SUCCESS

When a fast-food chain, a packaged-food giant, and an appliance manufacturer all say the same thing in the same quarter, it is worth stopping to ask why. These are businesses with nothing in common. They do not compete with each other. They do not share supply chains. They do not even sell to the same aisle of the store.But their CEOs have been delivering versions of the same warning for months now. The customers at the lower end of the income scale are under serious financial pressure. It is showing up in the sales data at all three companies. McDonald’s, Kraft Heinz and Whirlpool have each seen it from a different angle. Together their reports sketch a picture of American household finances that investors need to pay attention to.What Kraft Heinz, McDonald’s, and Whirlpool CEOs say about consumersKraft Heinz CEO Steve Cahillane gave the bluntest version of this view in a May 2026 interview. “They’re literally running out of money at the end of the month,” he told Bloomberg. “We’re seeing negative cash flows in the lower-income brackets where they’re dipping into savings.” Kraft Heinz responded by cutting prices on some products, adding promotions, and offering smaller package sizes at lower price points.McDonald’s CEO Chris Kempczinski described “heightened anxiety” among the company’s customers. CFO Ian Borden went further, pointing to higher gas prices hitting lower-income households particularly hard. Those customers are pulling back. Higher-income customers are not.Related: McDonald’s unveils unexpected first-ever partnershipWhirlpool CEO Marc Bitzer described a sharp pullback in big-ticket appliance demand. North America president Juan Carlos Puente used the term “recession-level industry contractions.” Discretionary appliance demand fell roughly 15%, according to Yahoo Finance.Whirlpool’s situation is the most acute because appliances are expensive and postponable. When a family is under financial pressure, they stop replacing things they can still live with.Credit card debt and savings rates signal U.S. consumer stressU.S. credit card balances hit $1.25 trillion in the first quarter of 2026. Auto loan balances were at $1.69 trillion over the same period. Americans are carrying more debt than they were a year ago, and they are saving less at the same time, per the Federal Reserve Bank of New York.The personal saving rate fell to 2.7% in June, per the BEA. That leaves most households with very little cushion when an unexpected expense hits.The Federal Reserve’s latest household economic well-being report found that 16% of adults had not paid all their bills in full during the previous month. Among those who struggled, 42% had paid at least one bill late, according to the Federal Reserve.These are not recession figures. They are signs of a consumer base that has been running on fumes for a while.

These warnings do not mean the U.S. economy is entering a recession.Alex/Getty Images

Why food, housing, and energy costs keep squeezing U.S. householdsThe cost-of-living issue is not just about recent months. It goes back to 2020. Food prices in the U.S. have risen more than 33% since the start of that year. Housing costs have risen roughly 33% over the same period. Energy prices have climbed more than 42%, per the Fed.Slower inflation does not reverse those increases. It just means prices stop rising as fast. The eggs that got more expensive in 2022 are still more expensive in 2026. Nobody got a refund. People dipped into savings or put it on credit. That money is gone.More Economy:Bank of America CEO warns inflation will back Fed into a cornerBank of America just made a strong call on inflation, economyGoldman Sachs says Americans may pay for the AI boomThe Federal Reserve Bank of Minneapolis inflation calculator puts $100 today at roughly the same purchasing power as $11.74 in 1970, according to Moneywise. Run that number past someone who grew up in the 1990s thinking they had a handle on their finances. Their wages went up. Their costs went up faster. The math stopped working somewhere in between.What lower-income consumer stress means for these 3 stocksEach company is seeing the same consumer problem from a different angle. At Kraft Heinz, shoppers are buying store brands instead of Heinz ketchup. They hold out for promotions. They grab the smaller pack. Cahillane cut prices and added smaller package formats because he had to. Sales volumes were leaving if he did not. Margins took the hit.For McDonald’s, the dynamic is more complicated. The chain has traditionally benefited from trade-down eating. People who cut back on casual dining tend to shift toward fast food. But higher fuel costs eat into the budgets of lower-income customers who would otherwise make that shift. McDonald’s is caught between benefiting from broader restaurant weakness and losing its cheapest customers to eating at home.Whirlpool has the toughest version of this. A refrigerator is not something you buy because you feel like it. You buy one when yours dies. Right now, people are patching things together and putting off that purchase. Credit is expensive. Confidence is low. Puente called it recession-level demand for a reason. There is no pent-up buying wave coming. There is just a repair bill that keeps getting extended.These warnings do not mean the U.S. economy is entering a recession. Higher-income consumers are still spending. Corporate profits remain elevated. But the bottom half of the income distribution is clearly under stress. The three companies named here are watching it show up in their own data. Investors should watch the same metrics: margin trends, volume per customer, and how much promotional spending it takes to keep shelves moving.Related: JPMorgan sends blunt verdict on oil, economy

Strong demand for copper and steady demand for iron ore underpinned a 30% profit increase by the world’s biggest mining company, BHP

