Plutonic Rainbows

Google shipped Nano Banana 2 today. The model — internally Gemini 3.1 Flash Image — replaces the original Nano Banana as the default across Gemini, Search, and Ads. I've already added it to my image editor and made it the default there too.

The numbers matter here. Eight cents per image at 1K, twelve at 2K, sixteen at 4K. The original cost fifteen cents at 1K and thirty at 4K. That's roughly half, and the output is sharper. Text rendering — which the original botched reliably — now validates character by character before the final render. I tested it on watermark removal and text overlays this afternoon. Both worked first time.

The architectural shift underneath is more interesting than the price cut. Nano Banana 2 runs a reasoning loop rather than straight diffusion — plan, evaluate, improve — which explains why spatial relationships and multi-element scenes hold together in ways the original couldn't manage. Four times faster despite doing more work per image.

I'm not sure it fully replaces FLUX 2 Pro for everything. FLUX still handles certain structural edits with more precision. But at eight cents versus five, the gap is small enough that Nano Banana 2 will be where I start most jobs now.

Sources:

• Nano Banana 2 — Google Blog

• Google Launches Nano Banana 2 — TechCrunch

• Nano Banana 1 vs 2 Comparison — fal.ai

Freedom To Spend has a specific talent for finding records that fell through every categorical crack available. Jack Spence's Bamboo Sun — originally pressed in 1985 on the tiny Equator Music imprint — is exactly that kind of find. Flute, bongos, vocal harmonics drifting somewhere between choral and accidental, all produced with a sharpness that doesn't match the deliberately loose playing. Spence handled keys, drums, and flute himself. Bob Glaub — a session bassist who played on Jackson Browne and Lennon records — held down the low end. That combination shouldn't cohere. Mostly it does.

The cover tells you what territory you're entering — sepia, handmade, a figure that might be a bird or a body or both. I can't decide, and I don't think Spence could either.

Freedom To Spend's uncommon¢ series has quietly become the most reliable excavation project in experimental reissues. They don't surface lost tapes. They surface records that were pressed in small runs, sold a few hundred copies, and vanished because nobody knew where to shelve them. Forty-one years later, the shelving problem hasn't been solved. The music just found an audience that doesn't need it solved.

Microsoft, Alphabet, Amazon, and Meta will spend somewhere between $650 billion and $700 billion on AI infrastructure this year. Gartner projects worldwide AI spending at $2.52 trillion for 2026. These numbers have become so large they've lost the ability to mean anything. A billion dollars used to be noteworthy. Six hundred billion barely makes it past the earnings call.

The question that keeps nagging — the one the earnings presentations spend entire segments avoiding — is what, exactly, all of this money is buying.

The honest answer: cloud growth, mostly. Microsoft's Azure grew 40% year over year in Q2, with AI contributing about 16 percentage points of that growth. Google Cloud hit $17.7 billion in Q4 2025, up 48%. Those are real numbers. Real revenue. Real customers signing real contracts. However — and this is where the narrative curdles — the total direct AI revenue across the industry last year was roughly $51 billion against $527 billion in spending. That is a gap you could park a civilisation in.

An MIT study found that up to 95% of firms investing in AI have not yet seen tangible returns. Only 14% of CFOs report measurable ROI. Despite this, 68% of CEOs plan to increase spending again next year. The logic is circular: we must spend because our competitors are spending, and our competitors are spending because we must spend. Nobody wants to be the one who blinked and missed the platform shift.

I keep returning to the comparison with OpenAI's revenue panic. A company that raised hundreds of billions, has 800 million weekly users, and still can't make the economics work without plastering ads across a product its CEO called "uniquely unsettling" to monetise that way. The unit economics are a warning sign for the entire sector, not just one company.

What frustrates me is that the useful stuff gets buried. Barclays cut £2 billion through AI-driven efficiency programmes. Anthropic just embedded Claude into Excel and PowerPoint, which is boring and practical and probably where the actual value lives — in incremental productivity gains that never make investor presentations exciting. The flashy demos get the funding. The spreadsheet automation gets the results.

Analyst projections warn that Big Tech free cash flow could drop as much as 90% in 2026 as capex outpaces revenue. Ninety percent. That is not a rounding error. That's a structural choice to defer profitability on the bet that whoever builds the most data centres fastest wins the next decade. Maybe they're right. The companies making this bet have been right before — about cloud, about mobile, about search. But they've also been wrong before, about the metaverse and crypto and social audio and a dozen other things that consumed billions before quietly disappearing from earnings calls.

