Google Cloud Next's infrastructure and agent platform announcements dominated today's coverage, with Shopify's CTO providing the most data-grounded account of enterprise AI adoption in circulation, and practitioners at AIE Miami converging on a concrete reframe for how to evaluate AI-generated work.

The framing shift at Cloud Next this year was deliberate: from "pilots" to "running at scale." The hardware anchoring that claim is substantial. Google announced TPU 8t for training (9,600 chips per pod, 3x compute vs. the prior Ironwood generation) and TPU 8i for inference (1,152 chips per pod, 3x SRAM, a new Collectives Engine for low-latency multi-agent workloads), the first time Google has separated training and inference into distinct silicon. One number from analyst commentary is harder to contextualize: TPU 8t can reportedly scale to 1 million chips in a single cluster. Storage announcements included 10 Tb/s Lustre deployment (5x the next competitor) and 15 Tb/s Rapid Storage at microsecond latency.

Gemini is generating 16 billion tokens per minute, up from 10B in December, with Gemini Enterprise growing 40% sequentially. Kurian framed the token growth the same way Google's financial results frame it: proof of demand. The agent platform announcement (positioned as the evolution of Vertex AI) now includes a Knowledge Catalog, an automatically built semantic graph where Gemini reads documents and API specifications to construct the organizational dictionary linking terms like "part number" to the specific database tables containing that data. Kurian positioned this as a no-field-deployment-engineer alternative to what Palantir does with its ontology, though the dependency on Iceberg-format data and well-specified APIs limits how frictionless it actually is in practice.

The cybersecurity integration is more consequential than the product names suggest. The Threat Intelligence Agent compresses a 30-minute investigation to 30 seconds and has processed 3.9 million threats. Three Wiz-integrated agents (Red for continuous red-teaming, Blue for triage, Green for automated remediation) close the loop from detection to fix. Google reports 75% of its internal code is now AI-generated, deployed as validation that the same infrastructure runs at Google's own scale before customers touch it.

Kurian was explicit on the TPU business model: selling compute to labs like Anthropic is not a contradiction of the Gemini strategy. It drives utilization, lowers cost of goods sold, and funds R&D. New third-party venue deployments (capital markets firms needing on-premise inference for latency reasons, national labs with data gravity constraints) expand the total addressable market without cannibalizing cloud. The coherence of the argument is notable even if it's a CEO doing the arguing.

At AIE Miami this week, the conversation among CTOs and VPs coalesced around "tokenmaxxing" — and immediately complicated it. Jensen Huang's directional argument (that engineers not consuming high token volumes are underutilizing coding agents) drew qualified agreement but a strong caveat on measurement.

Shopify's data is the most concrete available. Mikhail Parakhin, CTO, showed near-100% daily active AI tool usage across the company, with a sharp December 2025 inflection point. CLI-based tools (Claude Code, Codex, Shopify's internal River agent) are growing faster than IDE-based tools like Cursor and Copilot. Shopify funds unlimited token budgets with a floor, not a ceiling: "Please don't use anything less than Opus 4.6." Token consumption is skewing toward power users (top percentiles growing faster than the median), which Parakhin flagged as worth watching rather than celebrating — taken to the limit, it means one person consuming all the tokens.

The more actionable insight: running many parallel agents is the anti-pattern. The high-value mode is serial critique loops — one model generates, a different model critiques, the first revises — which takes longer per session but produces substantially higher quality. Parakhin framed the right metric not as token volume but as the ratio of generation tokens to review tokens, with expensive frontier models handling PR review rather than code generation.

The surface-level paradox is real: models write cleaner code on average than humans, but because they write so much more code, more bugs reach production. PR merges at Shopify are growing 30% month-on-month (was 10%). The binding constraint is now CI/CD — more PRs cause more test failures, which cause longer deployment cycles and more rollbacks. Time saved generating code faster is partially consumed by the expanded debugging cycle downstream. Parakhin noted that microservices (which he's been a lifelong opponent of) may see a comeback precisely because independent deployable units reduce the global-mutex problem that git/PR workflows create at machine-code-generation speeds.

Swyx noted that Dex Horthy (who coined "Context Engineering" and was publicly vibe-coding-pilled 6 months ago) publicly retracted his position this week, encouraging engineers to actually read generated code. The practitioner consensus is landing on depth over breadth: serial auto-research loops rather than parallel agent swarms.

Parakhin detailed 3 internal systems worth understanding for anyone building ML infrastructure at scale. Their value compounds when combined, which is the point.

Tangle is Shopify's ML workflow engine, production-ready from day 1 rather than requiring a separate porting step from development. The key design choice is content-addressed caching: if an output's content hash hasn't changed, the step doesn't rerun. The more valuable property is cross-team — if 2 independent teams start pipelines sharing upstream steps, the second team's pipeline uses the first team's cached result automatically. Network effects compound with adoption.

