heywoods

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...

https://github.com/anthropics/claude-code/issues/24537

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.

https://www.hardwarezone.com.sg/incoming/tech-news-apple-s-a...

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.

Will HN be around in 10 years? I hope so.

Interesting that the smells they were able to trigger seem to be related to basic survival. Smoke bad. Rotting food bad. Fresh air good.

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.

-- 1.(Morgan Stanley: https://www.datacenterdynamics.com/en/news/morgan-stanley-hy...) 2. (Grand View Research: https://www.grandviewresearch.com/industry-analysis/generati...) 3. (CNBC: https://www.cnbc.com/2025/02/06/amazon-expects-to-spend-100-...) 4. (Cerno Capital: https://cernocapital.com/accounting-for-ai-financial-account...) 5. (CNBC: https://www.cnbc.com/2025/10/28/amazon-layoffs-corporate-wor...) 6. (PwC: https://www.pwc.com/gx/en/issues/artificial-intelligence/ai-...) 7. (Fortune: https://fortune.com/2025/10/31/metas-27-billion-bet-turns-ai...) 8. (The Register: https://www.theregister.com/2025/09/29/oracle_ai_debt/)

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.

[Polysomnographic studies from the 1960s-80s](https://pubmed.ncbi.nlm.nih.gov/7318677/) documented this. Patients in alcohol withdrawal exhibit what researchers call ["Stage 1-REM"](https://www.sciencedirect.com/topics/neuroscience/delirium-t...)—a hybrid state where wakefulness and REM sleep characteristics get mixed together. Right before full-blown DTs, [some patients hit 100% Stage 1-REM](https://link.springer.com/chapter/10.1007/978-1-4757-0632-1_...). The hallucinations appear to be [literally enacted dreams](https://www.sciencedirect.com/science/article/abs/pii/S01651...) occurring while technically awake. The sleep-wake boundary just completely breaks down.

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.

"They used advanced wireless technologies to read the cards dealt in each hand and then pass that information to the defendants and co-conspirators."

Can anyone take a guess at what this means?

I don’t know why I expected anything else with that url.

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?

No worries. I should probably make sure I have at least a token understanding of the topic cloud based architecture before commenting next time haha.

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.

Egress costs? I’m really surprised by this. Thanks for sharing.

Point scored!

Every reply to the GP has at least reassured me that I am still on HN. Pedantic. Knowledgeable. Opinionated. My kind of people :)

I’m building my first iOS app ever so I know it has much more to do with me not understanding Xcode but getting builds to succeed after making changes with Claude code has been a nightmare. If you or anyone have any tips, guides, prayers, incantations for how to get changes in one to not clobber the other and leave me in xproj symlink hell I would be so grateful.

Your threshold theory is basically Amara's Law with better psychological scaffolding. Roy Amara nailed the what ("we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run") [1] but you're articulating the why better than most academic treatments. The invisible-to-researchers phase followed by the sudden usefulness cascade is exactly how these transitions feel from the inside.

This reminds me of the CPU wars circa 2003-2005. Intel spent years squeezing marginal gains out of Pentium 4's NetBurst architecture, each increment more desperate than the last. From 2003 to 2005, Intel shifted development away from NetBurst to focus on the cooler-running Pentium M microarchitecture [2]. The whole industry was convinced we'd hit a fundamental wall. Then boom, Intel released dual-core processors under the Pentium D brand in May 2005 [2] and suddenly we're living in a different computational universe.

But teh multi-core transition wasn't sudden at all. IBM shipped the POWER4 in 2001, the first non-embedded microprocessor with two cores on a single die [3]. Sun had been preaching parallelism since the 90s. It was only "sudden" to those of us who weren't paying attention to the right signals.

Which brings us to the $7 trillion question: where exactly are we on the transformer S-curve? Are we approaching what Richard Foster calls the "performance plateau" in "Innovation: The Attacker's Advantage" [4], where each new model delivers diminishing returns? Or are we still in that deceptive middle phase where progress feels linear but is actually exponential?

The pattern-matching pessimist in me sees all the classic late-stage S-curve symptoms. The shift from breakthrough capabilities to benchmark gaming. The pivot from "holy shit it can write poetry" to "GPT-4.5-turbo-ultra is 3% better on MMLU." The telltale sign of technological maturity: when the marketing department works harder than the R&D team.

