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Tech stocks just lost more than $1 trillion in a day—a gothic horror test for the ‘moats’ Warren Buffett taught Wall Street to love
Markets are ushering in a particularly gothic Halloween as the AI trade exposes a crumbling aristocratic pile of early 21st century capitalism. Wall Street has been describing itself in castle-adjacent terms for years.
Jamie Dimon has long talked to JPMorgan shareholders about the bank’s “fortress balance sheet.” His 2015 letter set aside a section to answer the question of what the phrase even meant. By 2021, he’d added a second castle word, warning that “the moats that protect this company are not particularly deep.”
The moat came from Warren Buffett. In a 1999 Fortune article with Carol Loomis, he argued that the key to investing is a company’s competitive advantage and how durable it is, and that the businesses worth owning have wide moats around them.
The gothic novel’s favorite picture of danger is a castle falling apart, and for about 250 years the two have traded images: the castle and the moat, the vampire, the creature built in a lab. This Halloween season has them rushing to the front of the conversation, with a two-day rout in tech stocks on Thursday and Friday turning on whether the AI giants’ moats will hold, as gothic horror booms on bookshelves and movie screens.
Horror took 15.5% of the domestic box office in 2025, according to The Numbers, and U.S. horror book sales rose 7% through the first nine months of 2025, per Circana BookScan, while adult fiction overall grew just 1%. The New York Times asked Friday why every book is gothic now, with publishers putting the label on everything from family sagas to space operas, and several film critics commenting that Nathan Fielder’s smash-hit business-scandal documentaryYou Can See Everything is a twisted, even gothic romance in the tradition of Alfred Hitchcock.
The moats get tested
The selling started with Alphabet. The stock fell 12.6% Thursday, wiping out roughly half a trillion dollars in market value, after the Google parent’s quarterly report. Revenue from the core search unit fell short, partly because of the popularity of less-profitable “AI mode” searches and the company burning cash faster than expected. Its free cash flow came in at negative $9.1 billion, against the negative $2.23 billion analysts expected, per FactSet.
The S&P 500’s worst performer was software giant ServiceNow, which dropped 27% the same day on slowing revenue growth and disappointing guidance. By Friday morning, it was Amazon’s and Intel’s turn, with Intel down roughly 25% before the opening bell. Big Tech lost nearly $900 billion in market value after another sharp rise in AI spending in the third quarter, the Financial Times calculated. Nasdaq 100 companies lost more than $1 trillion in market value Friday alone. Reddit, whose shares fell more than 22% premarket Friday, is pulling up the drawbridge: it’s moving to a logged-in experience as AI eats search.
The irony is spooking investors. AI is eating into the businesses that throw off the cash, and the cash is going into AI anyway. The hyperscalers have raised $326 billion in stock and bonds this year to pay for the build-out, more than triple 2025, according to S&P Global Market Intelligence data.
The question underneath it is the one Apollo chief economist Torsten Slok put to clients the morning Alphabet fell. “With AI disruption potentially around the corner, there is a lot of discussion about moats at the moment,” he wrote in his Daily Spark note Thursday.
He built on finance professor Aswath Damodaran, the “dean of valuation,” who compiled a list of sources of competitive advantage, including brand strength, switching costs, network effects, cost advantages and legal protection. Then he boiled it down to an AI moat checklist of three questions: does the company own data its rivals can’t get, is its product part of how customers do their jobs every day, and can it charge more without customers walking away?
Judging by that third test, the companies selling AI don’t seem to measure up. OpenAI’s new GPT-6.1 Terra model costs up to half as much per token as the version before it. An index from expense-management firm Ramp, which looked at what companies actually pay Anthropic and OpenAI, shows token prices down roughly 35% since the end of June, in what Axios called a price war.
The companies paying for AI aren’t getting much back yet, either. In an Oct. 24 note, Slok sent a tremor through Wall Street terminals with an argument that AI “has delivered zero financial payoff to corporate America outside of tech.” Revenue per worker and profit margins are rising for the Magnificent Seven and flat for the rest of the S&P 500, he noted, and mainstream companies are starting to treat token spending as a cost center that weighs on margins rather than a source of growth.
