The fact: This week will go down in the record books. On 29 May, Anthropic — the maker of Claude — announced a 65 billion dollar raise at a valuation of 965 billion dollars. It is now the world's most highly valued AI startup, ahead of OpenAI. To make that number concrete: it is more than the market capitalisation of Coca-Cola, Disney and Bank of America combined. Forty-eight hours earlier, on 28 May, they released Claude Opus 4.8: better benchmarks in coding, reasoning and collaboration; misaligned behaviours "substantially reduced"; and a fast mode now three times cheaper. In the same announcement, they confirmed that Mythos — their frontier model, capable of finding vulnerabilities in existing code — will be available across all accounts "in the coming weeks".

The Codito analysis: Three signals in the same week, one single message. The 965-billion valuation is not symbolic: that capital directly funds the acceleration that follows. Opus 4.8 arrives less than eight weeks after 4.7. Mythos is imminent. The release cadence is doubling, and every release adds a layer of capability — code, agents, reasoning, cyber — immediately available to any organisation ready to use it. The question for a leader is no longer whether AI is improving — it is improving faster than ever — but how long their organisation can remain unprepared without paying a visible opportunity cost.

What it means for you: In concrete terms: a competitor who adopts Opus 4.8 this week mechanically gains several months of operational lead over the one who waits for Opus 5.0. That gap is what really matters now — not the choice of model. And the condition for capturing it has not changed: having an organisation ready to absorb the value. Without that, you will watch each new version go by without being able to seize it, while others bank the gains.

The number of the week 965 billion dollars. That is Anthropic's new valuation after this week's 65-billion raise — an all-time record for an AI startup, and more than the combined market capitalisation of Coca-Cola, Disney and Bank of America. As a reminder: 95% of enterprise AI projects fail to deliver, according to a recent MIT study, and 80% of AI's economic benefits are already captured by 20% of companies. The capital now flowing into Anthropic will only amplify that double movement.

AI doesn't fail by accident: it fails by method. This week, I took the time to film the framework we use at Codito to answer a simple question — the one every leader we meet is asking: "Is my organisation ready to take the leap?"

The method comes down to five dimensions, a score out of 10 for each, and a clear sequencing rule — the one that tells you where to start if you want to save time. Here are the five areas we assess:

• Processes — are your critical processes mapped and legible, or do they live in your employees' heads?
• Teams — are your people curious and open to adoption, or paralysed by the fear of being replaced?
• Tools — is your stack open (APIs, protocols like MCP), or locked into ageing proprietary software?
• Data — is your data structured, centralised and reliable, or scattered across Excel files, emails and parallel databases?
• Governance — do you have an AI charter, a designated lead and clear rules, or are you letting shadow AI take hold?

And one golden rule: if any of the five scores falls below 3, you do not launch an AI project. You fix the weak link first. The near-universal sequence for doing so, in this order: Data → Governance → Teams → Tools → Processes. That is how value gets built — not the other way around.

The full video walks through each dimension, with what it concretely changes at 8 out of 10 versus 2 out of 10. Ten minutes, straight to the point:

▶ Watch the video on YouTube (10 min)

If you want your real score, measured across these five dimensions, that is exactly what we do every month as part of our Codito AI Partnership. The first meeting is free, and you leave with an initial diagnosis.

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To close this edition, we extend the framework from the video by zooming in on the dimension that determines whether all the others can even get going: Data. It is the weak link nobody wants to look at, because going back to it is costly, slow and barely visible. And yet it is the only one that truly decides.

Why Data is the number-one blocker

Whether it is Claude, GPT-5 or a dedicated business agent, AI only works on structured, centralised, reliable data. Without that, you will get plausible but wrong outputs: the agent invents, pads, fills the gaps with what it learned on average from the internet — not with what your company actually knows. The rule is final: if your Data score is below 3 out of 10, no serious AI project will succeed. And that is non-negotiable.

Three signs your Data score is below 3

If you recognise two of the following three situations, your Data score is probably very low:

• Your customer records live in three different places. The CRM, the accounting system, and an Excel file kept by a long-serving salesperson. None of them are synchronised. Nobody knows which one is authoritative.
• Your meeting notes, specifications and client feedback live as Outlook attachments. Searchable only by whoever took part in the conversation. Practically invisible at company level.
• Your internal know-how is written down nowhere. How you qualify an opportunity, how you price an unusual quote, how you handle a difficult client: all of it lives in the heads of four or five key people. It disappears with every holiday.

Three actions to take before any AI project

1. Choose THE source of truth for each business entity. One single canonical database for customers, one for products, one for projects. Everything else becomes a mirror.
2. Migrate to a structured, machine-readable format. CSV, SQL database, structured Notion, Airtable — the medium hardly matters; the goal is for an agent to be able to read every row, not just a human.
3. Define the update rules. Who changes what, when, and in which field. Without that, reliability erodes within a few weeks and everything has to be redone.

The work isn't glamorous. It is decisive. Restructuring your data will never headline your LinkedIn posts. No peer will congratulate you for migrating three Excel files into a single database. And yet it is this step — often invisible, often postponed — that will decide whether your 2026 AI project produces leverage, or merely noise. Opus 4.8, Mythos and the models that follow will accelerate this work: they will not replace it.