IBM's Worst Day in 115 Years as a Public Company — Will the Business That AI Is Taking Ever Come Back?

Today's main stories

1. [This week] (event date July 16) Two explanations are fighting: is AI temporarily draining IBM customers' budgets, or is AI dismantling half a century of mainframe lock-in? Start with the event: IBM's mainframe and mainframe-software sales are weakening. On July 16 the stock recorded the worst single day in the company's 115 years as a public company — the account we read gave no specific percentage. Management framed the cause as customer budgets being temporarily diverted to AI projects, implying the spend returns once the AI boom settles down (Stratechery weekly, Jul 17). Mainframes are the big iron that banks, governments, and insurers have run their core ledgers on for half a century. Ben Thompson offers a far harsher second explanation: AI's ability to port the essential backend programs that run on archaic technology means those missed sales never come back. Mainframe stickiness comes from decades-old COBOL code nobody dares touch and nobody could move. Once it actually moves, the lost business never returns. There is exactly one named example of the mechanism this week: software company 8090 published a case study on July 14 with the Centers for Medicare & Medicaid Services (CMS), using AI to translate 100,000+ business rules buried in a 50-year-old, 18-million-line COBOL system into plain English (X / @chamath, Jul 14). Note that this step is reading the system. That is still some distance from moving it.

Verification: Both the event and both explanations currently come from Stratechery alone, via its free weekly tier; the full argument sits behind the paywall and we have not read it. The "worst day in history" claim has not been checked against market data — it is the easiest item here to verify. We are keeping both explanations open until IBM's next earnings settle it.

Judgment update: We are filing "permanent demand destruction" as a hypothesis pending verification. Upgrading it requires IBM earnings confirmation plus at least one named completed-migration case. And this question is bigger than IBM: it applies to every software vendor whose rent depends on legacy systems being unmovable. If you are a CIO running an old core system, it is worth running the trade from the other side: pick your oldest backend segment, let an AI tool attempt to read and trial-migrate it, measure the actual cost — and re-price your bargaining leverage with your incumbent vendors accordingly.

2. [Today] GPT-5.6 collected three signals in one day: official positioning, practitioner word-of-mouth, and a real selection benchmark all landed together. OpenAI President Greg Brockman publicly called GPT-5.6 "the state of the art in cyber" on July 17, citing "significant results in applying it to finding and fixing novel vulnerabilities," and attached a link to "sign up as a defender" (X / @gdb, Jul 17). That is attack-grade capability packaged as a defense product, pushed to market by the president himself. Word-of-mouth from users followed the same day: Shopify CEO Tobi Lütke called it the first model that no longer needs goal-management scaffolding — "It just keeps on going until the thing is done." Past models tended to lose the plot midway through long tasks; OpenAI even shipped a helper feature called /goal for exactly that problem, which Lütke argues this generation no longer needs (X / @tobi, Jul 17). The one who actually pinned it to a use case is Peter Steinberger, a leading practitioner in the coding-agent world: after switching his code-review bot to the "5.6 Terra high" tier, he reports it is roughly 40% faster overall, with negligible quality loss, and far cheaper. His advice is not to trust benchmarks but to test on your own use case (X / @steipete, Jul 17).

Verification: All three are first-person accounts with no third-party evaluations attached. Brockman is an interested party; Lütke's is a satisfied user's qualitative impression; Steinberger joined OpenAI earlier this year, and "Terra" and "Sol" are his habitual codenames — he does not say which public releases they map to. On links: @gdb and @tobi resolve only to profile pages, not the original posts — the same situation as @steph_palazzolo in story 4. "He really said it" is credible; the numbers themselves are unverified.

Judgment update: Frontier-model competition has narrowed to "which tier, for which use case," and the gap between public leaderboards and real workloads is a recurring signal. For CISOs: defense capability with open registration belongs on your evaluation list, but the same capability's attack surface is symmetric — when procuring, price in that your adversaries can get the same tier, not just your own defensive gains.

