However, Fable 5 does deserve a different conversation. It’s a new class of model - the first Mythos-class model Anthropic has released to the public.

It’s expensive. There are some use cases where it’s likely worth the 2x premium over Opus, and many jobs where it’s not.

I’ve had less time than expected to play with Fable 5, as I had some unexpected time sucks this week. But I want to do a short post to highlight some marketing use cases where Fable 5 performs better than other models, and explain why.

Do marketers need Fable 5?

I believe the current set of models can cover the bulk of marketing use cases. I think what’s not solved is the UX, workflows, and the integration of AI more seamlessly into how people work.

Opus 4.8 and GPT-5.5 can already handle the bulk of a marketer’s daily AI work. Drafting emails, writing content, brainstorming campaign angles, rewriting landing page copy, generating ad variants, summarising meeting notes, and building email sequences. Most marketing tasks don’t require huge context windows or multi-agent orchestration. In fact, I’d argue the newer models haven’t improved much on general marketing tasks over a couple of cycles. I still don’t believe AI is incredible at creative tasks, which is where marketers spend the majority of their time.

Fable 5 is actually worse for some of those use cases. Its writing is dense, over-detailed, written for engineers, not a copywriter. It’s not the model you’ll start using for your latest blog post.

It’s also slow and expensive. $10 per million input tokens, $50 per million output. It burns through tokens at roughly 2x the rate of Opus, even on similar tasks. Using it to rewrite a subject line is taking a bazooka to a water gun fight.

There is a simple way to think about this: if you’re sitting in the loop, going back and forth with the model, editing as you go, use Opus. It’s faster, cheaper, and writes better prose. Fable 5 is for large multi-step tasks where you set a clear outcome and just let it run.

When should you use Fable 5?

So what is Fable actually good at?

Three things, and they all share the same shape: big input, autonomous execution, finished output.

It holds massive context and synthesises across it. Fable has a 1 million token context window and, more importantly, it actually reasons well across that full window. You can feed it thousands of survey responses, dozens of documents, and an entire website, and it will find patterns across all of them. Opus can handle pieces of this. Fable holds the full picture at once.

It chains multi-step work without you having to babysit each step. This is the real shift. Previous models needed you in the loop, do this, now do this, now do this. Fable takes a goal and works toward it autonomously, planning its own steps, checking its own work, looping until the job is done. One example is a user pointed Fable at a GitHub backlog and told it to close irrelevant issues and write fixes for the rest. It just went through the entire list and produced mergeable code. Fable is truly an outcome-driven model.

It builds complete, working things end to end. A vibe coder’s dream. It seems incredibly powerful and builds working things in one shot. Finished, functional tools, calculators, dashboards, interactive experiences, full applications. A user described a complex multi-feature productivity app in a single prompt and got back a working product with zero bugs. Another built an entire 3D game from one prompt in 3-4 hours of autonomous work. Yes, the model worked for 3-4 hours with no human input needed.

The way to think about when to use it: if the job would take a smart person half a day of focused work across multiple sources, and the output is something more complex than a Google Doc, that’s a Fable job.

Here are three marketing jobs that highlight use cases worthy of Fable 5’s power

1. Customer & Competitive data to positioning insight:

This works great for Fable as it’s gathering a lot of context, doing multi-step tasks, and working towards an outcome.

In the world of AI, positioning is more important than ever. It’s so much harder to stand out and differentiate your company.

It runs four phases, after one setup step where it loads your internal data: real sales calls and chat logs.

Phase 1 is competitor context. It pulls every competitor’s homepage, pricing page and their “vs you” attack page, plus what G2 reviewers praise and punish them for. Verbatim quotes only, links required.

Phase 2 is the internal context. It audits your own site the same way, then mines your customer conversations for objections, competitor triggers, and the exact words buyers use. Their vocabulary, not yours. If you don’t have all of this data, just ask it to create synthetic data to use so you can see how the prompt works. You can do this easily by asking to research your brand, reviews, and ideal customer profile to fake these interactions. Not perfect, but enough to test.

