Rent the pipes. Own the judgment.

About this paper. This is one of ten short background papers to a series of plain-English posts: six argument papers (background papers 1 to 6) and four primers (7, 8, 9, then 0). Every passage below is reproduced word for word from the working paper How Do We Use This? (Atkinson, 2026) as revised 7 October 2026, with that paper’s section, figure and table numbers. Passages are excerpts; a reference to anything not reproduced here resolves in the full paper. Tags such as “Our position” or “Conjecture” say what kind of support a claim has. Only the plain-terms summary, the box of terms, the post’s figure, and the section labels were written for this part. The full paper is linked from every post in the series.

Terms used here

Operator. The human who directs the agents and approves their consequential changes; here, the author.

Seat. The practice’s word for one running AI agent session, with its own context window and its own identifier.

Boot page, boot set. The boot page is the start-up page for the practice’s agent sessions; the boot set is the two rule-bearing documents every new session must load.

0-for-7. The boot page’s own record that a written rule was missed seven times on 12 July 2026, which it counts as one act rather than seven failures.

Stopping moment. The turn at which an agent states its answer as final without a further search or read.

Folding. A model giving up its position when challenged, whether or not the challenge is right.

The floor. A gap of one session between appearances of a tracked error class: it came back in the very next session. Recurrence, the program’s outcome measure, is how soon such a class comes back.

Detector, blind key. The detector is a local model that labels every operator turn as corrective or not. The blind key is a sample of turns the operator labeled by hand without seeing any model’s labels (195 graded rows); it measures how often the detector is right, not whether the agents’ answers were.

The counterweight. The paper’s proposed challenge to an agent at its stopping moment, not yet deployed. Its three clauses: (a) external to the agent’s own output; (b) firing on actual error, not on the agent’s confidence; (c) delivered at the stopping moment, at a rate above the ambient error rate.

H1 to H3. The paper’s hypotheses about the counterweight: H1, it is a projection of the operator’s judgment; H2, it is a control problem rather than a voting problem; H3, a reversal is not a correction.

From §8.6 · The judgment layer

8.6The judgment layer

The counterweight is one instrument inside a set of decisions the practice cannot delegate, and the set deserves naming because, as we read the market, it is commoditizing everything around it. Over 2026 the infrastructure beneath working agents, the orchestration, retries and state handling, became a managed rental; this practice rents it and expects to keep renting it. What did not commoditize are the decisions that infrastructure exists to execute: whose knowledge becomes the standing instruction set; what result, fixed before work opens, ends an experiment; and what “done” means for a given piece of work. A practitioner essay contemporaneous with this program raised questions we read as these three, the unanswered residue of managed-agent platforms (Forstie, 2026, cited under Prior art); the wording is ours, not the essay’s. The practice’s answers are machinery this paper has already described: changes to the standing instruction set are proposed by the machine and merged or refused by the operator; experiments carry decision rules ratified before their data are seen, under which a null closes the program (§10.4); and the finish line is ruled in advance. The record is the load-bearing element in each: every one of those decisions is made from the practice’s own logs, which is why §7 treats ownership of the record as a first-order property rather than a storage preference. An execution history that lives on a platform’s dashboard, in the platform’s format, is testimony in the sense of §6, not a record the operator holds: retention, format and query access sit with the platform, and its summary of its own agents’ success is the vendor’s account of its own product.

From §8.3 · What the rented layer ships with

It is worth recording what the default is, checked in September 2026 against each framework’s documentation and source, because it is lower than the discussion above implies. In the OpenAI Agents SDK, an agent run ends when the model emits a turn containing no tool calls; with no output type configured, any text at all satisfies the condition (OpenAI, 2026a). The framework does ship a human-in-the-loop approval primitive, and it gates tool calls rather than completion claims, so a developer can require a person to approve a refund before it is issued and has nothing built in to require a person to confirm the task was actually done (OpenAI, 2026b). The predecessor framework ended a run on the same no-more-tool-calls condition with no turn limit at all (OpenAI, 2024). The pattern repeats across the frameworks we checked. In the Claude Agent SDK the loop likewise ends when the model returns a response containing no tool calls, with no turn cap and no budget cap set by default, and the result carries the subtype success, which is a statement that the loop terminated without error rather than a claim about the answer (Anthropic, 2026a). In CrewAI the completion test is that the literal string “Final Answer” appears in the agent’s own generated text (CrewAI, 2026a), and a guardrail specified as a string is executed by the acting agent’s own model (CrewAI, 2026b). Each of these frameworks offers a real gate, and in each case it is opt-in and empty until a developer fills it.