But the timeline compression with AI is unprecedented. What took CPUs 30 years to cycle through, transformers have done in 5. Maybe software cycles are inherently faster than hardware. Or maybe we've just gotten better at S-curve jumping (OpenAI and Anthropic aren't waiting for the current curve to flatten before exploring the next paradigm).

As for whether capital can override S-curve dynamics... Christ, one can dream.. IBM torched approximately $5 billion on Watson Health acquisitions alone (Truven, Phytel, Explorys, Merge) [5]. Google poured resources into Google+ before shutting it down in April 2019 due to low usage and security issues [6]. The sailing ship effect (coined by W.H. Ward in 1967, where new technology accelerates innovation in incumbent technology)[7] si real, but you can't venture-capital your way past physics.

I think we can predict all this capital pouring in to AI might actually accelerate S-curve maturation rather than extend it. All that GPU capacity, all those researchers, all that parallel experimentation? We're speedrunning the entire innovation cycle, which means we might hit the plateau faster too.

You're spot on about the perception divide imo. The overhyped folks are still living in 2022's "holy shit ChatGPT" moment, while the skeptics have fast-forwarded to 2025's "is that all there is?" Both groups are right, just operating on different timescales. It's Schrödinger's S-curve where we things feel simultaneously revolutionary and disappointing, depending on which part of the elephant you're touching.

The real question I have is whether we're approaching the limits of the current S-curve (we probably are), but whether there's another curve waiting in the wings. I'm not a researcher in this space nor do I follow the AI research beat to weigh in but hopefully someone in the thread can? With CPUs, we knew dual-core was coming because the single-core wall was obvious. With transformers, the next paradigm is anyone's guess. And that uncertainty, more than any technical limitation, might be what makes this moment feel so damn weird.

References: [1] "Amara's Law" https://en.wikipedia.org/wiki/Roy_Amara [2] "Pentium 4" https://en.wikipedia.org/wiki/Pentium_4 [3] "POWER4" https://en.wikipedia.org/wiki/POWER4 [4] Innovation: The Attacker's Advantage - https://annas-archive.org/md5/3f97655a56ed893624b22ae3094116... [5] IBM Watson Slate piece - https://slate.com/technology/2022/01/ibm-watson-health-failu... [6] "Expediting changes to Google+" - https://blog.google/technology/safety-security/expediting-ch... [7] "Sailing ship effect" https://en.wikipedia.org/wiki/Sailing_ship_effect.

https://archive.ph/OIgon

What Boeing plant was the aircraft assembled at where this failure occurred?

[flagged]

From the other article which shared the email domains found in the heap. Sorry in advance for the poor formatting.

---

Source: `https://micahflee.com/telemessage-customers-include-dc-polic...`

### I. Industry Breakdown

*Financial Services (Dominant):* This is by far the most represented sector. It encompasses a wide array of sub-sectors:

* *Investment Banking & Brokerage:* A large number of domains belong to global and regional investment banks, interdealer brokers, and brokerage firms. * Examples: `jefferies.com`, `morganstanley.com`, `cantor.com`, `tpicap.com`, `bgcg.com`, `rjobrien.com`, `clarksons.com` (shipping finance/brokerage)

* *Asset & Investment Management:* Numerous firms managing diverse asset classes for institutional and private clients are present. * Examples: `kkr.com`, `aresmgmt.com`, `pimco.com`, `nuveen.com`, `franklintempleton.com`, `apg-am.com`

* *Banking (Commercial & Private):* Major multinational and regional banks are included, covering commercial, private, and retail banking. * Examples: `jpmorgan.com`, `bbva.com`, `cibc.com`, `scotiabank.com` (and its numerous regional variations), `bradescobank.com`, `safra.com`, `standardbank.co.za`, `dbank.co.il`

* *Wealth Management:* Firms specializing in wealth advisory for high-net-worth individuals are visible. * Examples: `gentrustwm.com`, `boltonglobal.com`, `rohrpwm.com`

* *Cryptocurrency & Digital Assets:* A significant and growing sub-sector, with exchanges, trading firms, and investment managers focusing on digital assets. * Examples: `coinbase.com`, `galaxydigital.io`, `b2c2.com`, `hiddenroad.com`, `aminagroup.com` (formerly SEBA), `panteracapital.com`