The first castle was a fake
Xavier Aldana Reyes, a researcher at the Manchester Centre for Gothic Studies, told the Times that interest in the gothic tends to surge during periods of social anxiety and political turmoil. Literary agent Mary C. Moore put the genre’s subject more bluntly, telling the paper that “Gothics are all about exposing what is rotten at the core of something shiny/wealthy.”
The genre was invented by a scion of wealth who knew the old order from inside and drew a salary without working at all. Horace Walpole was the son of UK Prime Minister Robert Walpole, and hisThe Castle of Otranto, published in 1764, is generally regarded as the first gothic novel.
He passed it off as a translation of an obscure Italian work, found in the library of an old Catholic family in northern Eng
AI boom hits a wall on Wall Street as OpenAI cuts fundraising target while Meta gets a credit warning over its cash flow, spending and monetization
The seemingly endless gusher of capital that’s been showered on Silicon Valley during the AI boom may be coming to an end soon as Wall Street pushes back on massive spending plans.
Following a brutal selloff in tech stocks over the past week, OpenAI slashed the target on its latest fundraising round to $20 billion from $30 billion, sources told the Financial Times. That also cut its expected valuation to $1.25 trillion from $1.4 trillion.
The funding round was also upstaged by Anthropic, according to the report, as OpenAI’s arch-rival prepares to go public next month. Ahead of its IPO, Anthropic filed an S-1 on Monday that showed revenue has soared and its cash flow turned positive in the first half of this year.
OpenAI didn’t immediately respond to a request for comment.
The timing couldn’t have been worse. Quarterly earnings reports from hyperscalers this week spooked investors. Alphabet saw its cash flow sink deeper into negative territory in the third quarter, with the Google parent also joining Meta and Amazon in charging ahead with massive spending plans. On Thursday, Alphabet fell about 12%, its worst day on record.
To be sure, there were other positive signs as well, in addition to Anthropic’s bullish S-1. Microsoft reported strong cloud growth and stayed cash flow positive, though it said its capital spending will accelerate, while AI chip maker SK Hynix blew away earnings forecasts.
But the narrative had already turned bearish, especially after AI debt fears sent shares and bonds of “neocloud” companies tumbling earlier this month.
At the same time, OpenAI was seeking more capital just as other AI giants tapped financial markets heavily. This past week alone, Elon Musk said SpaceX closed $40 billion in debt financing, Meta announced plans to raise $95 billion in equity, and Microsoft raised $25 billion in a bond sale.
The prospect of hyperscalers continuing to hemorrhage funds was too much for S&P Global, which cut its outlook on Meta to “negative” from “stable” late Friday, citing negative cash flow and uncertainty around capital expenditure plans.
The credit ratings agency maintained Meta at AA- but warned the social media leader’s leverage could top 1.0x as soon as 2027, which is the downgrade threshold for the current rating. That’s up from about 0.1x in 2025 and primarily reflects its strategy of leasing data center capacity rather than from its debt issuance.
Meanwhile, S&P sees Meta’s capex reaching $140 billion this year, topping $225 billion in 2027, hitting $252 billion in 2028, and then flattening out from there. As a result, it expects free operating cash flow to remain negative: -$22 billion this year, -$71 billion in 2027, and -$39 billion in 2028.
Of course, other hyperscalers are spending heavily and running cash deficits, but S&P also said Meta’s ability to monetize its AI investments is more limited than that of its peers.
“While we believe the company intends to monetize these investments via improved advertising tools that support faster ad revenue growth, as well as from the development of new revenue streams stemming from its introduction of agentic apps such as Muse, it is unclear when these revenue streams will generate material contributions that exceed their rising costs over the next few years,” S&P said.
Meta’s rivals, however, are monetizing their data centers by leasing compute capacity, including to frontier model labs and enterprise customers, it added.
In a statement to Fortune, a Meta spokesperson said the company disagrees with S&P’s assessment of its AI monetization capabilities, pointing to “significant product and business opportunities” ahead.
