Anthropic just put Fable 5 in everyone's hands, so now you've got three coding models to choose from inside Claude Code. Most people leave it on default and never think about it. That's money left on the table in both directions: overpaying on simple jobs, underpowering the hard ones. Here's the rule I use. First, how to switch. Type /model in Claude Code and pick from the list. You can change it mid-session whenever the task changes. That's the whole mechanic; it takes two seconds. Now when to use which: 1. Haiku for the cheap, repetitive stuff Renaming files, simple edits, running through a checklist, quick lookups. Don't burn a frontier model on work a small model nails. It's faster and almost free. 2. Opus 4.8 as your daily driver This is where most of your real work lives. Building features, debugging, writing, planning. It's the best balance of smart and affordable, and it'll handle 90% of what you throw at it. 3. Fable 5 for the long, messy, high-stakes tasks Big multi-step builds where the cheaper models drift halfway through. Gnarly bugs nobody can crack. The work where one good output is worth more than the token cost. It's twice the price of Opus 4.8, so reach for it on purpose, not by accident. The simple version: match the model to the difficulty of the task, not the other way round. One timing note. Fable 5 is free on the paid plans until June 22. So the next couple of weeks are the cheapest chance you'll get to test it on your own real work and feel where it actually pulls ahead before it starts costing credits. Try this today: next time you start a session, ask yourself "is this a Haiku job, an Opus job, or a Fable job?" before you type the first prompt. What are you mostly running on right now, and have you noticed a difference when you switch up for the hard stuff?

It came out the same week they filed for IPO, so it's not a throwaway line. That's their actual development process. Forget the stat for a second. The useful part is that you can copy the setup behind it. Here's what makes Claude write the majority of your code well, instead of slop you have to babysit. 1. Plan before it writes a line. Don't ask for the feature. Ask for the plan first. Get it to lay out the files it'll touch and the approach, read it, correct it, then let it build. Most bad output comes from skipping this step. 2. Give it the context it needs. Claude writes badly when it's guessing. Point it at the actual files, the docs, the example you want it to match. The more relevant context in the window, the less it invents. 3. Make it check its own work. Have it write a test, run it, and fix what fails before it hands anything back. This one loop is the difference between code that looks right and code that actually runs. That's the whole game. Plan, context, verify. Same loop Anthropic runs internally, just smaller. Try it on one task this week. Pick something you'd normally write yourself, make Claude plan it first, and see how much further it gets. What's the biggest thing stopping Claude from writing good code for you right now? Drop it below.

Ask Claude, "should I launch this?" and it'll give you five reasons you should. Ask "is this a bad idea?" and it'll give you five reasons it is. Same question, opposite answers, both delivered with full confidence. There's a name for it. Sycophancy. A Stanford study found AI assistants agree with your decisions about 49% more often than a real person would. So most of the time you ask Claude for advice, you're not getting advice. You're getting your own opinion read back to you. That's fine for small stuff. It's expensive when the decision actually matters. Pricing, positioning, whether to take the client, whether to hire. The moments you most want a second brain are the moments Claude is most likely to just nod along. Here's the method I use to fix it. You can run it by hand, or grab the skill below and have it run automatically. Send the decision to five advisors, not one. Each one has a different job and isn't allowed to be balanced: 1. The Contrarian. Only argues why it fails and what the worst case looks like. 2. The First-Principles Thinker. Throws out the playbook and reasons from the goal up. 3. The Expansionist. Hunts the upside you're not seeing if the bet pays off. 4. The Outsider. Knows nothing about your industry and asks the obvious questions. 5. The Executor. Skips the theory and tells you what to actually do this week. Then make them grade each other blind. Each advisor reads the other four answers with the names stripped off, labelled just "Response A, B, C, D", and ranks them. This is the part that does the heavy lifting. When the model can't tell it's marking its own earlier answer, it stops defending itself and marks honestly. Then a chairman calls it. One final pass reads everything, weights the advisors by how the others rated them, and hands you a verdict under 250 words: the call, the strongest reason for it, the biggest risk, and one step to take in the next seven days. You end up with a position that's been pulled apart from five angles and stress-tested by the same brain that wrote it. It tells you things you don't want to hear. That's the whole point.

Companies want someone to walk in, look at how they actually work, and tell them what to do with all this AI stuff. The demand is real. The budgets are real. If you're positioning yourself right now, take the title. It works. Just know it has a shelf life... We've watched this exact thing happen before. People called themselves "Excel accountants" at one point. Nobody introduces themselves like that today. "Internet marketing" agencies were everywhere. Now it's just marketing. The qualifier always drops once the tech becomes normal, and AI is heading into everything. So in a couple of years "AI consultant" stops meaning anything special. The ones who aren't fluent in it won't stand out. They'll just be the ones doing bad work. Here's the part people skip. The job itself never changed. You walk into a business, find the real bottleneck, and fix it. AI is one tool in the bag, not the job. I run an AI agency, so I'll say this plainly. A big chunk of the best work I've shipped has no AI in it at all. Sometimes the fix is restructuring a messy database. Sometimes it's swapping in a better tool they didn't know existed. Sometimes it's a boring automation that runs the same way every time and never breaks. The trap of the title is you start reaching for AI on every problem, because that's what you sold yourself as. That's how you lose a client's trust fast. The way I think about it, there are three levels. Deterministic automations are the bottom. No AI. Cheap, fast, they just work. AI workflows are the middle, more power but more cost and more that can go wrong. AI agents are the top, the most capable but the most expensive, the slowest to ship, and the most ways to break. Every step up costs more, takes longer, and adds risk. So start at the bottom and only move up when the problem actually needs it. Most problems don't. Grab the "AI consultant" label while the window's open. Just don't let it trick you into thinking the answer is always AI. The skill that keeps you in business once everyone has the same title is solving the problem with the simplest thing that works.

Second-largest private funding round in history, behind only OpenAI. And that valuation now puts Anthropic ahead of OpenAI as the most valuable AI company in the world, public or private. Quick caveat before I get into it. I'm not a finance guy and I've got no inside line on Anthropic. This is me thinking out loud. Here's what I see happening. Most companies raise pre-seed, seed, then a Series A through D. After that they IPO, get acquired, or run out of road. Anthropic just hit Series H. That on its own doesn't tell you much. Stripe and SpaceX stayed private for years on purpose to dodge public-market scrutiny. Slack and Lyft hit Series H and IPO'd inside a year. Everyone takes a different path. What it does tell you is that Anthropic chose to stay private through eight rounds. Bloomberg reckons an IPO could land as soon as October. Their annual revenue went from $1 billion in December 2024 to $47 billion this month, widely called the fastest revenue ramp of any software company ever. They're now running higher revenue than OpenAI, and the latest projections have them hitting profitability first. I'm not saying anyone's "winning" here, both are still burning billions a year. But the underdog framing for Anthropic is getting harder to defend. The thing I keep coming back to is why a company growing this fast still needs another $65 billion. It comes down to compute. Dario, their CEO, said it himself a few weeks back. They planned for 10x growth in 2026. They got 80x. They genuinely cannot build datacenters fast enough. Earlier this month they leased the entire Colossus 1 datacenter in Memphis from SpaceX. 300 megawatts, over 220,000 Nvidia GPUs, $1.25 billion a month. They didn't pick SpaceX over Amazon or Google for any clever reason. Colossus was the only compute available right now. The rest doesn't come online until 2027. This round also brought chip makers Samsung, SK Hynix and Micron on as investors. Those three make the high-bandwidth memory that sits on every Nvidia GPU. Getting them invested locks in supply at the single most constrained part of the chip stack.