Tangent is the auto-research loop built on Tangle, an agent that runs experiments and iterates toward a defined goal without human intervention. Results cited include: search throughput from 800 to 4,200 QPS on the same hardware through pure optimization; prompt compression quality improvements; storage reduction via automated identification of derivative datasets (one find: the largest table in the system was translating random IDs into other random IDs and was simply eliminated). Tangent has democratized to the point where a PM is currently the highest internal user by volume. Parakhin's honest ceiling: auto-research is excellent at "obvious improvements you didn't have bandwidth to do" and weak at genuinely out-of-distribution insights. He ran 400 experiments on a well-optimized personal project and got 1 improvement — which he still considered a win.

SimGym is customer simulation grounded in historical data, and that grounding is what distinguishes it from the generic approach. Without decades of real merchant and buyer behavior, simulated agents replicate whatever you prompt them to. With Shopify's history, SimGym can calibrate to a target of 0.7 correlation with real add-to-cart events — simulated A/B tests that predict real A/B test outcomes. The system now runs over single live storefronts and generates conversion improvement recommendations. Infrastructure cost is high (browser farms, multimodal models, partitioned GPUs), and the workload "violates almost every assumption standard LLM serving is designed for."

On Liquid AI: Shopify is running a 300M parameter Liquid model at 30ms end-to-end for query understanding, a workload where transformers can't compete on latency. For large-scale batch catalog work, Liquid's hybrid architecture (state space models plus transformer elements) outperforms alternatives on long-context efficiency. Parakhin is one of the few practitioners willing to say directly that Liquid is the only non-transformer he's found genuinely competitive, and it's been taking share from Qwen internally for specific workloads. His framing: if Liquid had Anthropic- or Google-scale compute, it would be competitive at the frontier.

Alibaba released a 27B dense model that outperforms its own 397B predecessor on every major coding benchmark. SWE-bench Verified: 77.2 vs. 76.2. SWE-bench Pro: 53.5 vs. 50.9. Terminal-Bench 2.0: 59.3 vs. 52.5. SkillsBench: 48.2 vs. 30.0. The size comparison makes the efficiency story concrete: 807GB down to 55.6GB, with a 4-bit quantized version (Q4_K_M) running in 16.8GB RAM. Simon Willison ran it locally at 25.57 tokens/second and described the SVG generation output as "outstanding for a 16.8GB local model." Day-0 ecosystem support from vLLM, Unsloth, llama.cpp, and Ollama; Apache 2.0 license.

The benchmark story has a complication. One practitioner showed that pairing Qwen3.6-35B with a different agent scaffold bumped Polyglot benchmark performance from 19% to 78% — a 4x swing purely from harness choice, with identical weights. If scaffold selection creates variance of that magnitude, comparisons between models running on different harnesses are measuring something other than model capability. The coding model eval landscape has a harness control problem that the field hasn't resolved.

Two smaller open model releases worth noting: OpenAI quietly open-sourced a Privacy Filter — a 1.5B parameter (50M active) mixture-of-experts (MoE) token-classification model for PII detection and masking with 128k context, Apache 2.0. More operationally useful than a generic small model release: it targets cheap on-device redaction before data reaches a larger model, a concrete preprocessing need in enterprise and agent pipelines. Also: Xiaomi's MiMo-V2.5-Pro claims SWE-bench Pro 57.2 and 1,000+ autonomous tool calls, with a 1M-context non-Pro variant, though both lack the ecosystem velocity of the Qwen release.

Anthropic released Mythos — its most capability-restricted model to date — to select partners on April 10 under the internal name "Project Glasswing," deemed too dangerous for public release. Within days, a Discord group had accessed it. The mechanism: naming patterns exposed in the recent Mercor data breach let the group construct Anthropic's deployment URL; a member with contractor credentials completed the access. The group claims no malicious use and also reports access to other unreleased models.

The failure mode matters more than the incident itself. The first unauthorized access to a model that triggered White House-level concern came from a hobbyist Discord group combining public breach data with pattern matching — not a sophisticated state-sponsored operation. Every partner access program expands the credential attack surface, and every third-party contractor database breach potentially exposes deployment infrastructure details. The security model for "too dangerous to release publicly but accessible to partners" relies on secrecy that data breaches systematically erode.

TL;DR - Google Cloud Next announced TPU 8t/8i (training/inference split, 3x vs. prior gen), 16B tokens/minute on Gemini, and a vertically integrated stack spanning chips, models, agents, security, and multi-cloud data — with the Knowledge Catalog and Wiz integration as the most practically differentiated pieces. - Shopify's near-100% internal AI adoption data points to a counter-intuitive finding: the binding constraint is now PR review and CI/CD stability, not code generation, and the right token investment is serial critique loops with expensive models at review time rather than parallel agent swarms at generation time. - Qwen3.6-27B beats its 397B predecessor on every major coding benchmark and runs locally in 16.8GB, but a 19%→78% swing from scaffold choice alone exposes how poorly current coding evals control for harness effects. - Anthropic's restricted Mythos model was accessed by a hobbyist Discord group within days of its partner launch using patterns from a third-party breach, illustrating that selective access programs scale attack surface faster than the security assumptions protecting them.