* *Fintech & Financial Technology:* Companies providing technology solutions for the financial industry, including trading platforms and compliance tools. * Examples: `smarsh.com`, `telemessage.com`, `interactivebrokers.com`

* *Venture Capital & Private Equity:* A strong showing of firms investing across various stages and sectors, from early-stage tech to large buyouts. * Examples: `a16z.com`, `sequoiacap.com` (implied), `vistaequitypartners.com`, `lcatterton.com`, `ardian.com`, `tigerglobal.com`, `tcv.com`, `bitkraft.vc`, `blockchaincapital.com`

*Energy & Commodities:* This sector is well-represented by:

* *Trading Houses:* Global and regional commodity traders dealing in oil, gas, metals, and agricultural products. * Examples: `vitol.com`, `gunvorgroup.com`, `eni.com` (also integrated), `amerexenergy.com`, `amius.com`, `pvm.co.uk`

* *Energy Companies (Integrated & Exploration/Production):* Major oil and gas companies and related services. * Examples: `totalenergies.com`, `petrobras.com`, `marathonpetroleum.com`, `p66.com`, `aramcotrading.us`

*Government & Public Sector:* Primarily U.S. government entities, including:

* *Federal Agencies:* * Examples: `cbp.dhs.gov` (Customs and Border Protection), `usss.dhs.gov` (Secret Service), `dfc.gov` (Development Finance Corporation), `who.eop.gov` (White House Office)

* *Local Government:* * Example: `dc.gov` (District of Columbia Government)

*Technology (Non-Fintech Focus):* While many tech firms are Fintech-related, some general software and IT service providers are present. * Examples: `nice.com`, `nebari.com`, `vlmsofts.com`

*Consulting:* A smaller representation, often specialized. * Example: `soteriasolutions.us` (safety/threat management)

*Real Estate:* Investment and advisory firms in the real estate sector. * Examples: `eastdilsecured.com`, `digitalbridge.com` (digital infrastructure)

*Shipping & Logistics:* Companies involved in shipping brokerage and services. * Examples: `clarksons.com`, `mcquilling-energy.com`, `freightinvestor.com`

### II. Geographical Breakdown (Based on domain extensions and company descriptions)

* *United States (Dominant):* A very large portion of the entities are U.S.-based or have significant U.S. operations. This is evident from the high number of `.com` domains associated with American companies and the presence of `.gov` domains. * Major financial centers like New York and tech hubs in California are implicitly represented (e.g., `aresmgmt.com`, `kkr.com`, `a16z.com`, `morganstanley.com`).

* *Canada:* A strong presence, particularly Scotiabank and its various divisions, along with other financial and tech firms. * Examples: `scotiabank.com`, `scotiabank.ca` (implied), `cibc.com`, `bitbuy.ca`, `wonder.fi`

* *United Kingdom:* Well-represented in finance (banking, brokerage, asset management) and commodities. London's role as a global financial hub is evident. * Examples: `cantor.co.uk`, `pvm.co.uk`, `ubauk.com`, `hbluk.com`, `rmb.co.uk`, `amcgroup.com`

* *Latin America:* Several domains indicate operations or focus in this region, with Scotiabank having a particularly strong showing. * *Mexico:* `scotiabank.com.mx`, `scotiacb.com.mx`, `scotiawealth.com.mx` * *Chile:* `scotiabank.cl`, `larrainvial.com` * *Peru:* `scotiabank.com.pe` * *Colombia:* `scotiabankcolpatria.com` * *Brazil:* `br.scotiabank.com`, `petrobras.com.br`, `bradescobank.com`, `itaubba.eu` (European arm of Brazilian bank) * *Panama:* `pa.scotiabank.com`

* *Europe (excluding UK):* * *France:* `totalenergies.com`, `ardian.com`, `mbcfrance.com` * *Switzerland:* `seba.swiss` / `aminagroup.com`, `hnwag.com`, `itau.ch` * *Monaco:* `tyruscap.mc` * *Netherlands:* `apg-am.com` * Other European presences through global firms (e.g., `itaubba.eu`).