The money is real. The infrastructure is real. The revenue is not — not yet, not at the scale the spending demands.

Sources:

• Big Tech Set to Spend $650 Billion in 2026 — Yahoo Finance

• Worldwide AI Spending Will Total $2.5 Trillion in 2026 — Gartner

• Big Tech's AI Spend in 2026: Following the Money — Campaign US

• Microsoft Cloud and AI Strength Drives Q2 Results — Futurum Group

WeWork raised $22 billion, peaked at a $47 billion valuation, and filed for bankruptcy in November 2023. SoftBank alone lost $14.4 billion. The coworking company didn't fail because coworking was a bad idea — it failed because the money propping up its growth never connected to a sustainable business underneath.

The AI industry has a version of this problem, and it's getting harder to ignore.

Bloomberg recently mapped the circular deal structure connecting Microsoft, OpenAI, and Nvidia. The pattern is striking. Nvidia committed up to $100 billion to OpenAI. OpenAI's CFO Sarah Friar acknowledged that the money "will go back to Nvidia" in GPU purchases. Nvidia also backs CoreWeave, which buys Nvidia GPUs to build data centres, then sells capacity back to OpenAI. The money moves. Whether it actually goes anywhere is a different question entirely.

Tom Tunguz drew an explicit comparison to Nortel's vendor financing during the telecom bubble — a company that lent money to its own customers so they could buy its products. Nortel's revenue looked real on paper. Until it didn't.

WeWork had the same circularity, just cruder. SoftBank invested billions. WeWork used those billions to sign long-term leases on buildings it didn't need yet. The expansion justified the valuation. The valuation justified more investment. Adam Neumann called it a "community company" and a "state of consciousness." The market called it a $47 billion technology company when it was a landlord with a beer tap.

The AI version is more sophisticated. The companies involved are profitable elsewhere. Microsoft and Google have cloud businesses generating hundreds of billions. Nvidia sells real products to real customers beyond the AI startup loop. And unlike WeWork — which was locked into leases it couldn't escape when demand fell — data centres have repurposing options. You can run cloud workloads, render farms, scientific computing. I keep reminding myself of this whenever the parallel starts feeling too neat.

The differences matter. I'm not arguing this is WeWork reborn.

What I am arguing is that the circular financing pattern should alarm anyone who watched a bubble before. When revenue from Company A depends on investment from Company B, which depends on revenue from Company A, the system is more fragile than the topline numbers suggest. The spending gap — $527 billion in, $51 billion out — looks especially precarious through this lens.

OpenAI is projected to lose $14 billion in 2026 while seeking another $100 billion in funding. The company that started the whole frenzy still can't make the economics work, even after turning to advertising despite its CEO calling the idea "uniquely unsettling" barely a year earlier.

WeWork's original sin wasn't ambition. It was the gap between the story and the balance sheet — the willingness to let growth narratives paper over unit economics that never worked. SoftBank kept writing cheques because the alternative was admitting the previous cheques were wasted. The AI industry hasn't reached that point. But the circular deals, the vendor financing, the ever-growing commitments justified by ever-larger projected returns — the architecture of the bet looks familiar.

The hardware is different. The founders are different. The technology does more real things for more real people. But money that goes in circles still ends up back where it started.

Sources:

• How SoftBank's Bets on WeWork Totaled $16 Billion — Yahoo Finance

• AI Circular Deals: How Microsoft, OpenAI and Nvidia Keep Paying Each Other — Bloomberg

• Circular Financing: Does Nvidia's $110B Bet Echo the Telecom Bubble? — Tomasz Tunguz

• Facing $14B Losses in 2026, OpenAI Is Now Seeking $100B in Funding — R&D World

Anthropic shipped Claude directly into Excel and PowerPoint last week — not as a separate app, not as a browser tab you alt-tab to, but as a resident inside the file you're already working in. Generate slides from a prompt. Build pivot tables by describing what you want. Edit charts, rewrite bullet points, restructure entire decks. All native objects, not screenshots or static images. You keep editing after Claude finishes.