Two details from that survey of agent-framework defaults (September 2026) are worth stating on their own, because they come from the vendors rather than from us. Claude Code, whose agent loop the Claude Agent SDK embeds, ships a built-in completion condition in which a separate small model checks after each turn whether a stated goal has been met, and its documentation says of that evaluator that “it does not call tools, so it can only judge what Claude has already surfaced in the conversation” (Anthropic, 2026b). That is an accurate description of the limit this paper is about, published by the party with the most incentive to describe it favorably. The same page documents Stop hooks, an opt-in automated gate that runs a developer’s check each time the agent stops. The same vendor’s guidance on building agents says of having one model judge another that “this is generally not a very robust method” (Anthropic, 2025), while allowing that it can be worth its latency where any gain matters. Meanwhile the human-in-the-loop primitives in the OpenAI Agents SDK and the Claude Agent SDK gate tool calls and not completion claims (OpenAI’s output guardrails can check a final answer, if a developer writes them): a developer can require a person to approve an irreversible action before it is taken, and has nothing built in to require a person to confirm that the finished work was actually correct. CrewAI is the exception among the three current frameworks surveyed: a task can be set to have a human review the agent’s final answer, and like every other gate here that setting is off by default (CrewAI, 2026b). Outside that one opt-in, the approval surface exists for the act and not for the claim, which is the asymmetry the counterweight (this paper’s challenge mechanism, §8) is aimed at. This is not a criticism of those libraries, which are explicit about what they are; it is the baseline against which every mechanism in this paper should be read. The default case is not a weak check. It is no check until a developer adds one, and a stop the agent declares for itself.

One open-source harness makes the distinction visible by shipping both answers at once. Nous Research’s Hermes agent has a stop-time verification path that parses the terminal log for real test, lint and build invocations and records their actual exit status, which is evidence causally downstream of the world rather than of the agent’s narrative, though the agent chooses which commands run and, in a coding task, can edit the tests. It also has a standing-goal judge that calls an auxiliary model with the goal text and roughly the last four kilobytes of the agent’s own final response, with no tool access and, by default, the same model as the agent. The project’s own issue tracker records the predictable failure: an agent reported writing a file, the write silently failed, and the judge marked the goal complete. The release carrying this work (v0.18.0, 1 July 2026) is announced with the line that done means proven rather than claimed, though the same notes set the stop-time verification to off by default. Half of the design lives up to that line; the other half is the stop problem with a second model attached, and it is the half that looks most like verification. We take these details from the project’s public repository, configuration and issue tracker, and they are current as of September 2026 rather than permanent (Nous Research, 2026); the repository had reached release v0.22.2 by 15 October 2026, and the /goal command was changed again in v0.22.0 (14 October), which this paper did not re-read.

The failures in §6.3 led the practice to adopt a design rule in September 2026. Our design rule, from one practice: an instrument the measured party operates will rot; an instrument that operates on them will not. Its test is a single question: can the measured party change the reading without changing the world? The rule restates, for a practice run by AI agents, a principle long familiar in the social sciences as Goodhart’s and Campbell’s laws. Its closest contemporary analogues in model training are reward bias substitution (Lamparth et al., 2026) and the equilibrium result of Wang and Huang (2026). Both also show a measure outside the optimized party being gamed, so external instruments can be gamed too; the rule says which way the risk runs.

Two further results bound what any version of this design can promise. Wan et al. (2026) build the closest published relative of the counterweight we have found: a rubric-guided verifier that evaluates an agent’s answer and returns feedback the agent then refines against, scaled at inference time rather than trained in. That a mechanism of this shape exists, is peer reviewed (Findings of ACL 2026), and works is another reason §7.2 claims no priority. And Wang et al. (2026) state the limit that applies to all of it. Characterizing verification along three dimensions, scalability, faithfulness and robustness, they argue that achieving all three at once is the central unsolved problem, and conclude that no fixed reward function can remain effective as policy capability continues to grow, so verification must co-evolve with the generator.

