Kelly Massad

CEO of Mainstay Digital | Emerging technology entrepreneur. Lifelong learner. Simplicity architect.

[SightBringer @_The_Prophet__]

⚡️Accenture is the professional-services pyramid getting repriced in public.

This matters because Accenture is one of the biggest embodiments of the old white-collar machine.

Sell trust at the top. Sell bodies in the middle. Sell offshore labor at scale underneath.

Turn enterprise confusion into billable hours.

Stretch complexity into programs. Wrap transformation in process, decks, migration teams, testing teams, PMOs, compliance layers, and managed services.

AI attacks that whole spread.

The old model made money because enterprises could not translate messy internal problems into software and workflows without armies of humans. Now agents can read code, document systems, generate migration plans, map processes, create tests, summarize requirements, write scripts, clean data, debug issues, produce decks, and compress the grunt layer that used to justify huge teams.

That does not remove the need for Accenture tomorrow. Big enterprises still need trust, governance, integration, politics, change management, compliance, accountability, procurement, and someone to blame when the transformation breaks.

But the margin story changes.

Clients will increasingly ask why a project needs 400 consultants when agents can do the first draft, the code migration, the documentation, the testing scaffolds, the data mapping, and the support workflow. The client may still hire Accenture, but the engagement gets smaller, more outcome-based, more discounted, and harder to staff with a giant pyramid.

That is the death of easy billable-hour inflation.

The most dangerous part for Accenture is the contradiction inside its own pitch. It has to sell AI transformation. But real AI transformation teaches clients that fewer human hours are needed. So every honest AI project weakens the old revenue engine.

That is why the stock is breaking.

The market is starting to understand that AI does not only disrupt software vendors. It disrupts the companies that monetized software complexity.

Consulting, outsourcing, staff augmentation, implementation partners, junior analysts, offshore delivery centers, enterprise IT services, BPO, corporate process labor. That whole belt is exposed.

Accenture survives because it has relationships, scale, trust, and access. The old multiple does not survive cleanly if investors conclude the labor pyramid is structurally impaired.

The deeper labor signal is brutal. Junior white-collar work is the shock absorber. The apprenticeship ladder gets thinner. Entry-level analysts, testers, documentation people, PMO support, data-cleanup teams, offshore implementation staff. Those roles become easier to compress, delay, offshore further, automate, or replace with a smaller number of higher-agency operators supervising agents.

That is the white-collar repricing map becoming visible on a giant ticker.

The next phase will be ugly across the sector: weaker hiring, smaller project teams, pressure on pricing, client demands for AI productivity discounts, more outcome-based contracts, fewer bodies per engagement, and more consolidation around firms that can prove actual implementation authority rather than just manpower scale.

The cleanest line:

AI is coming for the rent collectors of corporate complexity.

Accenture was one of the largest. Now the market is marking the model down.

[Alex Finn @AlexFinn]

I was wrong

I've been saying for months that open source AI models are 6 months behind frontier

They caught up. GLM 5.2 is as good as Opus 4.8

This changes everything. If you run GLM 5.2 locally no government can take it away. You become sovereign

And even if you run through APIs, its a fraction of the cost

The battlefield is different now. If open source is as good as frontier, and people have cheaper alternatives, governments can't be as quick to regulate. It will destroy the frontier AI labs

All of this is such a massive win for the people

If you are not paying attention to local models yet, you are making a tremendous mistake

[darkzodchi @zodchiii]

Google CEO, Sundar Pichai:

"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"

In 30 minutes he explains why the best engineers stopped writing code and started running agents.

Watch the interview, then save the exact setup below 👇

[NIK @ns123abc]

BREAKING: Microsoft exploring DeepSeek over OpenAI and Anthropic as Copilot Cowork moves to usage-based pricing

“We have users who do hundreds of tasks a week… the consequence is the costs can go very high...”

Jevons paradox

[AI Edge @aiedge_]

Fable 5 is banned, but the ENTIRE system prompt is leaked.