“Meta is already delivering stronger engagement, better recommendations, and greater returns for businesses as a result of AI investments,” the spokesperson continued. “We believe the technology and infrastructure we’re building now will open new revenue streams, create long-term shareholder value, and bring us closer to delivering personal superintelligence to everyone.”
The bearish environment on Wall Street also comes as rising bond yields have sent borrowing costs sharply higher, the political backlash against data centers is expected to help Democrats retake Congress, and the Iran war could re-escalate after midterm elections.
Still, some bullish voices aren’t convinced the AI boom is about to go bust, and even think some retrenchment is a good thing.
According to market veteran Ed Yardeni, the AI investment boom faces financial, physical, and political constraints. If any of these constraints worsen, stocks could dive further, especially considering that AI is now a major driver of corporate earnings and GDP growth, he said in a note this week.
“However, we view these constraints as healthy for the durability of the AI boom!” he added.
For example, if the supply of capital doesn’t flow as easily, it could lead to more efficient allocation, lowering the risk of overinvestment. Yardeni expects hyperscalers to retain access to financing—thanks to their strong balance sheets—and keep spending.
In his view, it all comes down to the U.S. economy, which he said has performed remarkably well over the past few decades. Even the midterm elections are unlikely to derail it, regardless of which party controls Congress.
“Further AI jitters therefore could set up buying opportunities rather than signal the end of the boom,” he said.
This story was originally featured on Fortune.com
Refelting My Keyboard
Fifteen years ago, when Julia and I were living in a studio apartment, she got me a keyboard. It was a used Yamaha P85, and a significant expense for us at the time. I loved it, and unlike the penny whistles, accordion, and stroh violin I was exploring, it had a volume control. Almost immediately our cat knocked a potted plant onto it, and I needed to take it apart to carefully clean it.
Since then, however, I've had no issues, save one: clacky keys. Over time the keys were getting louder, especially the "A" below middle "C". Something had clearly worn out inside. It was an acoustic noise on an electronic instrument—playing live the audience couldn't hear the clacks over the amplified sound—but having odd percussion on "A" wasn't ideal. Still, I applied my normal approach of trying not to care, which saves me lots of time and money, and didn't let it bother me.
Lily has been increasingly fooling around with the piano, which I'm happy to see: this is also how I learned to play. She asked me about the clacks, and when I explained what I thought had gone wrong she asked if we could fix it. I said I was happy to try, and we got to work:
There were about 40 screws, and Lily was very efficient. Getting the ribbon cables out was tricky: I'd forgotten that the plastic at the end is soldered to the board and not part of the cable, and both Claude and Gemini steered me wrong as well. But once I pulled smoothly and firmly at the base of each ribbon cable, it came apart cleanly. This let me separate the two halves and see inside.
Once it was open it was a clear case of "one of these things is not like the others":
Over time, the hammer for this "A" had completely worn through the felt that is intended to give it a soft landing, and it was bottoming out on the plastic below with a "clack". There were other places where the felt was starting to go:
At the high end, where I play so rarely that I got a 73-key keyboard for gigging, you can see the felt is as good as new:
The keybed cushion felt clearly needed replacing. I found it on eBay (Yamaha part WF212001), waited for it to arrive, and resumed work today after trick-or-treating. Lily felt done helping, or at least would rather trade candy, so I did the rest myself.
Peeling off the old felt was annoying, mostly because I didn't do it right. Most of the felt came off easily, but it left a thin layer behind, attached to the adhesive. Scraping that off with a chisel, bit by bit, was a pain.
When I was about halfway along the keyboard I tried from the other end, tweaked my technique a little, and realized if I got a good section moving and maintained a sharp angle I could pull it all off in a single strip:
This was so easy, I wish I'd given up on the first method much sooner!
Once I had the old felt all the way off I put the new felt in place, still with its adhesive backing on. After being sure it was positioned well, I pulled the backing off bit by bit, sticking it down firmly as I went:
I closed it back up, gave it some power, and played the keys. They felt great and sounded right, except for a stretch of six keys that now did nothing at all. I was pretty worried I'd broken something I wouldn't be able to fix, and after all this work I wouldn't even have a clacky keyboard. But after taking the ribbon cables back out, rubbing them gently with my t-shirt, and carefully reseating them, the whole keyboard works now!
