I think it’s fair to say the trajectory of all of this leads to bad actors creating the events and making decisions on policy that guarantees outcomes that they have conveniently made bets on. https://www.copingwithfootnotes.com/p/the-commodification-of...
Seems like a dashboard mode toggle to run in a dedicated terminal would be a good candidate to move some of this complexity Anthropic seems to think “most” users can’t handle. When your product is increasing cognitive load the answer isn’t always to remove the complexity entirely. That decision in this case was clearly the wrong one.
I am not a regular basketball fan. I love to play and I love to go to love games but I’ve never enjoyed the TV experience. Ben is right. If they released an NBA package where I could enjoy the game as they demonstrated in the Apple Store I would have bought the Vision Pro that same day.
I could “feel” the basketball dribble past me. I could hear the squeaky shoes as they rushed past. It was an experience I haven’t had since high school sitting on the bench :p
“They’re wirelessly charged, with a pad that can charge multiple bricks at a time.."
Did LEGO solve this problem and Apple didn’t? The Apple AirPower is what I’m referring to and it was a matter of physics that was the mighty hurdle Apple had to contend with. But they were also trying to pump out ~15w per device. These bricks will be measured in milliwatts per brick. But I’m curious if there is any additional information about this? How many bricks can be charged at a time? Can they be placed anywhere on the pad? (I hope so.) It would be great if specs were released. I would buy the pad alone just for charging other IoT devices.
Edit: It will not be usable by anything other than Lego Smart Bricks. It will use a proprietary or highly customized inductive standard designed specifically for the new Lego Smart Bricks.
There’s a tendency to treat the recent Uber revelations as uniquely egregious, but this framing misses the more important point: the egregiousness is not new, only the vector has changed.
Consider the nomenclature. Even during the Kalanick era, when “move fast and break things” was operational doctrine, you would not have seen internal naming this careless. I was there. We called drivers “supply” and riders “demand.” Clinical, yes, but accurate and apolitical. The language reflected the business model without editorializing about the humans within it.
What’s worth examining is not whether Uber engaged in questionable practices. Of course they did, and of course they still do. The real question is why the practices look different now.
Growth stage vs. profitability stage.
Uber in 2013-2016 was optimizing for growth. Uber in 2025 is optimizing for profitability. These are fundamentally different objective functions, and they produce fundamentally different behaviors. The perverse incentives remain constant; the tactics they generate do not.
Here’s the key distinction: growth-stage illegality and profitability-stage illegality carry asymmetric risk profiles. When Uber was in growth mode, the company had optionality. Infinite capital and public goodwill meant the growth team could deploy aggressive guerrilla tactics to enter new markets, absorb the legal consequences, and move on. The expected value calculation favored action.
Profitability-stage Uber has no such luxury. The levers available to a mature company fighting for margin are few, and they all point in the same direction: the humans. Drivers. The “assets.” When you squeeze there, you’re not circumventing a government. You’re directly degrading the livelihoods of your own platform participants. The reputational and regulatory exposure is immediate and personal.
This brings me to Spain.
When Spain blocked Uber from operating, we did not wait for lawyers to navigate the legal system. We shipped a technical solution. I watched this happen in real time.
Here’s what we actually built:
The goal was simple: keep the Uber app functional for Spanish drivers and riders despite the government blocking our server IPs at the network level. We needed a system that could rapidly distribute new, unblocked IP addresses to every app in the country without requiring an app store update.
The solution was a Lua interpreter embedded in the Uber app paired with a gossip protocol for peer-to-peer distribution. The Lua compiler allowed us to push executable code to the app dynamically. No app store approval needed. It was essentially a remote code execution backdoor into our own app, which was both brilliant and terrifying in hindsight. When a user opened the app, it would fetch and execute Lua scripts that contained the latest routing logic and server whitelist.
The workflow once it was live: when Spain blocked a batch of our IPs, our infrastructure team would publish a new IP whitelist. That list would seed into the gossip network, where each active Uber app became a node, sharing the updated configuration with other nearby apps. The propagation was exponential. Within hours, millions of devices had the new routing information. The Lua script would compile the updated whitelist and redirect all trip service requests to the unblocked servers.
The tech stack was essentially a censorship-circumvention system: Lua for remote code execution, gossip protocol for decentralized distribution, and a dynamically compiled IP whitelist that the app used to route around the blockade. Same playbook Tor uses for bridge distribution or how Telegram distributes proxy servers to users in Iran and Russia.
The Spanish government quickly realized they had exhausted their options. We forced the outcome, Uber was unbanned, and operations resumed legally.
Here’s the part that matters: it was illegal, but the illegality accrued to Uber’s benefit without harming users. Drivers kept driving. Riders kept riding. The Spanish government got cast as the obstruction, and Uber was welcomed back as the protagonist.
That’s the difference between growth-stage rule-breaking and profitability-stage rule-breaking. One makes you the hero. The other makes you a landlord squeezing tenants.
Just because they weren’t the first mover into predatory practices doesn’t mean they can’t say no to said practices. Each actor has agency to make their own operating and business decisions. Is Valve the worst of the lot? Absolutely not. But it was still their choice to implement.
This article gets the phenomenon right but the causation wrong: it's not "AI spending vs. AI replacing jobs". both are happening simultaneously, and they're causally linked.
The spending-revenue gap is real. Hyperscalers are projected to spend $300-550B on AI infrastructure in 2025[1] while generative AI revenue won't exceed $30-40B [2]. Amazon's capex jumped from $48B in 2023 to $84B in 2024 to a projected $100B+ in 2025[3], that's capital intensity doubling from historical norms of 11-16% to over 22% [4].