Phase 3 is the red-team loop. The AI drafts positioning principles, then attacks its own work: Does it contradict our own reviews? Anything that fails gets killed, and the kills stay visible. I have a red-team skill for my AI-second brain, which works incredibly well.

Phase 4 is the framework. Positioning statement, three message pillars with proof, rewritten homepage copy, objection scripts for sales, and a flag list of problems that need a business decision, not better copy.

Here is the prompt because it’s big

The output is good you can see the artifact here, this is just first draft, I haven’t iterated (because of the time sucks).

2. An entire website audit with a single prompt:

This one is so simple, but yet so powerful. Fable 5 can reason across large context windows. That means you can do things like ask it to audit your entire website and look for issues with your copy, positioning and overarching web experience.

The prompt for this is a little more simple, you could certainly make this much better with a few iterations. The purpose here is to illustrate what a great use case this would be for Fable 5.

I want you to audit my entire marketing website for messaging consistency.

Start by crawling every page on [yoursite.com] — homepage, pricing, product pages, case studies, about page, careers, and any other public pages you find.

For each page, extract:

- The main headline and sub-headline

- The core value proposition being communicated

- The target audience the page seems to speak to

- All CTAs (buttons, links, forms) and what they promise

Then compare across all pages and flag:

1. Where the value proposition changes wording or emphasis between pages (e.g. homepage says “save time” but pricing page says “reduce cost”)

2. Where the target audience seems to shift (e.g. one page speaks to founders, another to enterprise teams)

3. Where CTAs don’t match the page intent (e.g. a case study page with a “start free trial” button instead of “see more stories”)

4. Any claims on one page that contradict claims on another

Output a single markdown report with:

- A top-level summary of the biggest messaging gaps (3-5 bullets max)

- A page-by-page breakdown with specific quotes and specific fix recommendations

- A priority list of what to fix first based on likely traffic and conversion impact

Be specific. Quote the actual text you found. Don’t be vague.

The results were incredibly helpful, you can see the full artifact here.

This is for illustration purposes, it only ran throw a subset of HubSpot’s website, the main pages. But, you can ask it to audit incredibly large websites and provide an incredibly rich overview of what improvements you need to make.

3. Vibe Coding on steroids:

Lastly, there isn’t a prompt to share here and i’m honestly still a little early with testing it to build things because I lost a bunch of time unexpectedly this week.

But, I’ve spoken to a bunch of people who’ve been deep working with Fable 5 this week and the end to end ability of it to code is amazing. Fable 5 is able to build complete, interactive, end to end apps in one or a couple of prompts. It’s much better at handling complex logic, UI, interactions, self-fixing, and deployment-ready code autonomously

Here are some of the best ideas I’ve seen and an example of something you could try build with Fable 5 that I believe would be a great way to engage your customers.

Some of the best examples of this:

1. Riley Brown (@rileybrown) built Rilable — an iOS app that lets you describe an idea and instantly generates full web + iOS apps in sandboxes (Lovable-style). It uses Convex for DB, Vercel AI gateway, and even handles AI-powered features. Built in ~10 prompts (~$210 tokens). He also cloned Lovable itself in just 2 prompts with extra features. Just to reiterate that, he cloned Lovable. Another example that marketing is all that really matters :)

2. Chris (@ChrissGPT) one-shot a stunning explorable 3D starship in Three.js. Features: working cockpit, crew quarters, planet views through windows, dynamic lighting, sleep/eat interactions, all self-optimizing to 60fps in-browser. We are moving towards a world where you’ll have 1:1 media, games, movies, music created just for you. This shows off complex interactive 3D coding, autonomous refinement, and performance tuning without manual fixes.

3. Maji Ammanath (@majiammanath) created a Call Copilot, it transcribes live calls and delivers real-time AI nudges/suggestions based on conversation context. Now extending to a browser version. This shows off real-time audio processing, context-aware agents, and practical productivity tools.

Ok, here is my much more feeble example lol. It’s not to show off the power of Fable 5, more an idea of an app to build with it. You could easily create a version of Spotify Wrapped for your customer base to show how they engaged with your product and provide recommendations on how to get more from it. MVP here.