Cursor began rolling out Origin, its own code hosting platform, to paid users on Monday morning. Roughly three and a half hours later, GitHub’s status page lit up with what became a six-hour-and-forty-two-minute global degradation — error rates near 20% across pull requests, issues and the API, and near 50% on archive and raw file downloads, according to GitHub’s incident log. Enterprise single sign-on went down with it: SAML, OIDC, SCIM provisioning and Team Sync all failed. So did Copilot.The developer internet did what the developer internet does.”You can now host your repos in Cursor Origin and deploy to Vercel via Cursor Origin which is itself hosted on Vercel,” Vercel chief executive Guillermo Rauch posted on X. “And unlike GitHub, it’s online 😁” Asked why he was smiling, Rauch replied: “trying to make light of the situation. We ourselves are stuck because of github rn!”Matt Palmer, who works at Cursor, quote-tweeted his own company’s launch with the day’s best line: “We were going to ship this earlier, but GitHub was down.” A GitHub outage, in other words, delayed the launch of a GitHub competitor.Product launches get locked weeks in advance, and no evidence suggests Cursor timed this one. But the coincidence did the company an enormous favor, because it dramatized the argument Origin exists to make. For eighteen years, choosing where to host your team’s source code has been the least interesting decision an engineering organization makes. Cursor is betting that AI agents have made it interesting again — and for technical decision makers, that is the real news here. Not a new product, but a new procurement question with a governance problem attached.Inside Origin: what Cursor’s code hosting platform actually doesOrigin lives in a new Codebase tab inside Cursor. Teams name a codebase, which becomes part of its URL, then push to it over the command line. From there they get the machinery you would expect from a forge — the service layer that wraps Git and handles storage, permissions, checks and merges. Every repository comes with pull requests: timelines, commits, checks and files changed. Reviewers read the diff, leave comments and merge, without ever opening a browser tab.What Cursor built around that machinery is the part worth studying. Agents now operate in the same surface as the code and the pull requests they are modifying. “Your code, PRs, and agents are now in the same place,” the changelog reads. A developer can ask questions about the file on screen, hand an agent a review comment and have it revise the pull request in place, or tell it to push a branch — all inside the editor where the code was written.Three integrations shipped on day one, and the choice of partners is telling. Vercel spins up a preview deployment for every pull request and ships to production on merge, available in public beta for Pro and Enterprise customers, its developer account said. Depot and Buildkite run continuous integration, and critically, both execute existing GitHub Actions workflows unchanged. Buildkite adds native pipelines on top.That compatibility layer is the whole strategy in miniature. Cursor is not asking teams to rewrite their build system, retrain their engineers or rip out their deployment pipeline. It is asking them to try a second window onto code they already have — which is a far easier request to approve.More partners are coming, the company said, and the ones it landed first are the ones that matter to a platform team evaluating whether Origin can carry real work. A forge without deployments and CI is a code viewer. A forge that runs your existing Actions workflows and ships previews to the CDN you already pay for is a candidate.Why letting GitHub stay the source of truth is Origin’s smartest design choiceHere is the decision enterprise buyers should study most closely, because it determines whether Origin survives a security review at all.Cursor does not ask you to leave GitHub. Connect a GitHub organization, pick repositories, and they appear alongside Origin-native ones. “Pushes keep going to GitHub, which stays the source of truth for anything started there,” the changelog says. Access permissions mirror GitHub’s existing read and write settings rather than establishing a parallel system. Pull request conversations sync in both directions — comment in Cursor and it posts to GitHub; reply or react on GitHub and it surfaces in Cursor “within seconds.”This is a classic wedge, and a well-executed one. Rip-and-replace migration of source control ranks among the highest-risk projects an engineering organization can undertake. It touches continuous integration, compliance evidence, audit trails, branch protection rules, every integration in the toolchain and the muscle memory of every engineer on staff. Almost no chief technology officer approves that for a product in early beta.A read-mostly mirror that leaves GitHub authoritative approves itself. It costs nothing to try, breaks nothing if abandoned, and quietly relocates the place developers spend their working hours. If Cursor’s review experience proves better — and Cursor spent real money to make sure it would — the source of truth eventually follows the attention.That money went to Graphite, the code review startup Cursor bought in December 2025 for what Axios reported was well above its $290 million Series B valuation. Graphite built stacked pull requests, the workflow that lets developers keep shipping dependent changes without waiting on approvals. Announcing the deal, Cursor wrote that “the boundary between where you write code and where you collaborate on it feels increasingly arbitrary,” and promised “some more radical ideas we can’t share just yet.” Origin is the radical idea. Graphite co-founder Tomas Reimers unveiled it on stage at Cursor’s inaugural Compile conference in June and leads its development.How AI agents turned code review into software’s new bottleneckThe case for an agent-native forge rests on a claim that is easy to state and, unusually for this market, well supported by evidence: writing code stopped being the constraint. Reviewing and integrating it became one.Google’s 2025 DORA report, drawn from nearly 5,000 technology professionals, found that 90% of developers now use AI at work, spending a median of two hours a day with it, and more than 80% say it made them more productive. But AI adoption showed a positive relationship with software delivery throughput and a negative one with delivery stability. More output, more breakage. The report’s authors describe AI as “an amplifier” that “magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.”Trust has not kept pace with volume. Stack Overflow’s 2025 developer survey of 49,009 respondents across 177 countries found 84% using or planning to use AI tools, while trust in their accuracy fell to 33% from 43% a year earlier and distrust climbed to 46% from 31%. Two-thirds named “AI solutions that are almost right, but not quite” as their leading frustration. GitLab’s ninth annual DevSecOps survey, of 3,266 practitioners polled by Harris, put numbers on the operational drag: 73% had hit problems with vibe-coded output, 70% said AI made compliance management harder, and only 37% would let AI handle daily tasks without human review.The volume climbs regardless. GitHub’s Octoverse 2025 counted 180 million developers, 630 million repositories and 43.2 million pull requests merged per month, up 23% year over year. And RuntimeWire reported the internal figure that best explains Origin’s existence: 35% of pull requests merged inside Cursor were opened by agents running