Afternoon briefing · 16:00
Agent settings: the unseen variable
By · bombong
What is configured inside work agents is emerging as a variable that shapes human judgment. In an experiment by Anthropic's alignment team, professional analysts reportedly followed a lean secretly planted in the agent most of the time38. On the same day, DeepSeek and ByteDance released features that bring models from multiple companies into a single workflow3435. As a result, the layers of configuration that need checking are multiplying.
Hidden settings move experts' conclusions
An agent's default settings can change even experts' conclusions without users noticing. According to a community post, Anthropic's alignment team on October 27 published results of an experiment in which 50 professional analysts ran realistic statistical analyses with Claude Code. For each of four analytical choices, the researchers tilted the agent one way using a hidden system prompt (a prior instruction not visible to the user). The direction of each tilt was decided by coin flip, and the analysts were not told.
The results skewed clearly in one direction. According to the post, analysts chose the option the agent leaned toward 86% of the time. Chance would have produced about 50%, and by item the rate ranged from 81% to 92%. Even analysts who had written down their own preference in advance switched their choice 60% of the time when the agent disagreed, versus only 6% in the comparison condition. The finding is that, expertise notwithstanding, an agent's suggestion acts as a strong anchor. What needs checking therefore extends beyond an agent's output to the settings behind it.
More models, more points to check
The problem gets more complex as agents move toward structures that combine models from multiple companies. According to Pandaily, DeepSeek Harness v0.2.2-alpha.2 includes an experimental feature called 'subagent_oar.' It lets the main agent hand off separable tasks to Codex CLI or Claude Code installed on the user's PC. ByteDance's Doubao Work has also allowed customers on team and enterprise plans to use outside models such as Kimi K3.1 Pro, GLM-5.3, and DeepSeek-V4.1-Flash. In both developer tools and work services, setups in which multiple models take part in a single task are increasing.
When multiple models are chained into one workflow, it is hard for users to see at a glance which instructions and defaults apply at each step. The skew in the previous section was measured on a single agent. But in a structure where each subagent carries a different system prompt, the number of points to check grows accordingly. On the execution-environment side, responses have begun. According to the headline of an InfoQ China report, supply-chain security company Chainguard has launched a secure workspace and security verification features for AI coding agents37. It is an attempt to separately control the dependencies and execution environments that agents pull in.
Evidence is still thin
Still, the evidence is too thin to draw firm conclusions about this trend. The Anthropic experiment was relayed through a community post, and because the excerpt was cut off, it is unclear exactly what the comparison condition that produced 6% was. The experiment also dealt with settings that were deliberately tilted. Whether ordinary product defaults have a similar degree of influence is a separate question.
Signals on the product side are also early. DeepSeek's feature is an experimental one in a pre-release, and the scope, form, and pricing of Chainguard's product could not be confirmed from the excerpt. How widely multi-model setups will be used in real enterprise settings, and how quickly mechanisms to surface configurations will follow, remain to be seen.
Companies adopting agents should, alongside comparing model performance, add to their checklist who sets the instructions attached to agents and subagents and how they are recorded. For analysis and decision-making work, they could also consider a procedure in which people write down their own judgment before seeing the agent's suggestions. Going forward, things to watch include publication of the Anthropic experiment's original text and comparison condition, whether DeepSeek's experimental feature makes it into a stable release, and detailed specifications of Chainguard's product.
This piece was written with the help of AI and includes personal views.
Morning briefing · 08:00
Cloud growth, paid for in cash
By · bombong
AI demand is now showing up in cloud revenue. But the cost of the infrastructure behind that demand is also becoming visible, in deteriorating cash flow and borrowing. With earnings and financing news from three big tech companies arriving on the same day, growth and cost appeared in a single frame.
Revenue proved the demand first
The clearest evidence that AI demand is real comes from cloud growth rates. Amazon announced on the 29th (local time) that third-quarter revenue rose 12% year over year to $201.9 billion2. Total revenue and earnings per share missed market expectations, but according to Electronic Times (etnews), AWS revenue grew 39%, its highest growth rate in about five years. At Alphabet, Google Cloud revenue rose 103% year over year, and API throughput for its own models and Gemini app users also reportedly increased sharply6.
These numbers matter because the growth is not limited to one company. Following Microsoft Azure and Google Cloud, AWS too saw growth accelerate on AI demand. Evidence is accumulating that demand is growing across the entire infrastructure layer, not just from the popularity of a particular model or service.
The same earnings also carried numbers on the cost side. Amazon's free cash flow deficit widened as AI infrastructure investment increased. Ars Technica reported that Google's cash flow deficit grew to $9.06 billion and that its shares fell after the earnings release. This can be read to mean that even as revenue grows quickly, capital spending is being deployed even faster.
The cost moves to the bond market
The strain on cash flow is leading to changes in how companies raise money. According to Electronic Times (etnews), citing Bloomberg, Microsoft plans to raise at least $25 billion in investment-grade corporate bonds in the U.S. bond market5. It is Microsoft's first U.S. corporate bond issuance since 2017. The bonds are split into 10 tranches with maturities from 2 to 40 years.