Admittedly, this isn’t my best idea, I do blame writing this work at 1am. I will be back with proper examples illustrating point 3 (end to end apps) next week. In particular, I’m excited to plug my AI second brain into this model given that is a great example of reasoning across a huge amount of context.

A new skill to learn?

Most of the Fable 5 coverage you’ll read this week is about the model. Benchmarks, pricing, comparisons. All important. But, one thing that is really going to matter for people is - model selection is a new skill to learn.

Six months ago, you picked one model and used it for everything. That was fine because the differences between them were marginal. Now the differences are structural. Fable thinks differently from Opus. It’s better at different jobs. It costs different amounts. Using Fable for your content is not only a waste of money, it’s not very good. Using Opus for a full-site messaging audit is good, but Fable is much better if this is a priority task for you, and you’re willing to pay the extra money.

The marketers who get the most from AI over the next year won’t be the ones using the most powerful model. They’ll be the ones who match the right model to the right job, instinctively, without thinking about it. Opus for the drafting. Fable for the heavy lifting. Sonnet for the quick stuff that doesn’t need either.

That’s an entirely new skill to learn.

Fable 5 is included in your Claude subscription until June 22nd. After that, it moves to usage-based pricing, you pay per token through the API, so it’s going to get real expensive. This is the first time a frontier lab has released a model that isn’t included in the subscription long-term.

If you’ve been meaning to test any of the prompts in this post, the next ten days are free. After that, you’re paying $50 per million output tokens. Run the website audit. Feed it your customer data. Build the thing you’ve been putting off. Figure out whether Fable is worth the premium for your specific work, while it costs you nothing to find out.

Until Next Time,

Happy AI’fying

Kieran

Almost all of it is being written by a group of people who’ve already made their money, had great careers, and aren’t under the same pressures as people still trying to build one.

I want to tell you why this narrative is all wrong.

For the purpose of this post, I’m going to over-generalize workers into 3 categories:

The Coasters. They clock in, clock out, and do the basics. Work is a paycheck. AI is something they’ve heard about, not used or cared about. Going to be hard to compete with LLM models.

The Pros. They care about their craft. They do excellent work between 9 and 5. They close the laptop for dinner. They want a good career and a good life, a balance. Should be able to have a full and fulfilling career.

The Possessed. Work is identity. There is no line between work and personal life. They blur into the same thing. They’ll trade hobbies, relationships, and health to focus on career wins.

There is nothing wrong with any of these categories; there should be optionality for how people want to approach work vs. live.

Today, every piece of career advice you’re reading in 2026 around AI is from people in the Possessed category, for an audience the Possessed assume should become them.

Billionaires have written books on how AI will change how we work. Reid Hoffman published a book called Superagency: What Could Possibly Go Right with Our AI Future. In it, he says: “AI gives you superpowers for the entire world of information, navigation, decision-making, etc.”

He’s not wrong about AI.

The macro story is easy to tell. AI gets better. Workers adapt. New jobs replace old ones. People with high agency adapt.

But that ‘adaptation’ doesn’t come for free.

The micro story is different. DHR Global’s 2026 Workforce Trends Report found burnout sitting at 83%. Employee engagement collapsed from 88% to 64% in a single year. Nearly half of workers cited overwhelming workloads as the top driver of burnout. Forty percent pointed to long hours. Only 34% said their employer had clearly communicated how AI would affect their role.

Harvard Business Review has a name for the pattern now. A March 2026 BCG–Harvard study of 1,488 US workers coined the term “AI brain fry”, defined as “mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity.” Workers described a foggy, buzzing feeling. A mental hangover. Headaches. Slower decision-making. A sense they couldn’t tell if their AI-assisted work “made any sense” anymore.

It’s not just that, I’ve shared this graphic before, it’s hard to make the AI transition during your 9 to 5, it’s predominantly a night and weekend mission. There’s a period of time in your AI journey your productivity gets worse as you try to rebuild how you work around AI.