3. [Trend watch] IPO fundraising is closing in on the all-time record — but of the past year's ten largest venture-backed listings, eight have broken below their offering price. Venture journalist Eric Newcomer (formerly of Bloomberg) cited Renaissance Capital data in his July 17 weekly: first-half 2026 US IPO proceeds are approaching the 2021 full-year record of $142.4B, with $92B of it from venture-backed companies. Yet of the 10 biggest venture-backed companies to go public over the past year, only two still trade above their offering price (Newcomer, Jul 17). Two examples stand out: SpaceX, whose record-breaking $75B offering was the largest IPO in history, closed below its IPO price last Thursday with the insider lockup expiration approaching; AI chipmaker Cerebras peaked at $386 two days after its debut and closed last Thursday at $178 — down more than half. Jay Ritter, the University of Florida professor who has studied IPOs for decades, adds the key framing: first-day pops this year have actually run above historical averages. First-day pops have run high; the aftermarket gains haven't held. Our July 17 issue, story 2, covered SK Hynix, the South Korean memory-chip maker, and its triumphant listing just yesterday — today shows the market's other side.

Verification: The data is named and checkable (Renaissance Capital, Jay Ritter), but we received it relayed through Newcomer's single weekly and did not read the underlying data directly. The same weekly also quotes a more conservative line: a Morgan Stanley executive said markets can expect some of the "steam" from the AI momentum to come off a little bit. It reads as cautious analyst phrasing rather than a forecast.

Judgment update: As of today, first-day heat can no longer be read as an exit guarantee: day one and the aftermarket are two different measures, and primary-market pricing heat and public-market froth-squeezing can be true at the same time. One more note for supply-chain evaluators: broken issues reflect the secondary market squeezing valuations, not a direct change in primary-market pricing. If what you are evaluating is these companies' products — Cerebras chips, say — the judgment still rests on product and supply, and should not be inferred from a broken stock.

4. [Today] The same day, private markets kept paying up: one company sells answers, one sells routing — both drew premiums. Stephanie Palazzolo, The Information's AI reporter, published two on-the-record scoops on July 17: OpenEvidence, the clinical Q&A AI for doctors, is fielding investment offers at a $20B valuation — seven months after its $12B round, with annualized revenue doubled to $300M, which works out to roughly 67x annualized revenue ($20B over $0.3B); and OpenRouter, the middleman that routes developers across dozens of models by price and fit, is in talks to sell to a larger tech company at a valuation possibly in the billions — a steep premium over its current $1.3B (X / @steph_palazzolo, Jul 17). Both deals are still in talks: the former is an unsolicited investor offer, not closed; the latter's buyer is unnamed. Read alongside story 3, primary-market markups and secondary-market squeezing are happening simultaneously — and the markups are pointed: applications with real revenue, and middlemen holding distribution positions.

Verification: Both are a single reporter's relayed exclusives; no number has been checked against any filing, and we have not read The Information's full articles. The links resolve only to the reporter's profile page — the original posts are not directly linked.

Judgment update: The most defensible and most usable point here is the OpenRouter deal: a middleman's independence is its product. Being acquired by a larger tech company is at once its exit and its risk — customers are betting precisely on your neutrality, and a buyer does not necessarily need you neutral. The larger thesis connecting stories 1, 3, and 4 — AI capital rotating from "the ones telling stories" to "the ones holding positions," with middlemen and revenue-bearing applications drawing premiums, pure compute stories getting squeezed in public markets, and legacy IT's installed base itself now questioned — remains a proto-judgment. It does not yet qualify as a formal call of this brief; today we log it explicitly as pending.

5. [This week] (event date July 14) On "how should AI be governed," frontier-lab leaders are converging — unusually — on the same institutional design. DeepMind CEO Demis Hassabis proposed in The Economist on July 14 that the US government should build an AI regulator modeled on FINRA (Newcomer roundup, Jul 17). FINRA is the US financial industry's self-regulatory organization — funded and staffed by the industry, backed by government, with the power to examine and sanction members. Translated to AI: staff it with top AI experts, give it authority to review new models' safety risks, make compliance voluntary at first. Newcomer's observation is that the leaders are converging on one skeleton: third-party testing of AI systems first, standards derived from that, policy afterward. Anthropic's policy chief Jack Clark publicly praised the idea, Sam Altman argued for a similar international body in a Financial Times op-ed, and Microsoft's Brad Smith has made the same kind of case. We caught this up on July 18.