You can plug the exact Fable 5 system prompt .md into Opus, GPT-5.5, or any other LLMs.

Steal the system prompt here.👇

https://t.co/XGrwnZBsf9

[Szymanski @Szymansk_ii]

Next time you think of giving up, remember this photo of Elon in 2008.

[Morgan @morganlinton]

Okay, so this post ended up going viral, and I think it makes sense to build on it because I really didn't think it was going to get this much interest.

By Low effort is the alpha, I meant, you should run some tasks with Fable on low.

Low is often a forgotten effort level that people never use, and that kinda makes sense with previous models.

With Fable, low is actually very strong, in one benchmark, it out-performed both Opus 4.8 and GPT 5.5 high.

Also, people seem confused about low, I've seen a lot of comments from people thinking it will be slower. Just look at my screenshot in the post, it's faster, in fact, it's the fastest way you can run Fable.

Now here's the important point, while low is the alpha, i.e. low exists, it's insanely good, you should use it. This doesn't mean it's the only effort level you should use.

For me, I've find myself using Fable on Medium for most tasks, and I do think Medium is the effort level you'll want to use as a daily driver. Heck, even Fable itself says Medium is the effort level to use for "routine tasks"

So when would you use high, extra high, max, and ultracode? I'm still exploring this, and remember, Fable just came out two days ago so it's early, but, here's my take.

High effort is useful when you're thinking through architecture decisions, or need to go deeper into a bug or code review that requires more thought. I do think people will likely use high daily, but not as their daily driver.

Extra effort I've been using to check the work that high does, it can go a level deeper in thought and really check an architecture decision or make sure a gnarly bug is truly fixed across tricky edge-cases.

Max I think you'll use once in a blue moon. I would think of it as your friend, who's been coding for 30 years, she's an insanely talented coder who has seen so much and can pattern match on a lot of things you would never see. You won't use Max daily, probably not even weekly, but for a big architecture build, I would have Max review and give feedback, then use Medium and High to build.

Ultracode is an entirely different way of working with Fable, and I'm still experimenting with it, too early to share much feedback. Right now, be careful using it because it will burn through a crazy amount of tokens.

Anthropic says that Ultracode is "enabling a single prompt to fan out into dozens of parallel sub-agents to research, implement, and validate large-scale tasks simultaneously"

Okay, and that's all I got, I've used Fable for maybe 40 hours, and about 16 of those hours I've been asleep, and since I'm not using it all day every day when I'm awake, it's safe to say, I still have a lot more experimenting to do and a lot more to learn.

[Dan McAteer @daniel_mac8]

This is the best way to use Claude Fable in Claude Code without immediately hitting your limits.

1. Model set to Fable 5
2. Reasoning on Max
3. Instruct Claude to run a dynamic workflow where:
3a. Fable is the orchestrator
3b. Opus does the reasoning heavy phases

Fable is so overpowered that you don't need its intelligence for every step.

Let it orchestrate Opus or even Sonnet.

[Vibe Marketers HQ @vibemarketersHQ]

content that gets cited in ai overviews follows 3 simple rules:

1. direct answer in the first 150 words

2. every section tied to a real search question

3. every section complete on its own

here is the content brief framework for you to rank on search and get cited by AI:

[Tommy @Shaughnessy119]

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening

- Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs

- For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval

- On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect

- This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API

I shared more on this here: https://t.co/iZrqrCAIRW

- And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed)

- Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1.

More on open source inference provider raises here: https://t.co/7kf56P44yQ

- And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th

- Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free

- This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models

- Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months

- So go forward, what happens?

- I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter)

- It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122%

- With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it)

- The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses

- Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more

- This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

[How To Prompt @HowToPrompt__]

Google has published a paper that might end the transformer era.

For the last 7 years, every major AI, ChatGPT, Claude, Gemini, has been built on the exact same architecture: The Transformer.

But Transformers have a fatal flaw.