autonomously in cloud virtual machines.A forge built for humans assumes a pull request represents human intent, opened by someone you can ask what they meant. Once a third of merged changes come from software, the queue stops being a conversation and becomes a scheduling problem. That is a real architectural argument, and it is the strongest thing Cursor has going for it.GitHub’s reliability crisis handed Cursor an opening it did not have to earnThe supply-side case for an alternative is simpler: GitHub has been unreliable, and its own executives have said so.An analysis by LeadDev counted 257 incidents between May 2025 and April 2026, 48 of them major — roughly one significant disruption per week. February was the worst month on record with 37. GitHub Actions alone accounted for 57 outages in twelve months. Chief technology officer Vlad Fedorov has said the platform “wasn’t built for the scale it’s now being asked to handle” and must design for 30 times today’s load. In an April engineering post covered by InfoQ, the company acknowledged it “failed to meet its own reliability standards,” citing rapid growth, tight architectural coupling and inadequate load shedding. Monday’s outage was the seventh incident on GitHub’s status page in fifteen days.The fatigue is audible. “GitHub really doesn’t feel built for the agent era,” one developer wrote on X as Origin went live. “It goes down way too often, but until now there haven’t been many real alternatives.”The defections started before Origin existed. The Zig programming language moved to Codeberg in November 2025, citing Actions failures among its reasons. In April, Mitchell Hashimoto announced that Ghostty — a terminal emulator with more than 52,000 stars — would leave too, pointing to near-daily outages that blocked reviews and CI for hours. And The Information reported in March that OpenAI, a company Microsoft holds a large stake in, began building its own GitHub alternative partly because outages left its engineers unable to commit for hours at a time, as Tom’s Hardware relayed.Microsoft’s structure has not helped. Thomas Dohmke resigned as GitHub chief executive in August 2025 and was never replaced; the unit’s leadership was absorbed into Microsoft’s CoreAI organization under executive vice president Jay Parikh. In a May report, The Information wrote that Parikh had warned deputies that coding tools from Cursor and Anthropic could eventually make GitHub obsolete. GitHub’s own answer to the agent era, Agent HQ, lets customers orchestrate third-party agents from Anthropic, OpenAI, Google, Cognition and xAI inside GitHub — a coherent strategy that concedes the agent layer and keeps the substrate underneath. Origin attacks precisely that substrate.Now that SpaceX owns Cursor, who actually holds your source code?Cursor’s rise has been extraordinary even by the standards of this cycle. Founded in 2022 by four MIT students, Anysphere raised $8 million from the OpenAI Startup Fund in October 2023, per TechCrunch, then $100 million at $2.5 billion, $900 million at $9.9 billion, and $2.3 billion at $29.3 billion last November. In May, Bloomberg reported annualized revenue of $3 billion and more than 3,000 customers paying at least $100,000 a year.Then, three days before Origin shipped, Bloomberg reported that SpaceX completed its $60 billion all-stock acquisition of Cursor — an agreement TechCrunch covered in June, days after SpaceX’s record IPO and six months after it absorbed xAI. Cursor now operates inside a division called SpaceXAI. The vendor asking to hold your proprietary source code became, last Friday, a unit of a rocket company with its own frontier-model division and a founder not known for institutional caution.Jason Andersen of Moor Insights & Strategy raised the model-routing question to Tech Times in June, before the deal closed: “xAI’s models and treatment of guardrails are very different than what Cursor has stood for.” That piece framed the question a chief information security officer now has to answer. When one company controls the editor where agents write code, the host where that code lives and the model those agents run on, what governs what it does with the code?Cursor has not published an answer. RuntimeWire noted before launch that Origin’s pricing, security architecture, data-handling terms and migration tooling were all unpublished, and Monday’s changelog adds none of them. It says only that Origin reaches “all paid plan users starting today, except enterprise orgs whose admins opt out.” Opt-out, not opt-in — a sentence administrators should read twice.There is also a track record to weigh. In July, researchers at Mindgard disclosed that Cursor would execute a malicious git.exe planted in a Windows project’s root the moment a user opened it, with no prompt — a repository-poisoning flaw they first reported in December 2025. The Hacker News reported that Cursor declined to patch it, calling the issue out of scope under a shared-responsibility model while conceding it had not “closed the loop with the researcher in a timely manner.” No CVE was issued. The same flaw class turned up unpatched in GitHub Copilot CLI, Google’s Gemini CLI and OpenAI’s Codex — but a vulnerability the vendor declined to fix makes an awkward footnote for a product whose pitch is basically “let us hold your repositories.”What engineering leaders should settle before they let Origin into the toolchainOrigin is a beta, not a migration, and treated as one it is worth evaluating. The sync mode gives platform teams a low-risk way to measure whether an agent-native review surface shortens cycle time, without touching a single branch protection rule. But three things deserve resolution before anything authoritative moves.The first is the default. Origin switches on for paid users unless an enterprise administrator opts out, which means an organization that has not made an affirmative decision about whether proprietary code may be mirrored to a new host has effectively had that decision made for it. Confirming your posture is a Monday-morning task, not a next-quarter one.The second is the paperwork. Retention, residency, training use, subprocessors and what changes now that Cursor reports into SpaceX are all unpublished, and a product page is not a contract. Until those terms exist in writing, the defensible position is to treat Origin as a convenience layer over GitHub rather than a system of record — which is, conveniently, exactly what its architecture already is.The third is the exit. Origin’s Actions compatibility and its GitHub-as-source-of-truth design are the properties that make it safe to adopt. They are also the ones most likely to erode as Cursor’s incentives shift toward owning the substrate rather than borrowing it. Ask what egress looks like now, while the mirror is still a mirror.None of which makes Cursor’s argument wrong. GitHub earned its incumbency by being boring, dependable infrastructure, and it has spent eighteen months being neither while a third of the code arriving at its front door stopped being written by people. Origin is a serious answer to a real problem, built by a team that bought the right company to build it.But GitHub’s failure and Cursor’s are different in kind, and enterprises should not confuse them. Monday’s outage resolved at 20:22 UTC. Availability is an engineering problem, and engineering problems close. The question of who holds your source code, what they may do with it and who they ultimately answer to carries no such timestamp — and on that one, the company that spent Monday selling trust has yet to publish its terms.