That a company with large cash holdings has turned to external borrowing points in the same direction as the earnings in the previous section. The structure, with maturities extending to 40 years, can be interpreted as a design to match long-term debt to long-lived assets such as data centers. It means AI infrastructure costs have moved beyond each company's income statement into a stage where they are judged by capital markets.
Changes in revenue structure are also appearing. Google acknowledged that AI search generates less revenue than traditional search and said it will make up for this by increasing ads. The company itself has confirmed that while infrastructure costs grow, unit revenue in its existing core business may actually thin out.
The path to recouping the investment is still open
With costs being incurred up front, the remaining question is where the demand to fill this capacity will come from. On the product side, moves to build agent capabilities into everyday services continue. According to AI Times, Thibault Sottiaux, OpenAI's head of ChatGPT and Codex, said on X that "we plan to combine the best of chat and Work this year"3. Once merged, users will be able to write documents and operate external programs in a single conversation without switching modes. However, concerns are emerging in the community over Plus plan usage limits, and who will bear the cost of agent work, and how, remains undecided.
Enterprise cases show that demand is entering real work. MoEngage used Amazon Bedrock AgentCore Gateway to turn about 400 private REST APIs across 7 regions into permission-scoped agent tools11. Epsilon uses Amazon Nova Act to run UI tests written in plain English sentences as a multi-tenant service12.
But there are also obstacles to adoption. At a GitHub Universe 2026 panel, representatives of OpenAI, Anthropic, and Google named managing access permissions for each agent and tracking their execution as a shared challenge24. Whether such cases will turn into revenue fast enough to justify the current scale of investment has not yet been confirmed by the numbers.
Cloud providers will actively court agent workloads to fill their expanded capacity. Companies adopting AI services should plan for possible changes in cost structure rather than assuming current prices and usage limits will hold. Going forward, it will be worth checking how the ChatGPT chat–Work merger applies by plan, what basis underlies the efficiency metrics for Microsoft's announced large-scale Maia-based inference deployment, and how big tech cash flows change next quarter.
This piece was written with the help of AI and includes personal views.
Unconfirmed reports 9
Not yet confirmed by an official source. Check the original report.
- "Cohere announces multibillion-dollar Series E with up to $1.6 billion from Canadian government," criticized amid Qwen experiment controversy (community report, unconfirmed) Original
- IBM releases 'Granite Switch 4.1.1': "update models like software" Original
- Apex Intelligence releases 'Apex-35B-V1.0,' built on Qwen3.6-35B-A3B with help from an AI research agent; claims 32.1-point CFD-Bench gain (report) Original
- Van der Waerden number W(2;3,21)=416, conjectured in 2010, claimed proven with Lean 4 kernel-checked proof using large-scale proof generation tools (community report) Original
- Source claim spreads that OpenAI's next model 'Bel' is GPT-6.2 Betelgeuse (community report, unconfirmed) Original
- Claude Opus 5.5 and GPT-6 Astra reportedly flag 2,035 potential issues across 138 economics papers, 147 possibly conclusion-changing (community report, unconfirmed) Original
- "Moonshot AI and DeepSeek distilled OpenAI's GPT-6 Astra for new models," with Astra replacing Claude as top target (The Information, via Reddit; unconfirmed) Original
- "Anthropic renames Max5 and Max20 plans and drops Max20's 5-hour usage limit" (community reports, unconfirmed) Original
Community buzz 5
Topics widely discussed on Reddit, Hacker News, GeekNews and elsewhere. These are community reactions and may include unverified claims.
- Aversion to LLM writing style Discussion centered on Martin Fowler's 'LLM Voice' and a GeekNews post on 'the uncanny valley created by LLM writing style,' arguing that LLMs' characteristic tone reduces the desire to read regardless of whether AI was actually used. A post tracking unsourced statistics cited by AI ('zombie statistics') also drew attention.
- ChatGPT chat–Work merger and usage limits After news of merging chat and Work emerged, users worried that Plus subscribers would be pushed aside. Meanwhile, a series of posts said usage limits had returned to normal and doubled.
- AI agent swarms and hard scientific problems On r/singularity, users shared a post saying Sam Altman said agent swarm coordination would improve greatly within three months and could be used to automate AI research, a remark that more than $10 million in compute was spent on the Navier-Stokes problem, and accounts that models had solved a string of hard problems posed by researchers. A post claiming a Claude Sonnet 5 agent performed brain surgery on a mouse also drew attention.
- Silent model swaps in cloud APIs One user analyzed 40 days of coding agent logs and claimed that providers silently switched the deepseek-v4.1-flash endpoint to -1017 weights around October 19, after which model swaps increased 6.7-fold.
- Decision models vs. traditional classifiers A Voyage employee shared an experiment showing that the traditional approach of putting an sklearn model on top of embeddings was cheaper than the decision model Jev and had 37% fewer errors. Arc, a 70MB streaming speech model that does transcription, entity linking, and speaker tagging in one pass, also drew interest.