And the reality is, for a lot of people, your job and future earning potential are tied to that narrative. And that narrative is being driven by a group of people who likely don’t face the same pressures to make that transition because they’ve already made a lot of money in the last tech cycle.

The advice from your favorite YouTube channels, podcasts, or newsletters goes like this: read the papers, build the workflows, master the tools. Become irreplaceable. Be AI-native. Have agency. Simple.

It’s 100% correct. But it obscures the trade-offs.

The Possessed pattern isn’t an easy life. I speak from personal experience. My life bears the scars of it. You trade off time with family, relationships, health, hobbies, and more. To live that life today means you’re reading every newsletter. You’re consistently experimenting with the tools. You’re building a new skill stack outside of your job. It’s an investment. It will compound. But it’s a real sacrifice. I know.

I make that choice because it’s just who I am.

However, a lot of my best friends fit into the category of Pros, and I’m often envious of their more balanced lives, not so work-dominant.

Most Possessed people would make that trade again. That isn’t the issue. The issue is that the advice class doesn’t tell you a trade is happening. And in this instance, the outcome being sold is just to keep your job, to just stay relevant for the next tech cycle. It’s a much easier message to give when you’re comfortable, and if the trade fails, the outcomes for you aren’t that destructive.

What to do?

I don’t think the AI career advice needs to be so binary. Be in the ‘Possessed’ category or be left behind. That feels like an unfair choice for a huge group of people who don’t want to live that life.

It’s the binary nature of the choice that’s the real harm.

Choosing Pro should be a viable option. It’s not a weakness. It doesn’t mean you should be steamrolled by the AI wave. It’s great to build a life & a career. There’s no good reason that still shouldn’t be seen as a noble path to take.

Choosing balance isn’t settling. It’s not a weakness. It’s not a defensive crutch against AI. It’s a legitimate way to build a life, a career and life, in that order, that most people across history have aspired to. There’s no good reason it should stop being one now.

It shouldn’t be a consolation prize for people who decided against a career path being pushed to everyone.

The damage isn’t that some people choose Possessed. Plenty of us made that choice. Most would make it again.

The damage is the advice industry pretending Pros are a casualty class, who need to change their ways, have ‘agency’, instead of a category of people making a deliberate choice, a choice they should be able to make, and one where you can have a real, serious career inside of it.

And you can. Here’s the advice I’d give to those people, because everyone else is talking above you.

The 5 things you can do to fit AI transformation into your life, without burning that life to the ground.

1. Thirty minutes a day. Pick one workflow at a time. Most people pulling long hours around within AI are doing too much, and likely not applying any of it. Real progress shows up in the learning, application of AI, and seeing actual results. You can start by doing one thing well vs. doing twenty things poorly.

2. Build a moat, not a stack. Don’t chase every new tool. Don’t watch every new YouTube video. Learn one tool, go deep. Build one defensible workflow that drives a real, meaningful outcome. AI rewards depth of knowledge vs. shallow breath. I’d structure my learning on AI around that one principle.

3. Choose learning, not just titles. The biggest variable in your career right now isn’t your title. It’s whether your current role allows you to integrate AI into your day job, or you have to keep it as a side quest, furiously pulling late evenings and weekends. There are periods in life when you should optimize for the career ladder. Am I moving up? Am I nearer to the next title? Is my scope getting broader? More impactful. Now is the time to optimize for learning. Am I in a role that will help me cross the ‘AI chasm’?

4. Mute some of the ‘grind’ accounts. There is merit in following accounts that promote the ‘AI grind’, direct you to a new career path, and motivate you to get on it. But they can be overwhelming. Pick a couple. Tune out the rest.

5. Protect the off switch. Don’t. Burn. Out

Pro’s or Possessed is a choice. There are times in your life when you’ll alternate between them both. But painting the AI transition as a binary choice of being possessed or being redundant sucks. And all the advice is fixated on being ‘Possessed’.