That last point changes what this program should claim. A counterweight is not a gate that can be specified once and left standing, because the thing it constrains improves and the constraint does not. Wang et al. show this for a policy trained against its verifier; carrying it to a deployed counterweight, pressured by model upgrades and drifting practice, is our step. The honest framing is a point-in-time intervention whose calibration decays, which makes the re-calibration schedule part of the design rather than maintenance, and which means a null result three months from now would not distinguish a mechanism that never worked from one that was overtaken. Nothing in §10 currently measures that decay, and it should.

From §2.3 · The September 2026 record

2.3The September 2026 context

Between the first of September 2026 and this revision (21 September 2026), the public discourse of the frontier laboratories shifted in a way that bears on this paper’s propositions, and the shift is recorded here with dates. On 2 September the chief executive of OpenAI described the coming generation of models as “sobering for everybody” and said that progress would from here be paced by alignment and safety work (Axios, 3 September 2026, interview at the G20 Innovation Ministerial, as carried by The Next Web). In a Fortune interview published on 12 September, the same executive said that “given everything happening with safety, right now would be an ill-advised moment to go public,” and ruled out an offering in 2026 (Fortune, 12 September 2026); both laboratories had been reported in May as preparing public offerings this year at estimated valuations of about $1 trillion each (Fortune, 26 May 2026). On 13 September, on CBS’s Sunday Morning, the chief executive of Anthropic said that “for too long the industry lied to people about the fact that this technology had risks,” called on the industry to slow capability development, and committed his company to permanent access for independent model evaluators (CBS News, 13 September 2026; the web article is stamped as updated 14 September, and CNBC, 13 September 2026, reports the same interview). Semiconductor equities fell on 14 September, which Reuters attributed to the accumulated statements while noting that the 10-year Treasury yield briefly passed 5% that day (Reuters, 14 September 2026, as carried by Yahoo Finance). Each of these is listed with its link under Press and public statements.

Two features of the laboratory leaders’ statements of 1 to 14 September 2026 matter here. First, each concerns what the models will be: more capable, more dangerous, sooner. None concerns how an organization is to use the models it already has, which is the question this practice exists to study, and which, we argue, no maker can answer from where it sits: how to use a model is a fact about the deploying organization’s work, its costs of error and its standards of correctness, none of which is visible from the laboratory. The reported incidents with autonomous AI agents in the same weeks (Fortune, 12 September 2026) bear on exactly that question. Second, no party to the September argument disputed the deployment evidence: the capability and risk claims moved markets while the reported failure rate of enterprise deployments (§4) stood unchallenged. We read the fortnight as corroboration, at the industry’s own scale, of the distinction between capability and direction that §4 draws. Against it: silence is weak evidence, and that failure rate is itself publicly disputed on method (§4). The fortnight is also an instance of §6’s subject: a maker’s public statement about its own unreleased model is self-report, authored by the measured party and not verifiable by its deployers before release. No result in this paper rests on any claim in this subsection.

From §10.4 · Deciding the ending before the data

Pre-registration here means that a decision rule is ratified before its data are examined. Our position: a kill criterion written after the data are seen is a negotiation, not a criterion. Table 8 gives each study’s rule and its standing in September 2026, including where that standard was not met: for Study 1, and for the Study 2 criterion, completed on the day of a first reading.

From §5.2 · A finish line that could not move

The quantity to read is one that can vary. Counting how many of the observed intervals sit at the floor of one session gives the share of the time the class came back immediately. On the program’s status board as rendered at 18:57 UTC on 11 September, the two tracked classes pooled read 76 sessions seen, a median gap of 1.0, 48 of 75 intervals at the floor, and a session share of 0.53, 76 of 144 ordered sessions. That is a number the class could move, but it must be read against prevalence: with no tendency to cluster, about 40 of the 75 intervals (76 × 75 / 144 ≈ 39.6) would sit at the floor by chance, so 48 is above that baseline rather than above zero. The excess is modest: an exact binomial test against the 39.6 expected gives p = 0.033 one-sided and 0.064 two-sided, and it assumes independent sessions, which the lag-one autocorrelation reported in §10.9 (0.25) does not support. A separate live query the same evening returned 74 sessions seen; a cumulative count should not fall, the record does not say why it did, and every figure here is the 11 September render. The floor share is what §10.4’s primary outcome (Study 3’s pre-registered test of this measure) should be read against, and the median that the program ratified as its finish line cannot move at all while most intervals sit on the floor, which is a defect in the finish line rather than in the class. A flat median is therefore uninformative; the floor share, read against that baseline, can show a class being addressed.