To remember context, they have to process every single word against every other word. It’s called quadratic complexity. As your prompt gets longer, the compute cost explodes.

The alternative is the old-school RNN (Recurrent Neural Network). RNNs are incredibly cheap and fast, but they have a fixed memory size. If you give them a long document, they get amnesia.

Until today.

Google researchers published Memory Caching: RNNs with Growing Memory.

And it fixes the biggest bottleneck in AI.

Instead of an RNN having a fixed, rigid memory that constantly overwrites itself, Google gave it a "save" button.

The technique allows the RNN to cache checkpoints of its hidden states as it reads.

The memory capacity of the RNN can now dynamically grow as the sequence gets longer.

They built four different variants, including sparse selective mechanisms where the AI actively chooses exactly which checkpoints matter most.

The results rewrite the rules of efficiency.

On long-context understanding and recall-intensive tasks, these new Memory-Cached RNNs closed the gap with Transformers.

They achieved competitive accuracy without the explosive, quadratic compute cost. It perfectly bridges the gap between the cheap efficiency of an RNN and the massive capability of a Transformer.

We have spent billions scaling Transformers because we thought they were the only way an AI could remember a long conversation.

But Google just proved we don't need to process the whole history every single time.

We just needed a smarter cache.

[Crypto Rover @cryptorover]

🚨 WHY IS EVERYTHING CRASHING TODAY?

Gold and Silver wiped out $1 TRILLION.

US stock market wiped out $1 TRILLION.

BTC and alts hit new yearly lows.

Here's what's pushing everything down:

1) Hawkish Fed

The market is now expecting a 25bps rate hike in 2026.

Just a few months ago, everyone was convinced of rate cuts.

This flippening scenario has changed the liquidity dynamics.

2) Markets de-risking

The next FOMC meeting is on June 17th with a new Fed chair.

Markets don't know exactly what Kevin thinks of the economy, and this is why investors are de-risking here.

3) Prolonged war

US-Iran peace deal hasn't happened yet.

Iran has even stopped negotiations with the US and has started the attacks again.

Oil is still above 90, which means inflation is still sticky.

All these things are bad for asset owners, which is why everything is dumping and bond yields are going up.

4) Too much euphoria

Stock parabolic rally has pushed the markets in euphoria.

And when that happens, a dump is always the next thing.

Because crypto is correlated with stocks, it's going down too.

And this isn't the first time.

Something similar happened in late Jan 2026 too when everything crashed together.

This is just the way of markets to wipe out all the greed before the next sustained move.

[CyrilXBT @cyrilXBT]

THE CREATOR OF OBSIDIAN JUST TURNED YOUR NOTE VAULT INTO AN AI AGENT.

Not a plugin.

Not an integration.

A full agent skills system that teaches Claude Code, Codex, and OpenCode to READ, WRITE, and REASON inside your Obsidian vault like a power user.

27,000 GitHub stars in days.

Here is what shipped at launch:

obsidian-markdown — wikilinks, embeds, callouts, properties, the full Obsidian flavor Claude now understands natively.

obsidian-bases — Claude can create .base files with views, filters, formulas, and summaries.

json-canvas — Claude builds .canvas files with nodes, edges, groups, and connections.

obsidian-cli — Claude controls your vault, develops plugins and themes directly from the terminal.

defuddle — strips web pages into clean Markdown so you stop burning tokens on clutter.

Install the whole thing in one line:

npx skills add https://t.co/mHDEvCNOIC

Then connect it to Claude Code, Codex, or OpenCode.

That is it.

Your second brain now has an agent inside it that understands how Obsidian actually works.

Not a generic AI that pastes text into files.

An agent that knows what a wikilink is. What a callout is. What a canvas is.

Built on the open Agent Skills spec. MIT license. Free forever.

The gap between people using Obsidian as a note app and people using it as an AI operating system just got wider.

Bookmark this before you open your vault today.