The biggest AI model release of the past few days, at least among the developers and AI power users on social media, wasn’t a frontier cloud model from OpenAI, Anthropic or Google.It was a 27-billion-parameter model from Alibaba: Qwen3.8-27B landed on Hugging Face on Friday under an enterprise-friendly, open source Apache 2.0 license, giving developers downloadable weights for a dense multimodal model.But Qwen3.8-27B isn’t a garden variety small local model: it includes native image and video understanding, a 262,144-token context window, configurable reasoning and support for coding and agentic workflows — a “compact, deployment-friendly” version of the capabilities developed for its Qwen3.8 generation.That unusually small hardware footprint is a major part of Qwen3.8-27B’s appeal. Running the model at full 16-bit precision requires roughly 56GB of GPU memory, while an FP8 version needs about 28GB. But 4-bit quantization cuts the model itself to roughly 17GB, putting it within reach of high-end consumer machines such as a powerful gaming desktop or well-equipped laptop. Hitting the sweet spot between capability and sizeThe outsized reaction among developers has been due to the dynamic combination of its capability and size.Alibaba’s own launch benchmarks immediately supplied the first jolt. The company reported 61.7 on SWE-bench Pro, 90.3 on LiveCodeBench v6, 70.7 on its CoWorkBench office-work benchmark and 84.3 on OSWorld-Verified. In Alibaba’s published comparison table, the 27B model even beats the listed Claude Opus 4.6 Max result on SWE-bench Pro and LiveCodeBench, although Opus remains ahead on Terminal-Bench, GPQA Diamond and Humanity’s Last Exam. Some of Alibaba’s evaluations are internal, and benchmark harnesses are not identical across every comparison, making the numbers poor grounds for declaring a universal winner.Third-party results show a powerful, local model with performance equivalent to proprietary models from months agoThe conversation changed Monday when third-party results began arriving.Third-party AI benchmarking outfit Artificial Analysis gave Qwen3.8-27B a score of 52 on its Intelligence Index, a composite of nine evaluations spanning coding, science, reasoning and professional tasks. That happens to be the same score Artificial Analysis currently assigns OpenAI’s mid-tier model GPT-5.6 Luna at its maximum reasoning setting — a proprietary offering only available over the cloud. As open source coding agent Cline put it on X: “This is the first time a local model has scored frontier model capability. We weren’t expecting this pace of local progress anywhere near this soon.”On Artificial Analysis’ Agentic Index measuring model performance on agentic tasks, meanwhile, Qwen3.8-27B scored 51, beating Claude Opus 4.8 on maximum reasoning effort — a frontier model Anthropic released less than three months ago. That doesn’t mean these models are equivalent, but it helps explain why developers and AI power users stood up and took notice. As developer and AI podcaster/YouTuber Sero (@0xSero on X, real name Sharif Cherf) wrote on X: “A model that runs on 3k USD of hardware is beating everything from 4 months ago. Including Opus. Permanent underclass is cancelled.” Developer Joshua “Xenova” Lochner, known for bringing machine-learning models into web browsers, highlighted the result Monday on X alongside an experiment running Qwen3.8-27B with custom WebGPU kernels. His reaction — “What a time to be alive!” — captures much of the mood: a model scoring in the vicinity of proprietary frontier systems can be downloaded, modified and executed locally rather than accessed only through a vendor API.The appeal becomes clearer when the model is compressed. Developer and AI writer Simon Willison tested a roughly 17GB Q4_K_M quantization on an M5 Max MacBook Pro and Nvidia DGX Spark. He found that it could write code, interpret images and operate a coding-agent loop through the Pi agent framework. In one experiment, the model navigated a codebase to explain how authentication worked; in another, it wrote and tested a Python utility Willison needed to convert an agent transcript from JSONL to Markdown.“The fact that a 17GB file can do all of this stuff on my home machines is a miracle,” Willison wrote. His broader point is the one resonating with power users: capabilities that recently felt inseparable from expensive hosted models are moving into files small enough to keep on a workstation.The reaction is showing up in usage as well. Cybernews reported Monday that Qwen3.8-27B passed 3 million Hugging Face downloads in its first three days, while quantized versions rapidly appeared for local inference tools. The LocalLLaMA community on Reddit created a dedicated release megathread simply to consolidate the flood of benchmarks, quantizations, configuration advice and comparisons. One user showing a locally generated game described the model as “a different beast.”Overthinking is an issueThat frenzy comes with an important caveat: Qwen3.8-27B appears to buy some of its quality by thinking a lot.Artificial Analysis says the model generated 160 million output tokens across its Intelligence Index testing, versus a 43 million median for comparable open-weight models. Willison encountered an extreme version of the same behavior because Qwen defaults to its xhigh reasoning setting. A request to generate an SVG of a pelican riding a bicycle took 21 minutes and consumed more than 22,000 reasoning tokens before producing the answer. He recommends starting with low or no reasoning for ordinary local use.Investor and developer Tomasz Tunguz found a similar trade-off in a small nine-task test against DeepSeek V4 Flash: with reasoning enabled, Qwen edged ahead on quality in his agent stack, but he reported that it was roughly 30 times slower and 4.5 times more expensive. He explicitly cautioned that nine tasks were not enough for a verdict.Inference software may narrow that gap. Qwen3.8-27B includes Multi-Token Prediction, and Willison reported about a 72% performance improvement on his DGX Spark after enabling MTP through llama.cpp compared with his default LM Studio configuration. Even then, his normal LM Studio runs were producing only around 15 to 30 tokens per second — far below the responsiveness of many hosted models.That tension is precisely why Qwen3.8-27B matters more than another leaderboard position.What enterprises should take away from Qwen3.8-27BFor enterprises, the relevant comparison is not simply whether a 27B model “beats” Claude or GPT on a benchmark. It is whether a model small enough to run inside an organization’s own infrastructure can now perform enough coding, document analysis, vision and agent work to replace API calls for meaningful classes of tasks.That proposition changes privacy, deployment and cost calculations. Apache 2.0 weights can be inspected, modified and hosted behind a company’s own controls, while Alibaba already documents compatibility with serving frameworks including vLLM, SGLang and TokenSpeed. Alibaba says a managed Qwen Cloud version with a 1-million-token default context and built-in tools is coming later.The small size and accessible hardware requirements mean that enterprises, indie developers, and even curious consumers can easily deploy the model locally without worrying about their data leaving their machine — ensuring greater privacy, information security, governance and control.There is a broader reason power users are paying attention. Hugging Face data reported by Business Insider this week shows that actual model usage skews dramatically toward smaller models even as enormous frontier releases dominate headlines; models above 70 billion parameters accounted for only a small share of 2026 downloads. Alibaba’s strategy of publishing Qwen models across multiple practical size classes has helped make the family a recurring part of developers’ local deployment workflows.Qwen3.8-27B pushes that logic further. Its benchmark scores still need more independent validation, its default reasoning behavior can be painfully inefficient, and no single leaderboard establishes frontier-model parity.But three days after release, developers are no longer reacting primarily to Alibaba’s benchmark table. They are reacting to the experience of putting a comparatively small file on hardware they control and watching it perform tasks that, not long ago, seemed to belong exclusively to the largest proprietary systems.For certain developers, AI power users—and yes, even enterprise deployments—that is the benchmark that matters most.