The Cowork launch bundled this with customisable "plugins" — pre-configured agents for financial analysis, HR, design, operations — and the stock market responded like someone had pulled a fire alarm. A software industry ETF dropped nearly 6% in a single session. IBM had already lost 13% of its market cap over an Anthropic blog post about COBOL the day before. Two positioning statements, two market convulsions.

Boris Cherny, who created Claude Code, told Fortune he thinks the title "software engineer" will start to disappear by the end of the year. Dario Amodei, Anthropic's own CEO, published an essay warning that AI will cause "unusually painful" disruption to jobs — a shock bigger than any before. When the people building the tool are this candid about the damage, the alarm feels earned.

But I keep snagging on specifics. The PowerPoint integration is a research preview. It doesn't support advanced features, loses chat history between sessions, and Anthropic themselves flag prompt injection risks from malicious templates. The Excel plugin handles pivot tables and conditional formatting, which is useful — genuinely — but the gap between "reformats a spreadsheet" and "replaces the analyst who understands what the numbers mean" is enormous.

The pattern is the same one playing out with AI-driven efficiency programmes in banking. Automation compresses the mechanical work. Headcount shrinks at the junior end. The people who survive are the ones who know which questions to ask, not which buttons to press. The spreadsheet jockey who builds one pivot table a week is not the person at risk. The person at risk is the one who builds fifty — because that volume is precisely the kind of repetitive, pattern-matching labour that an LLM handles well.

Anthropic is positioning Claude as the "default operational layer across enterprise workflows." L'Oréal, Deloitte, and Thomson Reuters are already deploying custom agents. The plugins are open-source and portable, which is a deliberate play against Microsoft's Copilot lock-in. Whether that matters depends on whether enterprises actually want portability or just want one vendor to blame when something breaks.

The job panic will continue. Some of it is justified. Most of it is aimed at the wrong targets.

Sources:

• Anthropic Pushes Claude Into Excel and PowerPoint — DNYUZ

• Claude in PowerPoint Arrives, But Beta Bugs Linger — WinBuzzer

• Software Engineers May Not Exist by Year End, Says Creator — Fortune

Forty million registered accounts. Roughly three million daily actives. That's a 92 percent no-show rate. Every time X does something stupid — and it does something stupid often — Bluesky gets a spike, people poke around, and most of them leave within the fortnight. The baseline nudges up slightly each time, which Bluesky's supporters treat as vindication. It isn't. It's a platform running on someone else's dysfunction.

The business model is the real problem. No ads, no subscriptions, no revenue. Twenty-three million in funding and around thirty employees burning through it. Leadership says they have multiple years of runway, which in startup language means they need another round before 2028. The AT Protocol is technically interesting — genuinely — but "technically interesting" and "sustainable" occupy different postcodes.

I signed up. I posted a few times. The timeline felt like a conference afterparty where everyone agrees with each other and nobody's buying drinks. Good conversations happen there, I'm told. They also happen on Discord servers and group chats and park benches. The question isn't whether Bluesky is pleasant — it is — but whether pleasant is enough to build something that lasts without eventually becoming the thing it defined itself against.

Sources:

• Bluesky Growth: Top 2026 Statistics — Sprout Social

• Bluesky sustainability discussion — Hacker News

IBM lost 13% of its market cap yesterday because Anthropic published a blog post about COBOL. Not a product launch. Not a working migration tool. A blog post, plus a playbook PDF. That is a remarkable amount of damage for what amounts to a positioning statement.

The claim: Claude Code can map dependencies across thousands of lines of COBOL, document workflows nobody remembers, and surface risks that would take human analysts months to find. All true, probably. LLMs are genuinely good at reading large codebases and producing structural summaries. Anthropic demonstrated similar capability with security analysis just last week. Reading code at scale is a solved problem, or close enough.

But reading COBOL is not the same as replacing it.

Anthropic's own blog is careful about this — more careful than the headlines suggest. The tool handles "exploration and analysis phases." Human engineers still define the target architecture, decide which business scenarios need manual validation, and manage the actual translation. Implementation happens one component at a time. The framing is explicitly incremental.

IBM's Rob Thomas pushed back with the line that "decades of hardware-software integration cannot be replicated by moving code." He's not wrong. COBOL systems running ATM networks and insurance claims processors aren't just code — they're code plus forty years of operational assumptions, regulatory compliance decisions, hardware-specific optimisations, and implicit business logic that exists in no documentation anywhere. The programme runs correctly because it has run correctly since 1987. Nobody alive fully understands why.