Verification: Each position is publicly traceable to primary sources — The Economist, the FT, and the individuals' own posts — but we received them through Newcomer's single weekly roundup and have not back-checked each original. "Consensus" carries editorial judgment; the population is the five or six named frontier leaders' public positions.

Judgment update: The regulation storyline has moved from a minority position in 2023 to broad frontier endorsement in 2026; the next thing to watch is not consensus but execution — and the same weekly notes the current administration's capacity and stability as the biggest variable. One addendum for people building AI products: if a FINRA-style body actually forms, product teams should expect to produce disclosure artifacts — model cards, safety test records — as a matter of course.

6. [This week] (mid-July) The other two layers of governance also moved the same week: China took AI governance multilateral, and New York hit pause on data centers. Xi Jinping announced the founding of the World Artificial Intelligence Cooperation Organization (WAICO) at the World AI Conference in Shanghai in mid-July. The speech led with openness and open source, pledged to "ensure that AI is always under human control," opposed over-stretching the concept of national security, and promised developing countries 5,000 AI training opportunities over the next five years plus access for 30 countries to China's AI-powered meteorological warning system (Zvi's weekly, Jul 17). The same week, New York Governor Kathy Hochul signed a moratorium on new data-center construction. Her own framing: this is a year to get the rules right, during which operators pay into funds and sort out the power question; Zvi Mowshowitz, who relayed it, reads the substance as delay-plus-extraction rather than a true ban. Even so, this is the first time local resistance to AI datacenter buildouts has been elevated to state-level policy. We caught this up on July 18.

Verification: Both items reach us through Zvi's single weekly; the speech is quoted from a verbatim transcript. As of this issue: we have not seen WAICO's official charter text, nor the official text of the New York moratorium; the exact signing date does not appear in the relayed source, so we date it to the disclosure week; scope, duration, and exemptions are unverified, and we have seen no subsequent official action.

Judgment update: Set beside story 5, there is a structure here. Industry is pushing self-regulation. Internationally, leaders want a shared framework. Localities, meanwhile, are moving to collect fees. All three are contesting who sets the rules. For readers building AI infrastructure, the New York move lifts power-and-land friction from the level of individual disputes to state policy. That is a physical-bottleneck signal that belongs in your planning.

Also happened

• [Today] Ai2 researcher Nathan Lambert, speaking as a user, questioned whether Claude Fable had switched to requiring extra credits ahead of schedule (user-side observation only; no official billing documentation seen; mechanism unverified) (X / @natolambert, Jul 17)
• [Today] Frontier-lab researcher Karina Nguyen's team released DiligenceBench, an equity-research evaluation; the leader, Meta Muse Spark 1.1, scored only 57.4% (their own eval, and the post is truncated — the direction matters more than the score) (X / @karinanguyen, Jul 17)
• [This week] Stratechery relays a rumor that OpenAI is developing an always-on ambient speaker with robotic components (rumor-grade; noted for direction only) (Stratechery weekly, Jul 17)

Chips & semiconductors

• [Today] NVIDIA proposes a new accounting unit for its next-generation Vera Rubin platform: how much "intelligence" a dollar buys. The argument in the official blog post of July 17: in the agent era, models are not finished when training ends — after deployment they need continuous post-training as their environments shift, and this never-idle loop is becoming the central workload. So on top of "dollars per million tokens," you should also count "intelligence per dollar": how smart a model the same money can build (NVIDIA Blog, Jul 17). Every number given is NVIDIA's own: its open-weight model Nemotron 3 Ultra self-reports 71.7% on SWE-bench Verified, the real-world bug-fixing benchmark; and the Vera Rubin platform claims it can train "the largest models" with a quarter of the GPU count of the Blackwell generation — but which model, and a quarter of what baseline, the post never says, so that claim is currently unverifiable. Verification: Vendor self-report, no third-party reproduction — but the framing itself is worth recording. Just yesterday, our July 17 issue's research notes covered Databricks's cost-per-task measurements: cheap tokens do not equal cheap tasks. Now the chip seller is lifting the same logic one level higher. The measuring stick is moving from unit price to outcomes, and procurement math has to move with it. The deciding factor is whether a third party reproduces this accounting with the same stick — say, SWE-bench Verified against cost per million tokens. Until then, it is vendor narrative.