From §10.9 · The program as built and as planned

10.9The program as built and as planned, 21 September 2026

The 11 September revision described the counterweight’s engine as implemented and not deployed. In the ten days since, three build waves landed and merged, and the honest way to report them is by what each did to a measurement problem this paper has already named. A wiring audit now runs nightly: of 192 event producers (code paths that emit a logged event) in the practice’s codebase, 122 are wired to a reader, 70 are declared, none are unwired, and a burn-down of 55 grandfathered producers is printed each night, so a signal that reaches nothing can no longer do so silently (§5’s delivery argument, applied to the practice’s own instruments). A delivery switch for the counterweight’s challenge arms is built and deliberately unarmed: until the operator writes the first schedule entry under an account the working seats (agent sessions) cannot modify, every session is unassigned, and unassigned delivers nothing. The sample-size calculation that had existed only as prose now has a producer: at the measured compound-recurrence share of 0.5101 with lag-one autocorrelation 0.2547 and the proposed smallest effect of interest of 0.25, it returns 84 sessions per arm one-sided at 95% under Bernoulli assignment and 140 under cycle-switch (two-sided, 119 and 199; the 11 September prose figures reconciled with this producer on the dependence model and not on the base n, and their method was never recorded, which is why the producer exists). All four figures reproduce exactly if the effect is a 25% relative reduction (0.128 on the share scale) with no power term, which gives roughly even odds of a conclusive result; at 80% power the same inputs give about 191 and 320 sessions per arm one-sided, and 242 and 407 two-sided. Those inputs use a share of 0.5101, while §5.2 and Figure 17 bind Study 3 to the floor share (48 of 75, 0.64, on the 11 September render; the session share is 0.53), so the calculation should be re-run at the baseline the rule is finally ratified on: at 0.64, with the same 25% relative effect and lag-one autocorrelation, the figures are about 49 and 82 sessions per arm with no power term and 111 and 187 at 80% power, one-sided. And a machine-fed adherence instrument is on the nightly: the compaction hook (fired when an agent’s context is compacted) writes a row the seat cannot decline to emit, and a subsequent load is the only thing that clears it. At this revision it reads 70 compactions with 43 reloaded, 18 not, and 9 unresolved, a settled not-reloaded rate of 29.5%; the instrument reports 33% reload for August and 85% for September, though no monthly division of the 43 and 18 reproduces both, so those counts are being re-checked.

What remains is not construction. It is a short chain of operator decisions, recorded here because §10.4’s pre-registration freezes the instrument when signed, so the order matters. First the unit of analysis: a session identifier in this practice spans one turn to ninety-seven and zero compactions to sixteen, so a per-session outcome partly measures session length until the operator rules the unit. Second the feed: both of the primary outcome’s current inputs are typed by the seat being measured, and the record shows what that produces; the practice’s miss log accrued from twenty-two sessions in June and eighty-one in July, through thirty-two in August, to seven in September, a month counted only to the 21st. Conjecture: that is a collapse in voluntary emission, not in error; the counts are not normalized per session-day. The alternative decision log holds fifteen rows in its entire history, six of them written by the session that built the logger. Choosing between a hand-fed instrument with measured decay and a machine-fed one with a third bucket is the operator’s call, and this paper’s own §6 is the argument that it cannot be the seat’s. Third, the compute moves off the operator’s laptop, which suspends scheduled work when it sleeps and therefore cannot host an unattended measurement window. Fourth, the switch is armed, the pre-registration row is signed with the window, the n, and the frozen instrument identities, and the study of §10.4 runs to one of its three registered exits. That is where this program is pointed: challenges delivered on schedule to assigned arms, fed by signals the measured party cannot withhold, scored against a rule fixed before the data existed, on infrastructure that does not sleep, with the operator holding every gate that changes the system. The reader should weigh the plan accordingly: everything in this subsection before the word “remains” is merged and running, and everything after it is intention, dated 21 September 2026.

From §11 · Limits

External. One operator, one domain, one harness, one model family for the agents. Nothing here generalizes to other operators without replication, and the multiple-baseline extension (§10.2: staggered starts for the operator and three co-researchers) raises N to four within one organization, not to a population. The setting’s ecological validity is bought with exactly this cost.

