have an\u00a0incentive to deploy specialized, token-efficient models for specific tasks.\u00a0To the extent there is a Moore\u2019s Law for\u00a0AI, they\u2019ll benefit from that.\u00a0Deep Research might think less before providing answers, and\u00a0AI agents may come with built-in\u00a0throttles and guardrails to\u00a0make them more efficient or prevent a\u00a0digital \u201cfool\u2019s errand.\u201d<\/p>\n

Token consumption is a remake of the\u00a0<\/strong>S<\/strong>earch\u00a0<\/strong>U<\/strong>nit movie<\/strong><\/p>\n

Vendors, starting with OpenAI and Anthropic, need to grow revenue to stop losing money. Charging for tokens is one way they can do that\u00a0in the short term.<\/p>\n

Lawyers did not buy\u00a0legal research\u00a0because they loved\u00a0Search\u00a0Units. They bought legal research because they needed answers. Similarly,\u00a0lawyers will reluctantly pay for\u00a0tokens to achieve better results with AI solutions.<\/p>\n

History rhymes.\u00a0This is a transitional pricing model.\u00a0There will be a sequel to the token consumption movie.<\/p>\n

Budget predictability is the problem to be solved with\u00a0token-based\u00a0pricing.\u00a0Vendors will\u00a0eventually\u00a0have to\u00a0fix\u00a0this for customers while maintaining\u00a0a fair margin. Over the long term,\u00a0successful vendors\u00a0won\u2019t\u00a0charge for tokens.\u00a0They\u2019ll be the ones\u00a0who\u00a0get\u00a0customers\u00a0to\u00a0think about\u00a0value.<\/p>\n

AI was used in the creation of this article<\/em><\/p>\n

\n

Ken Crutchfield has over forty years of experience in legal, tax, and other industries. Throughout his career, he has focused on growth, innovation, and business transformation.\u00a0His consulting practice advises investors, legal tech startups and others. As a strategic thinker who understands markets and creating products to meet customer needs, he has worked in start-ups and large enterprises. He has served in General Management capacities in six businesses. Ken has a pulse on the trends affecting the market. Whether it was the Internet in the 1980s or Generative AI, he understands technology and how it can impact business. Crutchfield started his career as an intern with LexisNexis and has worked at Thomson Reuters, Bloomberg, Dun & Bradstreet, and Wolters Kluwer. Ken has an MBA and holds a B.S. in Electrical Engineering from The Ohio State University.<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

There has been a lot of\u00a0buzz\u00a0around AI token pricing lately, especially as agents create a multiplier effect, using an unpredictably large number of\u00a0AI resources known as\u00a0tokens. Uber\u00a0burned through\u00a0its 2026 AI budget\u00a0as\u00a0it\u00a0rolled out Claude Code to their developers.\u00a0The term\u00a0Token\u00a0Maxxing\u00a0surfaced in the software engineering community,\u00a0where\u00a0coders were measured on how much AI they used in their job\u00a0without regard […]<\/p>\n","protected":false},"author":3,"featured_media":155925,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-155924","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-above_the_law"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/xira.com\/p\/wp-content\/uploads\/2026\/07\/iStock-1131788406-pmDP3b.jpg?fit=747%2C467&ssl=1","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/155924","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/comments?post=155924"}],"version-history":[{"count":0,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/155924\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media\/155925"}],"wp:attachment":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media?parent=155924"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/categories?post=155924"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/tags?post=155924"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}