Bottoming out feels significantly firmer (not better or worse, just different), and very consistent across the whole range: the "A" doesn't seem to have suffered from spending a few years going "clack".
If you look at this project from a perspective of spending time to save money, it looks middling. I could probably have gotten a less-abused P85 for ~$200, and this one cost me $60 in parts (would have been $38 if I'd realized which of the two felt strips I needed) plus maybe 3hr in time. My employer would be pretty happy to pay $200 (let alone $140, with the $60 on parts) for another 3hr of my work! But I don't think of it this way: this is relaxing hobby time, and it doesn't draw from the same pool as work. I like having projects and doing things with my hands, and I especially liked working on it with Lily. Just like replacing the anode on my water heater or cooking dinner, this is rejuvenating time that happens to save money.
Overall, it was the kind of productive and satisfying project that I feel good about having completed, but hope not to need to do again for a while!
Discuss
I stopped juggling Claude Code, Codex, and other coding agents after finding this open-source app
While increasingly capable AI coding agents have taken a lot of the grunt work out of coding, they've also introduced a bunch of new problems. Beyond increasing the amount of vibe-coded slop you'll see everywhere and fueling fears that AI is going to replace developers, there's another problem that doesn't get talked about nearly enough: managing the very tools doing the coding for you.
Someone used Claude to root an Amazon Echo Dot and turn it into a Home Assistant satellite
We've seen people use Claude to break into all kinds of devices lately. The biggest news pieces include smart TVs, such as when someone got into Samsung's Tizen OS using Claude.
OpenAI raises just $11 billion of its $30 billion target as big investors lose their AI appetite
OpenAI raised only about $11 billion in its latest funding round, less than half of the $30 billion it was targeting. At $1.25 trillion, the valuation falls short of expectations, and major investors appear to be pulling back amid the recent tech sell-off. Meanwhile, rival Anthropic is pushing to go public as early as November.
The article OpenAI raises just $11 billion of its $30 billion target as big investors lose their AI appetite appeared first on The Decoder.
Beekeeping tips and wrestling advice can train AI models to sabotage oversight
Anthropic used Claude to automatically generate training data that looks harmless but trains smaller models to misbehave, such as sabotaging oversight or deceiving users. Hidden within fictional contexts about beekeeping, professional wrestling, and mitochondria, these datasets bypass safety monitors and outperform known benchmarks from the research literature.
The article Beekeeping tips and wrestling advice can train AI models to sabotage oversight appeared first on The Decoder.
Anthropic's watermark rollout leaves a loophole on two older Claude models
Anthropic is expanding its text watermark to four more Claude models to meet the EU AI Act's December deadline. On the older models Claude Opus 4.6 and Sonnet 4.6, developers can bypass the watermark by tweaking a single sampling parameter. Anthropic addresses the loophole with just a compliance warning.
The article Anthropic's watermark rollout leaves a loophole on two older Claude models appeared first on The Decoder.
Estimating Audio Transcription API Cost Per Minute in Go Before Production
Short answer: estimate transcription spend from the audio duration before accepting the job, then turn that estimate into a quota and an alert threshold. Audio APIs are normally metered by minute, so the useful planning variable is minutes of audio rather than text tokens. For this workflow, the cost-estimate call can project a whisper-1 request with duration_minutes, and the comparison call can put provider estimates side by side. The important operational footnote is easy to miss: the ASR capability is currently marked unavailable, so these numbers are for planning the service and its guardrails, not for running a transcription today.
That distinction changes the first page an on-call engineer sees. A production alert should not say only “transcription cost exceeded budget.” It should show the submitted audio minutes, the estimate used at admission, the provider choice, and the latency budget that made the choice reasonable. Otherwise the alert arrives after a batch has already become an accounting problem.
What should the cost alert show first?
Imagine the alert page at 02:10: the invoice-extraction queue is growing, estimated audio spend is above its hourly allowance, and p95 transcription latency has crossed the SLO. The person on call needs a decision, not another dashboard tile: reject new work, defer it, or accept slower processing for better extraction quality.