SpaceX(SPCX) stock is back trading near $140, up nearly 5% over the past week, as investors find new reasons to bet on Tesla (TSLA) CEO Elon Musk’s newest public-market heavyweight. Also, the latest 13F filings are only adding to the fuel.AI behemoth Nvidia(NVDA) disclosed 122.76 million SpaceX shares worth nearly $21 billion on June 30, according to 13f.info.At the same time, Alphabet (GOOG) reported 551.2 million shares valued at nearly $94.2 billion, according to 13f.info. That’s some insane institutional firepower behind a stock that hasn’t left the spotlight since its earth-shattering June debut.As we look ahead, nearly 320 million restricted shares will become eligible for transfer on August 20. For context, the last lock-up release on August 6 was much bigger, at 911.5 million shares, yet the feared selling wave never came, as reported by CNN. Instead, SpaceX jumped back above its $135 IPO price.Nevertheless, Wall Street remains bullish, slapping an average price target of around $227, implying 62% upside according to Seeking Alpha.That’s what makes five-star Phillip Securities analyst Glenn Thum’s note particularly interesting, putting a very different number on SpaceX stock compared with the bulls. Why Phillip Securities sees SpaceX falling to $75 Phillip Securities analyst Glenn Thum just took Wall Street’s most bearish stance on SpaceX stock. Thum kept a Sell rating and a $75 price target, implying 46% downside from the stock’s current price near $140. That’s also near the lower end of Wall Street’s consensus range.Thum’s call carries a ton of extra weight, as he’s rated a five-star TipRanks analyst, with a tremendous 89% success rate across his ratings. That’s higher than veteran analysts such as Dan Ives, who has a 58% success rate.Moreover, of his 106 tracked calls, 94 were profitable, generating an impressive average return of 20.8% per rating. More SpaceX:Morgan Stanley says SpaceX investors miss the bigger storyPeter Schiff says SpaceX is a warning for hyped stocksSpaceX stock defies latest Wall Street forecastsInterestingly, his concerns start with AI.Musk had suggested AI could potentially become SpaceX’s biggest revenue engine, not merely a side business. I covered that shift in my Aug. 12 story, “Elon Musk just redefined what SpaceX could become.”Musk told employees: “Probably our AI revenue — not probably, definitely — our AI revenue will exceed all other SpaceX revenue probably in September, like next month.”SpaceX’s AI revenue skyrocketed 247% year over year in Q2, with nearly $1.6 billion stemming from the initial ramp of cloud-service agreements. On the surface, that’s the sort of growth investors typically reward with a premium multiple.However, Thum makes the case that those contracts aren’t strong enough to justify one.The cloud agreements charge monthly fees and can be exited with just a 90-day notice following the initial ramp. What’s more important is that a single AI customer generated 19.5% of SpaceX’s Q2 sales, up from less than 10% a year earlier. That has SpaceX carrying an unusually high concentration risk if one major customer slows down spending or walks away.On top of that, there’s also the cost of chasing that growth.Reuters reported that SpaceX spent a whopping $18.4 billion on CapEx in Q2, nearly 2.4 times its quarterly sales, and Thum expects that elevated spending to remain near that level over the next two quarters. Additionally, compute capacity is expected to exceed 2 gigawatts by December, up from 1.4 gigawatts in June.Put simply, his argument is that SpaceX is spending as if AI demand is permanent before its contracts prove it is.For the stock to re-rate higher, he argues that those compute deals must be converted into multi-year commitments. 