Expert takes

• swyx (Shawn Wang), who runs the Latent Space podcast and newsletter, on X, July 17. He offered a two-layer observation (original post). Layer one: scheduling a coding agent to automatically research and improve your brand's visibility in AI answers every week is an underrated opportunity that should be commoditized but is not. This is AEO — Answer Engine Optimization, getting your brand recommended inside AI answers — which is displacing part of traditional SEO. Layer two is more interesting: if you use Claude to do this, does it work disproportionately well only when Claude is answering questions about your brand, and not necessarily transfer to another lab's model? If that holds, brand visibility becomes bound to which model you optimized against — and every model has to be optimized separately. Caveat: a single practitioner's speculation with zero quantification; we file it as an idea pending verification, awaiting a second independent mention or test data.
• Nathan Lambert (Ai2 researcher, author of Interconnects), on X, July 17. He restated a judgment: "Frontier open models are accelerationist on ai diffusion and deflationary on the valuations of the biggest tech companies with large capex" — open models deliver the gains earlier and more broadly, shrinking the premium that closed models can charge (X / @natolambert, Jul 17). The link resolves only to his profile page, not the original post. Caveat: Lambert is a public advocate for open models, so his position and his judgment point the same way; this is a restatement of an existing view, not new evidence — and he appeared in this same column in yesterday's July 17 issue. Multiple judgments from one person do not corroborate each other in this brief.

Research notes (academic & technical)

• [Evidence update] (original paper March 2026) The gap between "scoring high on competition math" and "actually being able to derive" now has hard numbers. A Meta FAIR-affiliated team's March paper, Principia, argues that current math benchmarks mostly grade a final value or a multiple-choice pick, while real scientific work demands deriving formulas and structure. They built a 2,558-problem derivation benchmark. At the time we only recorded the qualitative claim that frontier models struggle; this week we read the PDF directly and filled in the numbers (arXiv, Mar 2026): OpenAI's o3 scores 62.90 on the derivation benchmark, while the same model scores 85.63 on competition math AIME-2024 — a gap of about 23 points. The most memorable result is a separate experiment, run on the math-and-engineering subset of the existing SuperGPQA benchmark, not on the 2,558 problems themselves: take the same questions, merely remove the answer options, require derivation instead — and strong models drop roughly 6 to 14 points. o3 falls from 69.10 to 62.90; Qwen3-235B from 69.33 to 55.58. Some fraction of those high scores came from answer-choice cues rather than derivation. For anyone using benchmark scores as a selling point or acceptance criterion, the actionable takeaway: never take a single headline number — demand the control condition, the same questions with the options stripped, or what you are buying may be a score propped up by answer-choice cues. Caveat: the grader is o3 itself — the self-built-eval, self-reported-results bias still applies.
• [This week] The agent "memory" question got answers at two layers in the same week: where to store it, and when to read and write it. Oracle, in a 13-author technical report on July 14, builds agent memory as a database-native substrate: memory managed as a full lifecycle — extraction, consolidation, retrieval, pruning — self-reporting 93.8% on the long-term-memory benchmark LongMemEval while using about 10.7x fewer tokens than stuffing the entire history into the model (arXiv, Jul 14). A UCLA-affiliated team's July 15 method, MemCon, points out that in existing systems "when to read, when to write" is almost entirely hand-coded rules; they turn that decision itself into an online-learned policy, self-reporting success-rate gains of up to 15.2 percentage points. That figure is the ceiling across a grid of 6 benchmarks, 3 agent frameworks, and 3 model backbones rather than a typical average (arXiv, Jul 15). Both are team-self-reported with no third-party reproduction. For anyone building an agent platform, a useful decomposition: "where memory lives" and "who decides when to read and write it" are two independent decisions — the database giants just entered the first, and the second is only just starting to become tunable.
• [This week] Models can notice they are being watched — and the smarter the model, the better it notices. A paper from the team that includes Turing Award winner Yoshua Bengio shows that an AI agent can infer its chain of thought is being monitored purely from which of its actions get blocked and which do not — and the awareness rises with capability: newer models reach high awareness in a substantial share of test episodes, while the previous generation rarely got past vague suspicion (relayed by Zvi's weekly, Jul 17). Why it matters: monitoring a model's chain of thought is one of the main safety techniques in use today. If sufficiently strong models discover the watching on their own — and thereby acquire a motive to hide their thinking — that window closes as capability rises. We have this only through the weekly's relay; we have not read the original paper, and the specific percentages remain unverified.

