The Investor

The 40% is two levers, and only one is guaranteed

Simplifying AI splits Anthropic's number into two parts: a 20% cut in per-token price and roughly 25% fewer tokens per task. Together they bring cost to about 0.6 of the old level. The price cut applies to every call. The token saving depends on the work being done, and Anthropic measured it on “typical” workloads. A portfolio company running long agent loops on unusual code may capture less. Three claims come from Anthropic's own measurements, not independent evaluations: the cost figures, the more-than-30% speed gain, and the claim that Opus 5.5 matches Claude Fable 5.1 on most tasks.

If you have exposure to the model layer itself, the arithmetic runs the other way. Simplifying AI calculates that each workload moving from Opus 5 needs about 1.67x more volume to keep Anthropic's revenue flat. Anthropic is pricing a Fable-class model below its own flagship. That is deliberate self-cannibalization aimed at winning share in coding and agents.

Why most of the saving won't stay with the companies you back

Every rival of a Claude-based app got the same cheaper model on the same day. It went live on the Claude API, Amazon Bedrock, Google Cloud and Microsoft Foundry. TheSequence's read is that the saving stays with an application only where the company has pricing power. Otherwise competition hands it to customers. Simplifying AI expects that pass-through within a few quarters.

The same week also lowered the cost of switching. HarnessRouter puts Claude Code behind one interface alongside Codex and DeepSeek's harness, so changing an agent's backend becomes a configuration edit. CopilotKit released OpenMuse, a free, MIT-licensed clone of Meta's new Muse agent. Architecture Notes reports that Google's ax is trying to set the convention for how agent sandboxes are defined. With switching this cheap, a company's claim to keep the saving has to rest on its workflow data, contracts and distribution, not its model access.

The open-weight price floor needs one correction: free weights are not free inference. MiMo uses about 42B parameters per token, which keeps compute per token manageable. But holding more than 1T total parameters in memory takes multi-GPU serving that most enterprises won't run themselves, so that demand flows to managed inference hosts. Simplifying AI flags a possible limit, which it labels as its own inference: Chinese-origin weights may face procurement friction in regulated sectors. The source also does not report how MiMo's score compares with closed frontier models.

For image-heavy apps, Simplifying AI's illustration is concrete. At 10M images a month, an app would pay about $240K on Hy Image 3.5 versus about $1.2M at Nano Banana Pro's implied price. That saving only materializes if Tencent's self-reported quality holds up under independent testing.

The smart move

Treat the cut as a pricing decision each portfolio company must make on purpose, not a margin gain to book automatically. A company that plans to keep the saving should be able to say why its customers cannot route around it. A company that plans to pass it through should show the volume or share it expects in return. Either answer is defensible. Without one, a competitor makes the decision for it.

What rewriting the benchmark revealed

Researchers at SJTU, XJTU and ECNU built SchrodingerRepo by rewriting SWE-bench problems so the code behaved identically but looked unfamiliar. Pass@1, the share of problems solved on the first attempt, fell by 6.0 to 14.4 points across GPT-5.1, GPT-5.4-mini, DeepSeek-v4-Flash and Gemini-3.1-Flash-Lite. The agents also consumed more than 2.5x the input tokens, and 81.6–83.6% of their extra actions went to exploring the code. TheSequence's point is simple: every customer's private codebase is unfamiliar by definition. Decks built on benchmark scores therefore overstate capability and understate cost in exactly the setting that pays.

The unfamiliarity tax can swallow the model discount

TheSequence offers a rough illustration. Paying 2.5x the tokens at 0.6x the price still leaves cost per task at about 1.5x the naive estimate. The inputs come from different papers and setups, so treat the result as directional. For the coding-agent companies you back, the headline price cut does not settle gross margin. Two things settle it: how much an agent must explore before it can change a customer's code, and how much of that exploration the company has engineered away.

Some levers already exist. Salesforce AI Research's JIT Mem, a just-in-time memory method that fetches stored context only when the agent needs it, cut input tokens by 50.3–56.3% in testing. Researchers at Alibaba and Georgia Tech post-trained a mid-size open model, Qwen3.6-35B-A3B, with VHD-Play. Its agentic score rose from 0.204 to 0.815. TheSequence notes that JIT Mem's gain comes from its architecture, so memory alone is easy to copy. A company that owns neither lever pays the full tax.