That's the actual problem. Not translating syntax from COBOL to Java. Any competent LLM can do mechanical translation. The problem is that COBOL systems encode institutional knowledge in their behaviour, and that knowledge evaporates the moment you rewrite the code in something else without first extracting every implicit contract the old system maintains with every other system it touches.

Claude can read your COBOL. It cannot read the forty years of institutional decisions baked into it.

The market reaction was absurd, which doesn't mean the underlying technology is useless. Reducing the cost of the analysis phase — the boring, expensive consultancy work of mapping what a system actually does — is a genuine contribution. That work currently keeps Accenture and Cognizant in business. If Claude Code can compress months of discovery into weeks, that changes the economics of modernisation projects that were previously too expensive to even start.

However. Cheaper analysis doesn't mean cheaper migration. The analysis was never the hard part. The hard part is testing, validation, regulatory sign-off, and the paralysing fear that somewhere in two million lines of batch processing logic there's a conditional branch that handles a scenario that occurs once every eighteen months and will bring down the payment network if it's missing from the new system.

No LLM solves that. Not yet.

Sources:

• How AI Helps Break the Cost Barrier to COBOL Modernization — Anthropic

• IBM Is the Latest AI Casualty — CNBC

• Anthropic Says Claude Code Can Help, but IBM Says It's Not That Simple — IT Pro

The dirty secret of AI web search has always been the plumbing. A model fires off a query, fetches half a dozen pages, dumps entire HTML documents into its context window, and then tries to reason over the mess. Most of that content is navigation bars, cookie banners, sidebar ads, footer links — noise that burns tokens and degrades the answer. Anthropic just shipped a fix that's almost embarrassingly straightforward.

Dynamic filtering lets Claude write and execute Python code to parse, filter, and cross-reference search results before they enter the context window. Not after. Before. The model looks at what came back from the web, writes a quick script to extract only the relevant pieces, runs it, and feeds itself the cleaned output. It's the kind of approach an engineer would reach for instinctively — treat the raw HTML like data, run an ETL step, then reason over the result — but it took until now for the models to do it themselves.

The benchmark numbers are significant. On BrowseComp, which tests finding deliberately hard-to-locate information across multiple websites, Opus 4.6 jumped from 45.3% to 61.6%. Sonnet 4.6 went from 33.3% to 46.6%. On DeepsearchQA — multi-answer research queries where you need to find every correct answer — Opus climbed from 69.8% to 77.3%. Average across both benchmarks: 11% accuracy gain while using 24% fewer input tokens.

That last part is the one I keep circling back to. Better and cheaper. Those two things almost never move in the same direction in this industry. Usually you buy accuracy with more compute, longer chains of thought, bigger context windows. Here the gains come from subtraction. Throw away the junk before you think about it, and the thinking gets better because there's less noise competing for attention.

The implementation leverages tools Claude already had — code execution, memory, programmatic tool calling — just wired together differently. It's enabled by default with the new web_search_20260209 and web_fetch_20260209 tool versions on the API for Sonnet 4.6 and Opus 4.6. You need the code execution tool included, which makes sense. The model needs somewhere to run those filter scripts.

I keep thinking about the context bloat problem I wrote about earlier this month — how connecting multiple MCP servers can balloon tool definitions to hundreds of thousands of tokens before an agent even starts working. Dynamic filtering attacks the same fundamental issue from the search side. The pattern is clear: the next round of capability gains won't come from making models smarter. They'll come from making models more disciplined about what they bother reading in the first place.

Sources:

• Improved Web Search with Dynamic Filtering — Anthropic

• Dynamic Filtering in Claude Web Search: Faster, Cheaper — Gend

• Anthropic Upgrades Claude AI Web Search Tools With 11% Accuracy Boost — Blockchain News

Calvin Klein launched a men's fragrance in 1981 that most people have never heard of. Not Obsession. Not Eternity. Not even Escape. Just "Calvin" — lowercase on the bottle, uppercase nowhere else — marketed with four words that constituted the entire advertising proposition: "Fragrance for Men." The Fragrance Foundation gave its packaging the 1982 Packaging of the Year award. Then the decade moved on, and Calvin Klein moved with it, and the scent that started everything quietly disappeared.