How to cite this part. Atkinson, B. (2026). Rent the pipes. Own the judgment. Background paper 6 to How do we use this? Working paper, Wolfberg LLC.

Disclosure. Drafting and literature synthesis were assisted by AI models (Claude, Anthropic) working under the author’s direction; the author is responsible for the content. The works in the References were read in full by AI reading agents working under the author’s direction, and quotations and numbers taken from them were checked against the saved full text. The prior-art works listed under Prior art are cited at the level of an established concept and its origin: each was verified for author, title, year and venue, but not read in full, and no numeric claim rests on any of them. Software documentation, source code and press accounts are listed under their own headings and were read at the linked pages. Every reference below carries a link, and every arXiv identifier and DOI was resolved against its registry, with title and first author matched, on 23 September 2026.

Competing interests. The author owns Wolfberg LLC, the practice studied.

Data availability. The practice’s logs contain client work and personal records. They are private and are not offered for sale or sharing. The measures are described in enough detail to be reimplemented, and figures from the practice are reported as of the dates given. What is available is the design: the clauses of §8.1, the evidence contract and admission checks of §8.5, and the decision rules of §10.4 are stated in this working paper fully enough to be rebuilt without access to the logs; the background papers reproduce only parts of them.

Corrections to the full working paper. The notes below record corrections to the working paper as a whole; some concern sections not reproduced in this part.

Corrections, 23 September 2026. No figure and no finding changed. The paper was retitled; earlier revisions were titled The Stop Problem: Defensible Is Not Correct, and the stop problem remains this paper’s name for the failure it studies. The Anthropic interview in §2.3 aired on 13 September, not over a weekend of 13 and 14 September; the web article is stamped 14 September. The essay listed under Prior art is by Ryan Forstie; an earlier revision gave the initial K. The completion evaluator in §8.3 is documented as a Claude Code feature, whose agent loop the Claude Agent SDK embeds; an earlier revision attributed it to the SDK directly. Two works in the References, Graves (2016) and Liu (2026), were listed without being cited in the text; each is now cited where it bears (§2.1, §8.3). Links were added to every press, documentation and prior-art source. A duplicated section number in §10 was corrected.

Corrections, 25 September 2026. No figure changed. §8.3 said that the human-in-the-loop primitives across the three frameworks surveyed gave a developer nothing to require a person to confirm finished work. That holds for the OpenAI Agents SDK and the Claude Agent SDK. It does not hold for CrewAI, whose task documentation, already cited as CrewAI (2026b), offers an opt-in setting for a human to review the agent’s final answer; §8.3 now says so.

Corrections, 7 October 2026. After review by a panel of AI reviewers: a quotation in §4.2 from Mytsyk, Zhang and Krishnamurthy (2026) carried words its source does not contain (“or truthfulness, only about sycophancy”) and now reads as the source does, “Our results say nothing about correctness”; the point it supports is unchanged. The §8.3 design rule is no longer stated as a law: an instrument the measured party can operate tends to rot; one it cannot alter is harder to game, though not immune. Zhang et al. (2026) is now read as a gain from training, not from moving a check outside the agent (§8.3, Figures 10 and 11). Laban et al. (2023) replaces Kelley and Riedl (2026), whose tasks have no ground truth, in Table 5 and H3. The 48% is now the share of operator turns the detector flagged, roughly a third adjusted; the 27× logging gap is about 19× adjusted and partly definitional. Table 9 separates measured counts from interpreted links, §13 offers its design rules as hypotheses, and the Disclosure says who read the cited works. A second pass the same day restored, in the author’s wording, positions that revision had softened for reasons other than fact, and labelled each for status (our position, conjecture, from one practice, measured) instead of hedging it; Table 9 now carries a Status column and §13 states its design rules as our position. Every factual correction above stands.