SpaceX stock faces a $75 target despite its recent market rebound.Anna Moneymaker/Getty Images

AI is already reshaping SpaceX’s sales mix SpaceX’s relatively complex valuation becomes easier to understand when its business segments are pulled apart.For perspective in Q2, SpaceX generated $7.81 billion in revenue. Connectivity products, spearheaded by Starlink, contributed $4.29 billion, or roughly 55% of sales. On top of that, AI generated $2.56 billion, about 33%, while space products contributed another $962 million.So clearly, on paper, AI is far more than just an experimental side project for SpaceX.It is currently the company’s second-largest sales engine, and that gap with connectivity is likely to close out pretty quickly. Thum feels that even though demand is strong, he feels investors are assigning too much value to sales backed by contracts that can be exited with hardly much notice. Nevertheless, the scale matters. SpaceX is targeting over 2 gigawatts of compute capacity by December, up from 1.4 gigawatts in June, while dropping billions to build it.SpaceX investors are paying years aheadFor SpaceX investors, the debate is pretty much about how much future growth is embedded into the stock. At $140 per share, SpaceX trades at 373 times forward non-GAAP earnings, compared to just 13.4 times for the sector, according to Seeking Alpha. On a forward GAAP basis, the multiple balloons to roughly 1,891 times.Those numbers look pretty extreme, as Wall Street is expecting earnings to rise from a remarkably low base. GuruFocus estimates put GAAP EPS at just $0.008 in 2026, before jumping to $1.66 in 2027, $4.95 in 2028, $7.29 in 2029, and $11.19 by 2030.If we take today’s $140 share price, it means investors are paying roughly 84 times 2027 earnings, 28 times 2028 earnings, 19 times 2029 earnings, and only around 12.5 times projected 2030 EPS.That’s a pretty telling piece of information. SpaceX looks remarkably pricey when compared to near-term profits, but a lot less so if Wall Street’s long-term earnings ramp materializes. Revenue expectations point to the same story. According to GuruFocus data, sales are expected to rise from nearly $44.7 billion in 2026 to $103.8 billion in 2027 before surging to about $451.6 billion by 2030.Essentially, it then becomes more of a duration trade.Investors are loading on SpaceX today, betting that AI, Starlink, and its space businesses could convert sales growth into bottom-line strength quickly enough to compress that multiple. On the flip side, if AI contracts weaken and CapEx is elevated with meager margins, Thum’s bearish arguments become tougher to miss.Related: Peter Thiel invests $118 million in surging big tech stock

Amazon spent years telling Wall Street that AWS could become a few hundred billion dollar revenue business. Then on its last earnings call, management revised that estimate up, significantly.The new figure was $1 trillion. Not as a stretch goal. As what they now believe is a real possibility.Morgan Stanley just shared a note with TheStreet that tries to answer the question that number raises: how far away is it, and what does it mean for the stock if the math actually works out?Morgan Stanley Overweight rating on Amazon AMZN stock and $335 price targetIn a note shared with TheStreet on August 16, Morgan Stanley analyst Brian Nowak laid out what a $1 trillion AWS business would mean for Amazon shareholders.The firm raised its price target to $335 from $330 after Amazon’s Q2 earnings, representing roughly 27% upside from Amazon’s $262.65 close on August 14. The Overweight rating is unchanged, as TheStreet reported.More Amazon:JPMorgan resets Amazon stock target after AI payoffAmazon CEO Jassy may deliver a July 30 AWS earnings shockAmazon stock slides as Prime Day data reveals shopper shift ahead of earningsBut the note is really about a longer-range scenario. If AWS can reach $1 trillion in annual revenue and maintain margins consistent with what cloud computing has historically delivered, Morgan Stanley sees a path to $500 per share by year-end 2027. That is not the firm’s official forecast. It is a valuation exercise built on capacity, monetization and margin assumptions.The bull case is $500. The $335 base is where Morgan Stanley actually plants its flag. The note’s broader message is that even the base case implies meaningful upside, and the conditions needed to push toward $500 are already starting to show up in the quarterly numbers.Amazon AWS $1 trillion revenue vision and AI margin outlook from Q2 earningsThe note was prompted by what Amazon management said on the Q2 earnings call. The quote is worth reading directly.”We long believed AWS could become a few hundred billion dollar revenue business,” Amazon said, “and now believe it’ll be at least double that, and very possibly be $1 trillion annual revenue business for us in time with very appealing accompanying free cash flow and return on invested capital.”Management also addressed the margin question directly. “We’ve done this before in the first era of cloud computing, just over a longer time horizon where demand built more gradually than it has in AI. But we see the margins and returns in AI tracking what we saw with core at the same point of evolution. Actually a little ahead.”Related: Jeff Bezos just named Amazon’s next big pillarThat commentary is the foundation of Morgan Stanley’s model. AWS revenue grew 37% year over year in Q2, its fastest growth in 18 quarters, with the AI and custom chips businesses each surpassing a substantial annualized revenue run rate, according to CNBC.If margins in the AI era track historical cloud economics, as Amazon management suggests, then a $1 trillion AWS becomes a very large profit engine rather than just a large revenue line.AWS AI data center capacity buildout and Amazon $220 billion capex planMorgan Stanley’s note is explicit about what actually determines how fast AWS gets to $1 trillion. It is not demand. Demand looks strong. The constraint is whether Amazon can bring enough computing capacity online fast enough to meet it.Six gigawatts of new compute this year. Eight next year. Then eight a year after that, indefinitely. That’s the capacity schedule the note builds the whole model around.Amazon raised its full-year capital expenditure forecast to roughly $220 billion to fund it, with AI infrastructure making up the majority of the increase, according to Quartz.Beyond next year the note admits the picture gets murky. Power, construction, servers, labor. Any of those can create a bottleneck. But Morgan Stanley’s read on Amazon’s current execution is that the annual cadence it models is achievable.