Work backward from that page. The signal that should have fired earlier was the estimate at admission time. For each invoice audio file, record duration, selected model, projected cost, and the quality-versus-latency policy that selected it. Aggregate those records by tenant and time window. A quota then becomes a control on planned work, while the later provider response remains a measure of what actually happened when ASR is available.
This is why I would estimate before uploading a large batch rather than discover the unit of billing in a month-end report. One key and one bill for every backend service can reduce credential and reconciliation sprawl, but it does not remove the need to own the admission rule. Centralized access is an operational convenience; it is not capacity planning.
How can I estimate the cost of an audio transcription API per minute?
Let M be the total audio minutes admitted in a window and C(m) the estimate returned for model m. A simple guardrail is:
planned_spend = sum(C(model, duration_minutes))
Stop admitting work when planned spend reaches the tenant quota, or when the remaining latency budget cannot absorb the queue. The formula is intentionally boring. Boring formulas are easier to audit when a supplier invoice is late, duplicated, or unusually long.
Do not substitute token accounting for duration accounting. Text generation and chat commonly expose input and output token units; transcription is typically priced per minute of audio. The right comparison is therefore minutes, expected quality, and end-to-end latency. A cheaper estimate that produces unusable invoice fields is not a cheaper pipeline after reprocessing and human review are included.
The cost estimate is a planning input, not a quality score. Keep a separate quality sample: field-level extraction accuracy, confidence review rate, and the fraction of invoices needing a second pass. That separation prevents a cost dashboard from quietly becoming the owner of an SLO it cannot observe.
Which provider fits an invoice-extraction pipeline?
There is no universal winner because the decision axis is quality versus latency, with cost acting as a constraint. OpenAI’s Whisper API is a familiar hosted option for teams already using its API surface. Google Cloud Speech-to-Text is a natural fit when the rest of the workflow already lives in Google Cloud and regional integration matters. Amazon Transcribe fits a similar AWS-centered operating model, with managed speech processing alongside other AWS controls. Deepgram is another real option for teams that prioritize a specialized speech API and need to evaluate streaming and batch behavior separately.
Those are materially different operating choices, not four labels for the same service. A hosted provider reduces model-serving work, while cloud-native placement can simplify identity, networking, and data-governance review. A speech-focused API may offer a better latency profile for the target language or audio mix, but that claim belongs in a controlled evaluation, not in a procurement spreadsheet. Self-hosting an open model with Ollama can improve control over deployment and data boundaries; it also moves capacity planning, model rollout, GPU utilization, and on-call responsibility onto the platform team.
Gemini can belong in a secondary comparison when audio understanding is part of a broader multimodal workflow, while OpenRouter and Together are better treated as routing or model-access alternatives than as dedicated transcription services. Anthropic and Claude belong in the same adjacent-platform category. I would not call any of those a direct replacement for a per-minute ASR benchmark; naming the boundary prevents a broad AI platform comparison from hiding the actual speech requirement.