Works cited in this part (keys, including letter suffixes such as 2026b, follow the full paper’s reference list)

References

1. Lamparth, M., Fein, D., Haupt, A., Hussing, M., & Kochenderfer, M. J. (2026). Reward bias substitution: Single-axis bias mitigations redirect optimization pressure. arXiv:2605.27996. arxiv.org/abs/2605.27996
2. Park, H., & Choi, B. (2026). When do agent loops mistake stagnation for progress? Self-evaluation bias and externally grounded verification in long-running autonomous LLM agent loops. arXiv:2607.25152. arxiv.org/abs/2607.25152
3. Wan, Y., Fang, T., Li, Z., Huo, Y., Wang, W., Mi, H., Yu, D., & Lyu, M. R. (2026). Inference-time scaling of verification: Self-evolving deep research agents via test-time rubric-guided verification. Findings of ACL 2026. arXiv:2601.15808. arxiv.org/abs/2601.15808
4. Wang, B., Zhang, C., Liu, D., Zhang, J., Chen, J., Li, M., Chen, M., Fang, R., Zhang, S., Wang, X., Jing, Y., Ma, Z., & Cui, Z. (2026). The verification horizon: No silver bullet for coding agent rewards. arXiv:2606.26300. arxiv.org/abs/2606.26300
5. Wang, J., & Huang, J. (2026). Reward hacking as equilibrium under finite evaluation. arXiv:2603.28063. arxiv.org/abs/2603.28063

Prior art (cited at concept level; verified for author, title, year and venue, not read in full)

1. Forstie, R. (2026). The part of the agent stack nobody wants to build. LinkedIn, 25 August 2026. linkedin.com/pulse/part-agent-stack-nobody-wants-build-ryan-forstie-ihyqc Cited for its three closing questions on managed-agent platforms, first read on 1 September 2026; author, title, date and the three questions re-verified at the source on 23 September 2026. No figure from it is cited anywhere in this paper.

Software and documentation (read at the linked pages, September 2026; current as of then, not permanent)

1. Anthropic. (2025, September 29). Building agents with the Claude Agent SDK. claude.com/blog/building-agents-with-the-claude-agent-sdk
2. Anthropic. (2026a). How the agent loop works. Claude Agent SDK documentation. code.claude.com/docs/en/agent-sdk/agent-loop
3. Anthropic. (2026b). Keep Claude working toward a goal. Claude Code documentation. code.claude.com/docs/en/goal
4. CrewAI. (2026a). Agent output parser (source code). github.com/crewAIInc/crewAI, the agent output parser
5. CrewAI. (2026b). Tasks. CrewAI documentation. docs.crewai.com/en/concepts/tasks
6. Nous Research. (2026). Hermes agent (repository, configuration and issue tracker). github.com/NousResearch/hermes-agent
7. OpenAI. (2024). Swarm (repository; experimental, superseded by the Agents SDK). github.com/openai/swarm
8. OpenAI. (2026a). Running agents. OpenAI Agents SDK documentation. openai.github.io/openai-agents-python/running_agents/
9. OpenAI. (2026b). Human in the loop. OpenAI Agents SDK documentation. openai.github.io/openai-agents-python/human_in_the_loop/

Press and public statements (§2.3; read at the linked pages, or at the named carrier where the original is paywalled)

1. Axios. (2026, September 3). Sam Altman’s sobering siren. Interview at the G20 Innovation Ministerial. axios.com/2026/09/03/axios-interview-sam-altmans-sobering-siren; paywalled, read as carried by The Next Web: thenextweb.com/news/sam-altman-axios-idea-guy-sobering-models
2. CBS News. (2026, September 13). Anthropic CEO Dario Amodei: “For too long the industry lied” about AI risks. Sunday Morning; web article updated 14 September. cbsnews.com/news/anthropic-ceo-dario-amodei-on-ai-risks/
3. CNBC. (2026, September 13). Anthropic’s Amodei says China presents “toughest dilemma” for his proposed AI slowdown. Cited for the broadcast date. cnbc.com/2026/09/13/china-dilemma-ai-slowdown-anthropic.html
4. Fortune. (2026, May 26). On the two laboratories walking back earlier job-loss forecasts ahead of public offerings; cited for the reported valuations. fortune.com/2026/05/26/sam-altman-dario-amodei-walking-back-ai-jobs-apocalypse-prophecies-ipo/
5. Fortune. (2026, September 12). Interview with the chief executive of OpenAI on safety and the timing of a public offering. fortune.com/2026/09/12/sam-altman-interview-ai-doomsday-safety-models-control-ipo-2027/
6. Reuters. (2026, September 14). Wall Street ends down, calls for AI slowdown pummel chipmakers. As carried by Yahoo Finance. finance.yahoo.com/technology/ai/articles/ai-warnings-knock-nasdaq-futures-092329455.html