Follow @cyrilXBT for every build that changes how Obsidian and Claude work together.

[Kelly Massad @k3lmass]

Would you rather

Admit your infra can’t scale, AI budgets are getting slashed & ROI looks bleak

Or

Spin “models are getting so powerful they might birth Terminator” so we conveniently pause, you catch up, fix the financials & relaunch at 10x prices?

This is capex cosplay.

[Cointelegraph @Cointelegraph]

🚨 LATEST: Claude maker Anthropic is calling for a global pause in AI development, warning that models are approaching the ability to self-improve without human intervention.

[The Kobeissi Letter @KobeissiLetter]

It's official.

MicroStrategy, $MSTR, is now facing its biggest unrealized loss in history, at -$10.8 billion.

In other words, after 6 years of buying Bitcoin, the company is now down -17% on its position.

By comparison, the S&P 500 is up +116% over this same timeframe.

Since MicroStrategy sold 32 Bitcoin at $77,135 per coin, their positions has lost -$11.8 billion in value.

This puts MicroStrategy's stock, $MSTR, down -77% since its record high.

Bear market is an understatement.

[SightBringer @_The_Prophet__]

⚡️Bitcoin is the first asset in modern history whose main product is refusing to die.

That is why Hal Finney’s line is so powerful.

He saw the actual mechanism before almost anyone else.

Bitcoin does not become valuable because someone promises yield, growth, dividends, guidance, or political backing.

Bitcoin becomes valuable because it keeps surviving every attempt to dismiss, ban, corrupt, fork, ridicule, financialize, and bury it.

Every day it survives, the world has to quietly update.

At $0.01, the bet was “this is probably a toy.”

At $15, the bet was “maybe this survives among weirdos.”

At $1,000, the bet was “maybe this becomes a speculative asset.”

At $20,000, the bet was “maybe this becomes digital gold.”

At $60,000+, the bet became “maybe this is a permanent monetary rail.”

The price is just the visible surface of that probability update.

Bitcoin’s real chart is not price. It is death probability collapsing over time.

That is what skeptics still do not understand.

They think Bitcoin has to keep proving itself with new arguments. It doesn’t. Time is the argument. Blocks are the argument. Halvings are the argument. Failed bans are the argument. Exchange collapses that fail to kill it are the argument. Bear markets that fail to erase it are the argument. Governments regulating it instead of destroying it are the argument. BlackRock packaging it is the argument. States discussing reserves are the argument.

Bitcoin wins by making disbelief more expensive each year.

The real genius of Bitcoin is that it turned survival into compounding credibility. Most assets need management teams to execute. Bitcoin needs the network to keep producing blocks and refusing invalid rules. That sounds simple, but simple is the point. It is a machine that converts time, energy, and consensus into monetary credibility.

Fiat credibility decays because humans keep modifying the promise.

Bitcoin credibility compounds because the promise keeps refusing modification.

That is the entire civilizational split.

Every fiat system eventually asks for trust again. Trust us through this emergency. Trust us through this deficit. Trust us through this war. Trust us through this bailout. Trust us through this inflation. Trust us through this temporary measure. Trust us through this debt spiral.

Bitcoin says: verify.

That is why it terrifies the old system. It exposes money as a credibility game and then offers a version where the rules do not need a priesthood.

The hardest truth: Bitcoin is no longer trying to become legitimate. Legitimacy is slowly being forced to route through Bitcoin.

That does not mean the path is clean. There will be crashes, confiscation attempts, custody failures, regulation, taxation, ETF paper games, political attacks, quantum fear cycles, and stupid leverage blowups. None of that changes the core. Those are stress tests.

The longer Bitcoin survives the stress tests, the more absurd the zero case becomes.

The zero case was plausible in 2010.

It is now mostly a psychological defense mechanism for people who missed the compounding of monetary credibility in real time.

Bitcoin is not just an asset anymore. It is a running referendum on whether trust in code-backed scarcity can outlast trust in political restraint.