* *Asia:* Highlighting its role as a financial hub. * *Hong Kong:* `apg-am.hk` * *Singapore:* `apg-am.sg`, `gfigroup.com.sg`, `icap.com.sg`, `sg.pimco.com`, `traditionasia.com` * *Japan:* `mitsui.com`, `tullettprebon.co.jp`, `smbcgroup.com` * *Israel:* `dbank.co.il`, `fibi.co.il`, `opco.co.il`, `nice.com` * *Indonesia:* `miraeasset.co.id`

* *Middle East:* * *UAE:* `freightinvestor.ae`, `aramcotrading.us` (US trading arm of Saudi Aramco) * General presence of firms like Alpha Wave Global with strong ties to the region.

* *Africa:* * *South Africa:* `standardbank.co.za`

* *Global:* Many firms operate globally, even if headquartered in a specific country (e.g., `a16z.com`, `kkr.com`, `morganstanley.com`).

### III. Notable Trends & Observations

* *Dominance of Financial Services:* The sheer volume of financial sector domains underscores its significant role in this context. * *Globalization of Finance:* Many financial institutions have multiple country-specific domains (e.g., Scotiabank, PIMCO, ICAP/TP ICAP), reflecting international operations. * *Rise of Digital Assets:* Numerous cryptocurrency exchanges, traders, and VCs focused on Web3 indicate the growing institutionalization of this asset class. * *Concentration of Energy Trading:* A significant number of specialized energy and commodity trading firms are present. * *Venture Capital Focus on Technology:* Many VC firms listed are known for investments in technology and, increasingly, blockchain/crypto. * *Government Presence:* Inclusion of U.S. federal and local government domains suggests interactions with these regulatory or administrative bodies. * *Prevalence of `.com`:* Despite geographical diversity, `.com` remains the most common top-level domain. * *Personal Email Addresses (`gmail.com`):* The presence of a few Gmail addresses (6 emails) is minor but indicates not all communications are necessarily from official corporate domains.

---

Sounds like a great candidate for decking.

These are the kinds of policy/feature decisions around a product that infuriates me:

1. Because it’s fucking stupid if you think about this for more than 5 seconds. You can think of edge cases where this will be problematic immediately.

2. Some PM at Google is making mid 6 figures to come up with this simp brain decision that has devastating rippling effects to the developer community and trust while also fucking over the cash cow market, that is a mobile App Store, by stifling dev incentives to develop on your platform.

Amazing. I wish I could have been a fly on the wall for that meeting

Your intuition regarding the shift from vertical to horizontal integration is spot on!

Sam Altman, in a recent Stratechery interview, detailed parts of OpenAI's future strategy that align with your prediction — a persistent, personalized AI. He envisions users interacting with OpenAI not just through core products but also across other applications.

Altman described a key part of the strategy: "...we have this idea that you sign in with your OpenAI account to anybody else that wants to integrate the API, and you can take your bundle of credits and your customized model and everything else anywhere you want to go".

This system aims to create a portable AI experience and by virtue, would usurp the vertical software business model that has historically dominated the software economy. A horizontal play, that sits in the middle collecting their tidy sum of the pot will require a very compelling argument. That would require a low barrier to integrate for developers coupled with a value-add proposition that is meaningful and not possible for anyone other than the largest technology companies.

As you know, it’s sort of the Wild West of tech right now. OpenAI is looking to find a territory in the AI landscape and make their stake now, and I think this is the correct strategy. We have seen what being the first to market with a great product can do for the longevity and growth of tech companies - especially the consumer markets. They have the name recognition, forever embedded in the lexicon of the internet, and a great product vision that will lead to critical mass adoption that and what awaits them is the coveted moat, at least in the consumer market, that AI companies have been struggling to find out in the Wild West of AI.

Altman mentioned wanting users to "be able to sign in with your personal AI that's gotten to know you over your life". This sign-in would ideally carry "your memory and who you are and your preferences and all that sort of thing" across different integrated services.

The OpenAI SSO login will be the Trojan horse and later on the app developers will either be incentivized by OpenAI or compelled to integrate their products because of the compelling value proposition it would bring to bare with an integrated personalized AI assistant, complete with its memory and preferences.

Lastly, I suspect this is one of the driving motivations to become a consumer hardware company as there is little to no chance that current players (Apple, Google, Meta) would allow the same 1st party access to their internal API’s would be a requirement for what Altman has laid out for OpenAI moving forward.