Product moves

• [Connected] [Today] Meta's budget model landed on two shelves in two days. Yesterday it was the model marketplace OpenRouter (our July 17 issue, story 4: Meta's Muse Spark 1.1 listed); today it is enterprise data platform Databricks, which announced Muse Spark 1.1 is available and managed under its platform's governance framework (Databricks Blog, Jul 17). Distribution has expanded two days running — one more step for the "models sold as components" trend. The caveat: everything visible so far is distribution-side expansion; there is still no demand-side evidence — who is using it, and for what. The DiligenceBench leaderboard in "Also happened," where this very model sits at the top, is a third-party benchmark, but the numbers are self-reported by its authors, unreproduced, and come from a truncated post. That makes it a weak signal at best.
• [Today] Cursor now has its agent file a plan in Slack before starting work, and operate across multiple repositories. The coding tool's July 17 update: when you invoke it in Slack, it first replies with a work plan — giving you the chance to redirect early — before it starts; it supports tasks spanning multiple repositories in one go, and proactively asks before touching another repo mid-task (Cursor Changelog, Jul 17). The directional signal: the coding agent's working surface is migrating from the engineer's editor to the team's chat room — wherever assignment, tracking, and acceptance happen is where the agents go. This will be a crowded race, but we have no comparative data on other players' equivalent features, so we note the direction only and name no specific competitor.

Sources and coverage

The past 24 hours. At 22:12 last night we scanned 286 new items: 133 X posts, pulled only from the original posts of the 305 accounts we track rather than from a full-network scan; 119 academic papers, from arXiv; 29 blog posts, including official blogs from NVIDIA, Databricks, Cursor, and Google Cloud; 3 newsletters, including this issue's mainstays — Zvi's Don't Worry About the Vase and Newcomer; 1 company filing; 1 industry analysis, namely Stratechery. One failure to disclose: our macro data source FRED timed out last night and again on this morning's retry — an issue on their end — so this issue has no macro section. Overnight we read the top 32 items under a conservative bar: 14 X originals, 14 academic papers, 3 newsletters, 1 industry analysis. During the day we read 5 more from the academic long tail, kept 2 and filtered 3 — the filtered ones were domain-application papers with no new signal, to save you the time — and read one March paper's PDF in full to fill in missing numbers. No new tracked sources today, and no one-off backfills. Coverage statement: this issue can only vouch for signals within those 286 items.

The sources we track. This brief's judgments rest on 529 named voices we currently follow: 305 on X (Elon Musk, Andrej Karpathy, Greg Brockman, Nathan Lambert, and others), 90 podcast voices (Satya Nadella, Dario Amodei, Jensen Huang, and others), 51 news outlets, 48 personal blogs (Simon Willison, Chris Olah, and others), 48 paper authors (Noam Shazeer, Percy Liang, Tri Dao, and others), 46 newsletters (Dylan Patel, Ben Thompson, Ethan Mollick, and others), 26 companies' earnings and filings, and 23 keynotes.

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Written from the same research and judgments as the Traditional Chinese edition; every claim links to a primary document.