Every career path is a trade. And that trade is based on your life as a whole. The advice on how to succeed with AI isn’t wrong. I’ve said similar things, nights and weekends, be proactive, have agency. But it’s a blinkered way to look at the world. I look at it that way because I tend to live in the ‘Possessed’ category. At times, I wish I didn’t.

But all career choices involve some trade-off. Some years back, I traded an incredible career opportunity because 6 months prior to that offer, I’d been in and out of the hospital with a severe health issue. The kind you question what kind of choices, if any, you’ll have in the future.

Everybody deserves to have a career to be proud off, in a way that can work for the life they want to build.

Until Next Time,

Happy AI’fying

Kieran

That’s a mistake.

If you integrate AI into how a team works, you’ll get minimal results. I believe you have to rethink how the team works, how it’s structured, and what its core skills are. The org chart stops being a tree of functions. It becomes a system that produces output. And a system needs a different shape than a hierarchy.

How does the system work?

Most people assume this model is about replacing marketers with agents. It isn’t.

AI should remove the coordination tax, the status meetings, the handoffs, the “circling back”, and concentrate humans at the points where they actually add value. The humans don’t empty out. Their work is elevated. Fewer people coordinating other people. More marketers are directing systems, agents, and practicing their craft.

There are four layers. The important detail is the arrow on the left of the diagram: every layer reads from the bottom one, and every layer writes its learnings back into it. It’s a loop, not a ladder.

Let me take the layers one at a time and tell you how they work. This system isn’t perfect. It’s a first draft. Much of it is doable today.

Layer 1: Context — the foundation everything runs on

This is the biggest unlock available to you right now, and it’s the one almost nobody has built.

Pre-AI, your intelligence lived in your people’s heads. What the market actually wants. How a campaign really gets shipped here. Which plays work and which quietly died. When someone left, it left with them. Context is a system that captures that intelligence, market knowledge, how the team operates, what’s working, and what isn’t, and makes it readable by every human and every agent above it. It updates automatically from the work you do.

Everything else reads from this layer and writes back to it. Get it right, and every layer above gets smarter. Get it wrong, and you’ve automated your own confusion. That’s the gotcha with this model. Marketing teams aren’t used to documenting these artifacts. It’s why remote teams have a unique advantage when it comes to AI. Everything they do has an artifact.

Some notes:

This is real work. This isn’t a Notion tidy-up. It’s real data engineering, CRM, analytics, support tickets, Slack, docs, call transcripts, brand guidelines, stitched into something queryable. Most orgs see “shared knowledge base” and budget for a wiki. The honest framing is: this is infrastructure, and infrastructure has a cost. For me, it’s an investment worth making.

Someone has to own it, or it goes stale like the wiki it replaced. The answer can’t be “the team.” It has to be a named owner, most likely AI Ops or RevOps, with senior strategists curating what’s true. Layer 1 is a new discipline/investment for marketing.

It has to be deterministic, not vibes. If the same question returns a different answer on Tuesday than it did on Monday, you don’t have an intelligence layer; you have a slot machine. That means declared definitions, not just stored learnings. “Campaign” means five different things across most companies. The substrate has to fix the vocabulary before it stores the knowledge.

Don’t ingest everything. Start with your 3–5 best artifacts. A lean, trusted core beats a giant pile of noise. Someone has to decide what’s a trusted input and what’s just chatter; the system slowly poisons itself with its own bad outputs. Layer 1 needs someone with taste who understands what should make it into the layer and what should be routed away.

The full layer for larger companies includes more than just marketing.

Customer knowledge actually lives in sales and CS. Product owns the roadmap. Finance owns the unit economics. If Context is marketing-only, it’s missing most of the company’s intelligence on day one. The refined version: Context is an enterprise asset that marketing reads from and contributes to, not a thing marketing builds in a corner.

Layer 2: Execution — agents at volume, humans at the bar

Execution is where the work gets made, and it’s a mix of agents running in parallel and a number of genuine craftspeople.

Why “craftspeople” and not “marketers”? Because good enough won’t cut it when everyone has the same models. AI can draft most of a thing. The gap between forgettable and unmissable is taste, judgment, and a real understanding of the customer, and that gap is widening, not closing.