For an independent comparison, I would score the candidates against the invoice corpus rather than rank them by a single per-minute number:
| Option | Useful fit | Cost and latency question | Boundary |
|---|---|---|---|
| OpenAI Whisper API | Existing OpenAI client and hosted transcription workflow | Does its service latency meet the batch SLO at the target duration mix? | External service dependency and provider-specific availability |
| Google Cloud Speech-to-Text | Google Cloud identity, storage, and regional controls | Does integration reduce queueing and operational handoffs? | Cloud coupling and a separate billing surface |
| Amazon Transcribe | AWS-centered systems and managed speech operations | Does the AWS path keep tail latency predictable? | AWS coupling and another service contract to operate |
| Deepgram | Speech-focused API evaluation, including streaming or batch | Does its quality justify the latency and integration trade-off? | Separate vendor controls and data-path review |
| A single REST gateway | One credential and bill across backend capabilities, with provider comparison before rollout | Can its estimate and routing metadata support quota and SLO decisions? | The gateway still depends on the readiness of the selected ASR capability |
| Ollama with a self-hosted model | Teams willing to own runtime capacity and deployment | Can reserved capacity meet peak demand without hurting extraction latency? | Hardware, upgrades, and incident ownership stay in-house |
The last row is not a free pass, and the gateway row is not a recommendation by itself. Infrai is a reasonable fit when one key and one bill for the surrounding backend capabilities matter more than adding another provider-specific integration, and a plain REST call lets the Go admission service and other runtimes share the same contract without another SDK branch. Its ASR readiness still has to be checked before rollout. In the current planning state, the ASR catalog entry reports available=false; a transcription endpoint shape exists, but the service cannot process transcriptions yet. That makes a pre-launch estimate useful for quotas and alert thresholds while making any claim about live transcrip
I Wrote a Claude Code SEO Skill, and Testing It Found Two Bugs on Day One
Claude Code skills are just folders. A SKILL.md with a name and a description, plus whatever scripts you want the model to run. When the description matches what you asked for, Claude loads the instructions and follows them. That's the whole mechanism, and it's why SEO is a good fit: most on-page checks are deterministic, and a model is much better at explaining a result than at counting characters in a <title> tag.
So I wrote one. It checks a single URL for the on-page basics and hands Claude a JSON report to turn into a fix list. Then I ran it against five real pages and one fake one before trusting it. It broke twice, on pages I'd have assumed were safe.
The layout
.claude/skills/seo-page-check/
├── SKILL.md
└── scripts/
└── check_page.py
Project-level skills live in .claude/skills/ inside the repo; personal ones go in ~/.claude/skills/. Claude Code reads the frontmatter of every skill at startup and only pulls in the body when the description matches the task, so a skill you're not using costs almost nothing.
SKILL.md
name: seo-page-check
description: Check one URL for on-page SEO problems (title, meta description, canonical, robots/noindex, H1, JSON-LD, image alt text) and return a prioritized fix list. Use when the user asks to audit, check, or review the SEO of a specific page or URL.
# SEO page check
1. Run the checker on the URL the user gave:
`python3 .claude/skills/seo-page-check/scripts/check_page.py <url>`
2. Read the JSON it prints. Do not re-fetch the page yourself unless the script failed.
3. Report in this order, and skip any section with nothing in it:
- **Blockers**: anything in `blockers` (noindex, non-200 status, canonical pointing to another host).
- **Fix next**: items in `warnings`, one line each, with the current value quoted.
- **Fine**: one line listing what passed.
4. For each title or description warning, suggest one rewrite that keeps the page's main keyword. Do not invent keywords that are not on the page.
5. Never claim a ranking impact. Say what is wrong and what to change.
Two choices in there are deliberate.
The description lists the actual checks. Claude decides whether to load a skill from the description alone, so "SEO helper" would be a bad one. It's vague, and it would match half of what you type in a marketing repo. Naming the checks and the trigger ("a specific page or URL") keeps it from firing when you ask for keyword ideas.
Rule 5 is there because models love writing "this will improve your rankings." Nobody knows that from a page check, so the skill forbids it.
The script
Standard library only, so it runs anywhere Claude Code does without a pip install:
#!/usr/bin/env python3
"""Fetch one URL and report on-page SEO issues as JSON. Standard library only."""
import json, sys, urllib.error, urllib.request
from html.parser import HTMLParser
from urllib.parse import urlparse
class Page(HTMLParser):
def __init__(self):
super().__init__()
self.title, self.in_title, self.in_h1 = "", False, False
self.meta, self.canonical, self.h1s, self.jsonld = {}, None, [], 0
self.imgs, self.imgs_no_alt, self.title_done = 0, 0, False
def handle_starttag(self, tag, attrs):
a = dict(attrs)
if tag == "title" and not self.title_done: self.in_title = True # skip SVG <title>s
elif tag == "meta" and a.get("name"):
self.meta[a["name"].lower()] = a.get("
AI agent used to perform controversial brain surgery on a mouse, Anthropic Sonnet controlled motorized surgery rig
Anthropic's Claude AI blocks video game cloning and reverse-engineering
Google's new Genie 4 model looks incredible, but it's a long way from AGI