And the answer keeps getting clearer.

Every block says the same thing:

The promise held again.

[Bull Theory @BullTheoryio]

🚨Michael Burry just said Elon Musk and Nvidia's deal is built on fake numbers.

Burry published a detailed breakdown calling the entire structure "Fugazi", his word for fake.

He is alleging that billions of dollars in Nvidia chips are being hidden off balance sheets, and that American retirees are unknowingly funding the whole thing.

Nvidia, the world's largest AI chip company sold $5.4 billion worth of its most advanced GPUs, the GB200, to a company called Valor.

Valor is not a real operating business. It is a special purpose vehicle, a shell company created specifically to hold these chips and nothing else. Nvidia also invested $1.9 billion of its own money directly into Valor on top of the sale.

Those 100,000+ chips are now physically inside xAI's data center. xAI is Elon Musk's artificial intelligence company, the one that builds Grok. xAI is using every single one of those chips right now to run its AI models.

But here is what Burry is flagging.

Neither Nvidia nor xAI owns those chips on paper. Valor, the shell company holds legal title. That means $5.4 billion in GPU assets do not show up on Nvidia's balance sheet as inventory.

They do not show up on xAI's balance sheet as assets. They are legally invisible to both companies.

Nvidia gets to book the $5.4 billion as a completed sale and record it as revenue. xAI gets full use of the chips without owning them. And the risk disappears into a shell company in the middle.

Now here is where American retirees enter the picture.

Valor needed $3.5 billion in debt to fund this structure. Apollo provided it. Apollo is one of the largest asset managers on earth with $1.03 trillion under management and $834 billion specifically in private credit.

Apollo raised the $3.5 billion, packaged it into debt securities, and sold those securities to Athene.

Athene is Apollo's own insurance company. It sells fixed and indexed annuities, retirement savings products, to ordinary Americans.

When a retiree buys an Athene annuity, they believe their money is sitting in safe, stable investments. That money is now inside a structure funding Elon Musk's AI data center.

The numbers inside Athene are most alarming.

Athene holds $74.2 billion in reserves. It has moved $217 billion in assets into a captive insurer based in Bermuda, meaning those assets sit outside normal US insurance regulation and oversight.

Of the entire portfolio, 34.7%, equal to $103 billion, is classified as Level 3 assets.

Level 3 is an accounting classification that means there is no observable market price for these assets. No outside party can independently verify what they are actually worth.

The leverage sitting on top of those unpriced assets is 16 times.

Burry's says:
Every step of this structure is technically legal and publicly disclosed. But the entire thing was deliberately engineered across 8 to 12 steps to move credit risk off balance sheets and away from any market pricing.

- Nvidia books the revenue.
- Apollo collects the fees.
- xAI gets the computing power.
- And retirees sitting at the bottom of a 16x leveraged Bermuda insurance structure, holding $103 billion in assets with no market price carry the risk without knowing it exists.

[Vivek Sen @Vivek4real_]

LARRY ELLISON: AI IS RAPIDLY COMMODITIZING BECAUSE MOST MODELS ARE TRAINED ON THE SAME PUBLIC INTERNET DATA.

THE REAL COMPETITIVE EDGE ISN’T THE MODEL ANYMORE — IT’S ACCESS TO EXCLUSIVE, PROPRIETARY DATASETS.

THAT MAY BE THE ONLY MOAT LEFT.

[Mark Ajzenstadt @mardehaym]

None of this is satire.

→ A company spent $500,000,000 on Claude in one month because nobody set usage limits

→ Uber ran leaderboards ranking engineers by how much AI they used, not what they shipped

→ Uber burned their entire 2026 budget by April. Their COO said he can’t connect any of it to consumer features

→ A CTO told Axios employees were using enterprise AI to check the weather

→ Microsoft canceled most Claude Code licenses because the token bill spiraled

→ Companies are now laying people off to pay the AI bill. Not because AI replaced the work. Because the bill replaced the headcount.