But here's what the article misses: this isn't financial desperation. When Amazon's CEO announces 14,000 layoffs and explicitly states that AI will enable "fewer people doing some jobs"[5], he's revealing the strategic logic — show me the incentives and I'll show you the outcome. Companies aren't cutting jobs despite AI spending; they're cutting jobs because they know AI spending will pay off.
To be clear, the article treats the spending-revenue gap as evidence of irrationality. But infrastructure buildouts always precede revenue: railroads looked insane before they transformed commerce, electricity grids consumed massive capital before delivering returns, the internet required enormous infrastructure investment before creating trillion-dollar companies.
What's different now is companies are pulling the future forward. If we take this article at face value which I can appreciate is a BIG “if” then AI is already automating 25% of tasks and delivering 10-55% productivity gains[6] so they're not waiting for AI to replace jobs organically. They're cutting headcount now to fund the infrastructure that will make those cuts permanent.
More broadly, this is rational capital reallocation in a winner-take-all race. Companies that don't build AI infrastructure won't gradually decline, they'll lose competitive positioning entirely. That's why Meta is using off-balance-sheet financing for a $27B data center[7], why Oracle is borrowing $25B annually despite already carrying 450% debt-to-equity [8]. They're all-in because the alternative is obsolescence.
The real story isn't "spending causes cuts" it's that AI infrastructure commoditizes human expertise, the complement to compute infrastructure. Companies are trading labor costs for compute infrastructure because they've correctly identified compute as the new moat. The job cuts aren't the price of spending on AI; they're the business model shift that AI enables.
The article is right that we're not seeing mass AI job replacement yet. But the job cuts are happening in anticipation of replacement, not as an unfortunate side effect of spending. That's not desperation just business strategy.
This reminds me of delirium tremens a bit. Same compensatory mechanism, different sleep process - or at least that's the pattern I'm seeing.
The MIT study shows CSF waves—normally a sleep-only process that flushes metabolic waste—intruding into wakefulness when you're sleep-deprived. Your brain is apparently so desperate for the cleanup that it forces the process to happen anyway. Cost: attention lapses.
From what I've read, delirium tremens during alcohol withdrawal seems to follow a similar pattern, except it's REM sleep intruding into waking consciousness instead of CSF flushing.
What strikes me is the system-level similarity here. Sleep normally maintains clean states: you're either awake (alert, reality-testing intact, no CSF flushing) or asleep (offline, dreams permitted, maintenance running). But when the system gets stressed enough—whether through sleep deprivation or the neurochemical chaos of alcohol withdrawal—it seems to start making desperate tradeoffs.
The brain apparently needs certain processes to run. Period. Total no-brainer! CSF flushing can't wait indefinitely. Neither can REM sleep, which serves its own critical functions. So when normal sleep architecture fails, the system appears to force these processes anyway, even though the conditions are completely wrong for them.
Maybe that's why the costs are so specific. CSF intrusion during wakefulness costs you attention. REM intrusion costs you reality testing, because REM is the state where your brain accepts impossible narratives without question. Same compensatory mechanism, different critical process forced into the wrong state.
What I find interesting is how the brain knows what lever it needs to pull and how it pulls it. Sleep deprivation forces waste removal. REM deprivation forces wakeful dream states; which might be a side effect not the actual goal. The brain seems to know what maintenance is overdue and attempts the repair, consequences be damned.
So many changes over the years and some of them might actually be decent but I wouldn’t have known about this had I not read this comment or accidentally triggered it in the future. Has Apple experimented with “micro” tutorials that can pop up if they detect the user is performing an action in an inefficient/deprecated pattern? I.e. if in Safari I navigate to all tabs by tapping at the bottom —> hamburger icon —> all tabs a one time modal pops up showing the ux pattern they recommend
Is it fair to say OpenAI is in a sense “washing” the money passing between Nvidia and Oracle? And instead of taking a cut in the traditional money laundering they are enjoying massive valuation gains?
When the fed investigates this does it matter if one of the 3 companies is not a publicly traded company?
I submitted this not only because of the significance of the fires but how law enforcement built the case largely from his digital footprint.
Less interesting and common evidence was used (e.g. video surveillance, cellphone data) but the inclusion of the alleged arson included their ChatGPT history, and his music history leading up to the fire.
He was also an Uber driver so my guess is they also subpoenaed this as well if they were this thorough.
I suppose this is as good of a place as any to piggyback off the topic.
Can anyone share their experience with 3D scanner tools like the hand wands? I’ve seen a few kickstarter campaigns for these devices and they appear to be a godsend for someone looking to make a functional print (eg. Replace a broken hose handle)
I imagine these lower-end devices get you ~80% of the way and higher end devices get you closer to 90-95% ignoring technique and size/intricacy of the object being scanned.
How much time does it take to edit the 3D files from these scanners by the way? My only point of reference is the 3D wand my dentist put in my mouth to make impressions for Invisalign haha. The hygienist had to spend ~10 additional minutes after the initial pass scanning my mouth going back over several spots that were not up to par but overall it seemed very straightforward and easy. Tap the area of the mouth to edit —> shove the scanner back in my mouth —> review (software gave a red/green status) —> if green (ie. passing) —> save and done.
lastly, any recommendations for a 3D scanner in any form factor?
Maybe one in a million is hyperbolic but that’s sorta the game with these attacks isn’t it? Registering thousands upon thousands of domains + tens of thousands of emails until you catch something from the proverbial pond.