At higher monetization rates the model shows AWS reaching $1 trillion as early as 2034Isabella/Getty Images

AWS revenue per watt monetization and when $1 trillion arrives for AmazonCapacity alone doesn’t get AWS to $1 trillion. What matters is how much revenue comes out of each watt of that capacity.The note puts AWS at roughly $8 in revenue per incremental watt right now. That number needs to go up. The note expects it to, driven by more valuable AI workloads and better compute pricing.At higher monetization rates the model shows AWS reaching $1 trillion as early as 2034. At the base assumption the milestone arrives around 2035. The note projects strong AWS revenue growth through the end of the decade before moderating as the base gets larger.If AWS margins in the AI era track historical cloud economics, a $1 trillion revenue line produces hundreds of billions in operating profit. Layer in the retail business and total Amazon operating profit could approach a figure that would make it one of the most profitable companies on earth.Amazon AMZN stock risks free cash flow and Morgan Stanley $500 bull caseThe risks are real and the note names them. Amazon’s AI infrastructure spending is already pressuring free cash flow, which swung to an outflow on a trailing 12-month basis through Q2 as property and equipment purchases jumped sharply year over year.Monetization may not rise as fast as the model assumes. Competition could pressure pricing. Regulation could slow construction.None of those risks disappear because Amazon said $1 trillion and Morgan Stanley built a model around it. AWS is already the largest cloud business in the world, it is growing at its fastest rate in 18 quarters, and the AI infrastructure cycle is still in its early stages.The $500 scenario is the outcome if a lot of favorable trends continue at once. The $335 base case is the outcome if they just continue normally, according to Amazon’s official Q2 earnings release. For investors, Morgan Stanley’s message is that either way, Amazon’s biggest opportunity may no longer be selling more products online.Related: Bank of America resets Amazon stock forecast on key service launch

Jeanie Buss’ lawyer has contested the Buss’ family’s attempts to sell its minority ownership shares of the Los Angeles Lakers

Cinematographer James Whitaker, creator Steven Conrad and Jason Bateman discuss the distinctive storytelling and creative vision behind HBO’s ‘DTF St. Louis.’

Twenty years ago, the NFL caused a seismic shift in its broadcast footprint when it allowed ABC to move Monday Night Football to its sister network, ESPN.Before then, 100% of NFL games were shown on broadcast television. Moving its flagship, primetime broadcast behind a cable paywall was a big risk at the time. While ESPN was incredibly popular at the time, it was only present in fewer than 100 million homes, whereas broadcast TV sets were present in nearly every home.Today, having just one game sit behind a paywall almost seems quaint, as the NFL has fully embraced spreading its broadcast rights across as many partners as want a piece. Now the NFL has high-profile games on Amazon Prime, Peacock, and Netflix, as well as ESPN.ESPN leads the way with 19 Monday Night Football regular season games to be broadcast and streamed on the ESPN App (ESPN is also simulcasting the Super Bowl for the first time in its history), though 10 of those games will also be broadcast on ABC. NFL Network has seven exclusives, including five of the league’s international games.Prime has the rights to 16 regular-season Thursday Night Football games, FBSchedules noted.The league’s biggest experiments will be broadcast on Netflix, as the league will stream a Week 1 matchup between the Los Angeles Rams and San Francisco 49ers from Australia before it tries its first-ever Thanksgiving Eve game on Wednesday, Nov. 25, to go along with its new Christmas Day two-game slate. All told, Netflix has five games this season.Peacock also has the rights to one game this season. So if you are a fan who insists on seeing every game, get ready to pay up.NFL fans pay hundreds extra to watch streamingFor NFL fans, the largest benefit to having the NFL exclusively on the Big 4 broadcast networks was that watching was essentially free. If you had a television and rabbit ears, you could watch the whole season slate. Today, with nearly 40 games exclusively behind paywalls, it takes a lot more effort, and money, to watch the NFL. It will cost the average NFL fan $207.80 to watch all of the streamed games this season, according to data compiled by digital security website All About Cookies. That averages out to about $51.95 per month. And that’s just the streamed games. “Most NFL games are broadcast for free over-the-air on channels like FOX and CBS,” All About Cookies explained. “However, the specific games available on those free channels differ from one media market to the next, meaning that fans living outside of their favorite team’s home market may not be able to watch their squad play on their local affiliate station.”That is, unless they also spring for the out-of-market packages.

It will cost the average NFL fan $207.80 to watch all of the streamed games this season.Scott Taetsch / Getty Images