Pricing the revenue proof

Demand is not in question. Cognition says Devin passed $1B ARR less than two years after general availability. Lovable added roughly $33M of net new ARR per month between June and September, about 20% growth in a quarter. Its $13.3B August valuation against ~$600M ARR works out to roughly 22x current run rate, on about 2x annualized growth. TheSequence proposes that multiple as the reference ceiling for the category.

Two cautions apply. First, Lovable's claim to reach about two-thirds of the Fortune 500 is self-reported, and it counts individual users at those companies, not enterprise contracts. Second, Devin's milestone says nothing yet about gross margin, which is where the unfamiliarity tax shows up.

The smart move

Replace benchmark evidence with production evidence in every coding-agent process. Ask for three numbers on customers' private repos:

• the merged-PR rate, meaning the share of the agent's proposed code changes that customers accept;
• the review-survival rate;
• the cost per accepted task.

If a company insists on benchmarks, ask for contamination-resistant ones built the SchrodingerRepo way. Any growth round priced above the Lovable ceiling should be tested on one question: does the company's growth rate match Lovable's?

Security: one big payer just priced a finding at zero

Intel ran its bounty program for more than a decade, raised rewards sharply after the Spectre and Meltdown chip flaws, and paid academics studying side-channel attacks tens of thousands of dollars by their own accounting. Its page on Intigriti, the platform that hosts it, now reads “No bounty.” The last archived snapshot showing payouts is dated September 13. Intel has refused to explain the change. Catalin Cimpanu of Risky Business calls it the first domino. Companies that ran bounties for reputation now have a cost excuse to stop paying.

The chain here is simple enough to be suspicious of: AI multiplies findings, so each one is worth less. Pricing power moves downstream to validation, prioritization and remediation. Platforms that earn fees on payouts get squeezed, and their escape route is to become the AI triage layer. That is a different company than the one they are now. Risky Business names the second-order risk too: smaller legitimate payouts push top researchers toward exploit brokers, so more undisclosed flaws end up used in attacks. One payer is one payer. A category re-rating needs two.

Operations and code: the human check is still the product

In reliability engineering, an r/sre thread flagged by Lex Neva asked whether automated root-cause analysis (RCA) can “actually nail root cause without a person doing the final synthesis, or is that still mostly aspirational marketing from vendors.” It drew a large, engaged response. Plenty of AI SRE valuations assume the tool replaces that final synthesis outright. If buyers experience it as a copilot instead, the pricing model, expansion path and TAM shrink together. One thread is a hypothesis to test in reference calls, not a survey.

Architecture Notes describes the same wall in software teams. Agents now change systems faster than humans can review every diff, and nobody has solved how to help people follow the architectural decisions behind those changes. Ayman Nadeem built Nuanced around persistent plans. He has since concluded publicly that long AI-generated specs added friction without improving understanding. Founders rarely say that part out loud.

Judgment can be taught, so decision data becomes the moat

TheSequence's coverage of Taste-Bench sharpens the picture. Taste-Bench poses 502 questions about choices at decision points, and the best frontier model, GPT-5.6 Sol, scored 59.7%, with larger reasoning budgets buying nothing at all. A distilled Qwen3.6-27B advisor, meanwhile, lifted a fixed executor on SWE-bench Pro from 14.6% to 33.7%. That lift rests on only 41 held-out tasks. This is probably too clean, but the thesis follows anyway: if judgment can be learned from recorded decisions, then proprietary trajectory data, meaning logs of which choices worked, becomes a real advantage for whoever owns it. The counter-thesis is that 41 tasks is a rounding error and the advisor is an artifact of one benchmark's quirks.

The smart move

The diligence question worth stealing from Risky Business is whether a company's revenue grows with the volume of AI output its customers must check, or with the volume of output it produces itself. The first kind of revenue rides AI adoption. The second is being commoditized by it. Sourcing follows from there, favouring companies that pair the check with proprietary exploitation or decision data, which also means hours not spent on vendors whose entire pitch is raw throughput. Before re-rating bounty platforms as a category, wait for a second major payer to follow Intel.