Some notes:

Agents enable humans to do elevated work. Humans don’t bolt on the final 20% like a last screw. They frame the work before a single agent runs, the brief, the angle, the bar, and they exercise discernment all the way through. The split isn’t a sequence. It’s “humans own the brief and the standard, agents do the volume in the middle.” Put the human judgment at the start and the end, not just the end.

Some of this work doesn’t compress at all. A whole category of marketing isn’t an output an agent can draft; it’s a relationship. Distribution through events, partnerships, influencers, community, and the founder’s brand. Physical touchpoints, activations, retail, and the room, that were never digitally instrumented and so never enter the feedback loop. In people-heavy marketing, human discernment is the work. The model has to leave that work alone, not pretend that an agent will absorb it.

Layer 2 doesn’t mean agents replace humans. It means that, where possible, agents augment humans, allowing them to elevate their work. However, humans are determining the prompts, setting the guardrails, and enriching the work.

Layer 3: Orchestration — the role I think matters most

Your org isn’t a tree anymore. It’s a living system producing output. Someone has to run that system: make routing decisions, monitor what the agents are producing, make sure they’re reading the right context, and feed the results back into Layer 1.

This is the newest role and, I think, the most important one to get right.

It’s probably not one role. It could be two. There’s an Orchestrator who designs the system and decides what flows where, which decisions stay human, which get agent-drafted for human approval, and which run fully autonomously. And there’s an Operator who lives in the system day-to-day, QAs the agents, and improves them. Design versus run. Most teams will need both, even if one person wears both hats early on.

Where the real decisions live. The hard part of orchestration isn’t routing tasks; it’s the delegation logic. What’s allowed to run on its own? What gets escalated? What never leaves a human’s hands? That’s a deliberate design choice, not a default, and it belongs in this layer.

Who actually does this? From what I’m seeing, it’s one of three people: a RevOps lead, a GTM product manager who came up through MarketingOps, or an AI generalist.

Layer 4: Leadership — direction, not coordination

The new leadership job is to set direction for a system, refine it, and get output from it. You stop coordinating people and start steering an engine. You decide what the system should optimize toward, make the judgment calls it can’t, and feed strategy back into Context so the whole thing inherits your point of view.

Some notes:

Is the current CMO even equipped to lead this? One stat speaks to this: only around 15% of CEOs believe their CMO is AI-savvy. Most marketing leaders were selected for their ability to coordinate people and manage the org chart effectively. This model asks them to direct systems instead. That’s a genuinely different skill, and I won’t pretend it’s an easy switch. For some, it’s learnable. For some, it’s a hiring reset. If the leader can’t even see that the rebuild is needed, the rebuild happens to the team, not with it.

Why is leadership the human-only layer? Not because leaders shouldn’t use agents, they absolutely should, including for strategy and brand work. It’s human-only because someone has to own the taste the system optimizes toward and carry the judgment calls when the model is confidently wrong. You can automate the work. You can’t automate accountability for it.

Push back against this model

I heard the following two, which I felt were fair, and wanted to answer:

“Is this actually new — or is it just a good marketing process with better tools?”

Fair, but. A lot of this is what great marketing has always demanded: clear thinking, real craft, tight feedback loops. What’s genuinely new is the substrate underneath it. A shared intelligence layer that survives turnover was simply not possible before. The roles change as the unit of work shifts from “a person does a task” to “a person directs a system that does the tasks,” and, in turn, those roles make the system smarter. Today, the most intelligent marketer doesn’t make the entire team smarter just by working; in this system, they do.

“Where do entry-level marketers learn the craft if the middle concentrates?”

The fear is, if you remove the rungs people used to climb, the *entry* work where judgment is quietly built, you risk a generation that can operate agents but can’t tell good from great. My working view: the entry path shifts from “do the task” to “operate the system and build the context,” and IC fluency with AI has to come before you redesign the org, not after. But anyone who tells you this is solved is guessing. It’s one of the hard AI problems to solve.

Until Next Time,

Happy AI’fying

Kieran

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