I spent a week researching this fragrance through primary sources — print ads, packaging photos, database reconstructions, corporate sale documents — and the thing that kept surprising me was how little survives. No press release from the 1981 launch. No named perfumer, just a house credit to IFF. No official note pyramid, just database approximations that disagree on whether the base includes oakmoss. For a brand that would soon become synonymous with cultural provocation, Calvin's debut masculine was almost aggressively understated.

The bottle tells you everything about the original intent. Deep blue-black pack, silver typography, rectilinear glass designed jointly by Klein and Fabien Baron. This was modernist packaging in a decade that hadn't yet decided whether it wanted modernism or maximalism, and Calvin bet on restraint. The industry noticed — that Fragrance Foundation award wasn't for the juice, it was for the object. The design language predates Baron's more famous work with Klein by nearly a decade, which means the aesthetic DNA of CK One and everything that followed was already present in 1981, just waiting.

The scent itself sits in the aromatic fougere space. Citrus-herbal opening — bergamot, neroli, chamomile, depending on which database you trust — into an aromatic floral heart of tarragon and orange blossom, settling on a woody-mossy base of patchouli, vetiver, musk, and possibly oakmoss. "Possibly" because no one has an official note list. Fragrantica includes mugwort in the top. Parfumo adds cinnamon leaf and vervain. The structure is consistent with what prestige men's fragrance looked like in the early 1980s: clean enough for an office, complex enough to signal intent, nothing that would overwhelm a room. Perfume Intelligence classified it as an "aromatic masculine fougere edt" and moved on.

What makes Calvin interesting isn't the composition — it's the advertising strategy that would later become the brand's entire identity. The 1981 print ad is product-led: bottle, carton, dark background, the "calvin" wordmark, and nothing else. No model, no lifestyle aspiration, no copy beyond the descriptor. By 1985, the execution had shifted entirely. An intimate couple-in-bed image with the same minimal overprint — "Calvin Klein" and "FRAGRANCE FOR MEN" — established the template that Obsession would detonate across every magazine in America the following year. The move from product shot to sensual lifestyle happened inside Calvin's short advertising run, and almost nobody talks about it because Obsession eclipsed everything.

I keep thinking about the ingredient list on a boxed aftershave that surfaced in a collector listing. S.D. Alcohol 39-C, water, fragrance, P.P.G.-20, methyl glucose ether. "Calvin Klein Cosmetics Corp., Dist., New York" with a Vol. '85 marking. Five functional ingredients and a corporate address. The entire identity of a prestige men's fragrance reduced to a label that could pass for industrial solvent. There's something honest about that — the gap between the image and the chemical reality laid bare in a way that contemporary fragrance marketing would never permit.

Calvin was discontinued around 1990 and reportedly relaunched in limited form worldwide on 4 October 1999. I remember buying a bottle in the UK in September 1990 before heading off to drama college. The evidence for both events is thinner than you'd expect. Basenotes says discontinued. Parfumo says it "disappeared" in the early 1990s. A Fragrantica editorial notes the 1999 relaunch claim but adds that the brand never confirmed it. Some Basenotes reviewers say the 1999 bottles were "not quite the same." Others say spot-on. Without analytical chemistry, the reformulation question stays unresolved, and the oakmoss issue — EU regulatory tightening around Evernia prunastri extracts — means any modern version would likely differ from the original regardless of corporate intent.

What happened around Calvin is more documented than Calvin itself. In 1989, Minnetonka's deal transferred Calvin Klein Cosmetics to Chesebrough-Pond's, a Unilever unit. The 1989 business reports note $158 million in sales, 82% domestic. Obsession, Eternity, and Calvin were listed as portfolio assets. By 2005, Unilever sold the entire Calvin Klein fragrance business to Coty for $800 million. Calvin the scent was long gone by then — a footnote in a deal worth nearly a billion, its name identical to the corporation that created it and therefore impossible to Google with any precision.

Vintage bottles surface on eBay occasionally. A boxed 50ml aftershave was listed recently at $185. Whether that reflects genuine market value or the optimism of a seller with a clean box and no comparable sales data is anyone's guess. The collector market for pre-Obsession Calvin Klein is effectively nonexistent as a structured category. It's just bottles that sometimes appear, priced by people who know they have something unusual but aren't sure what it's worth.

Nine years. That's how long Calvin existed as a live product in its original run. Nine years of quiet authority before Obsession rewrote the rules about what a Calvin Klein fragrance could say, and how loudly it could say it.