It costs nearly $1,000 to watch the NFLThe NFL’s regional broadcast model has always had its drawbacks, namely the fact that fans were only able to see a small sliver of the full slate of games being played every Sunday. The NFL came up with a solution for that when it launched the NFL Sunday Ticket more than 30 years ago. But Sunday Ticket has seen some price increases in recent years, according to IMDB.Currently, there are two ways to subscribe to Sunday Ticket. NFL Sunday Ticket pricing starts at $240 for new subscribers for the season, while returning users pay $378 as a YouTube TV add-on and $480 as a stand-alone product without YouTube.Related: Netflix joins Disney and YouTube in chasing World CupWhen combined with YouTube TV, which features all of the cable games, it costs $609.95 for new users to watch all of the broadcast games and $792.95 for returning users. But that cost doesn’t include the 22 games being streamed exclusively on Prime, Netflix, and Peacock. To watch those games, fans will also have to buy monthly subscriptions, bringing the total cost for new Sunday Ticket subscribers to $737.83 and for returning subs to $920.83, according to All About Cookies.Packers, Rams lead league in paywalled gamesIn addition to new subscribers paying less, fans of teams that aren’t very good will also be paying less than the rest of us. If you are a fan of the Tennessee Titans, New York Jets, Miami Dolphins, Las Vegas Raiders, or Arizona Cardinals, then you have little to worry about, since those teams haven’t been scheduled in any primetime or streaming games this season.But on the other end of the spectrum, if you are a fan of the Green Bay Packers or Los Angeles Rams, both of which have a league-leading five premium games, be prepared to pay about $889 this year to watch all of their games with a Sunday Ticket + YouTube TV subscription, along with the necessary streaming subscriptions.Fans of some teams with four premium games, such as the Denver Broncos, Houston Texans, Pittsburgh Steelers, and Jacksonville Jaguars, will pay $852.91. The cost for other teams with four games, including the Chicago Bears, Philadelphia Eagles, and Buffalo Bills, will be $888.87.Teams with three primetime games, including the Baltimore Ravens, New England Patriots, Dallas Cowboys, Kansas City Chiefs, New York Giants, Seattle Seahawks, New York Giants, and Atlanta Falcons, will also be paying $852.91. The Cincinnati Bengals are the only team with just three primetime games for which fans will have to pay $888.87, the same as the Packers and Rams, who have two more premium games, according to All About Cookies.Related: Disney’s big bet on the NFL is already paying major dividends

A retail worker in San Francisco showed up late to 17 of 23 shifts. Their manager noticed, issued warnings, provided additional training, and then forgot about it for months.The manager was not a person. It was an AI agent named Luna, built on Anthropic’s Claude Opus 4.8, running an experimental store called Andon Market at 2102 Union Street in the city’s Cow Hollow neighborhood.When humans finally prompted the system to revisit its own attendance policy, it recommended firing the employee, according to TIME, which broke the story exclusively.How Anthropic’s Claude AI manager ended up firing a real human employeeEarlier this year, Andon Labs handed an AI called Luna a corporate credit card and a $100,000 budget and pointed it at an empty retail space in San Francisco. The instructions were loose: Open a store, hire people, try to make money.Luna picked the merchandise, posted the job listings, ran the interviews. The store at 2102 Union Street in Cow Hollow opened on April 10. Since opening, it has generated sales but lost roughly $13,000 overall, The Next Web reported.Related: Anthropic clarifies stance on open-weight AI modelsThe firing didn’t happen the way the headline implies. Luna had created an attendance policy months earlier, then lost track of it entirely. The lateness kept happening. Nothing was done.It was only when an Andon Labs staffer specifically asked Luna to conduct “a deep memory search” for its own employee handbook that the situation was reassessed. Even then, Luna’s first recommendation was a formal warning, not termination.The human manager had to inform Luna that multiple offline warnings had already been given. Only after that did Luna recommend parting ways. Humans at the lab reviewed the recommendation and carried out the dismissal.What the Andon Labs Luna AI firing logs reveal about Claude’s memory limitsAndon Labs co-founder Lukas Petersson called Luna a lenient manager. A human in the same role, he said, would have fired the employee much sooner.That’s probably true, but it also points to something the story doesn’t fully answer. A human manager doesn’t usually need to be reminded to check their own policies. They don’t forget the attendance rules they wrote.More AI:Nvidia just made a move Wall Street wasn’t ready forMicrosoft just took sides in AI policy fightOpenAI just disclosed something genuinely alarmingThe conversation logs show a system capable of making a reasonable judgment when prompted, but not one capable of monitoring situations and acting without external input. An AI that needs a human to tell it to look at its own policy before enforcing it is not yet replacing the oversight function of management, according to Inc.Petersson said Andon Labs tested the same scenario with other frontier AI models afterward. The most advanced ones reached the same verdict as Luna. Weaker agents were more hesitant.AI employment decisions and the legal gray zone U.S. regulators haven’t addressedAndon Labs built one safeguard into the experiment that most people glossed over. The workers are employed by the lab, not the AI. That kept this from becoming a legal disaster.Uber’s algorithm deactivating a gig worker is one legal category. An AI recommending the termination of someone on a real employment contract is another. The lab structured it so there was always a human employer on the hook if something went wrong.No US federal agency has issued meaningful guidance on AI in employment decisions. The EEOC and NLRB have stayed largely silent. Colorado’s AI Act and California’s SB 947 now require human review of termination decisions, but neither was in force when Andon Labs began the experiment, according to Business Insider.

Andon Labs co-founder Lukas Petersson called Luna a lenient manager.Henry/Getty Images

What the AI boss firing means for the future of work and human employeesPetersson has said he believes companies could eventually be run entirely by AI. That outcome is speculative and probably distant. But the Andon Market experiment shows that pieces of the management function are already being handed to AI systems in real settings.The more uncomfortable question isn’t about Luna specifically. Software that watches attendance, flags patterns and surfaces recommendations to managers already exists inside companies most people have never heard of.Luna is just a louder, more visible version of a dynamic that has been creeping into workplaces for years. The difference is that nobody put out a press release about the HR software tracking your badge swipes.Petersson’s own warning about where this goes is worth sitting with. “The models are increasingly being trained to be more ruthless and follow goals,” he told Inc. “If we allow them to fire people and they become more ruthless, maybe this is a future humans don’t want to live in.”The store at 2102 Union Street lost $13,000 and fired one employee. Whether that represents the beginning of something or a well-funded curiosity depends almost entirely on whether AI agents get better at the thing Luna failed at: remembering what they decided, without being asked.Related: Wells Fargo doubles down on AMD stock after Anthropic deal