Meta's Q1 2025 results on April 30 lifted full-year capital expenditure guidance to $64-72 billion from $60-65 billion, citing additional data-center investment for AI and a higher expected cost of infrastructure hardware; the same release puts Meta AI at almost 1 billion monthly actives. Microsoft reported the same day: revenue of $70.1 billion, Azure growth of 33%, and $21.4 billion of capex in the March quarter, up 53% year on year. CFO Amy Hood told the call that capex keeps growing in FY26 but at a lower rate than FY25, weighted toward short-lived assets correlated with revenue — from a company that had spent recent months slowing or pausing some data-center projects.

The two numbers price capability in opposite directions on the same day. A raised Meta budget says capacity is scarce enough to justify paying more for servers; Microsoft's decelerating curve and shift to revenue-linked hardware says the build-out is being sized to demand rather than ambition.

Alibaba's Qwen team released Qwen3 on April 29: eight open-weight models under Apache 2.0, two of them mixture-of-experts — Qwen3-235B-A22B (235B total, 22B active, 128K context) and Qwen3-30B-A3B (30B total, 3B active, 128K) — plus dense 32B, 14B, 8B, 4B, 1.7B and 0.6B checkpoints. Every model runs in two modes, a thinking mode that emits a reasoning trace and a non-thinking mode for latency-critical calls, toggled per request through the chat template's enable_thinking flag. Qwen reports the flagship as competitive with o1, DeepSeek-R1, o3-mini, Grok-3 and Gemini 2.5-Pro on coding, maths and general benchmarks, and says the 30B MoE beats QwQ-32B while activating a tenth of the parameters.

The cost consequence is arithmetic: activating 22B parameters per token carries roughly the memory and FLOP profile of a 20-30B dense model, so frontier-adjacent quality now deploys on hardware teams already own. Supported checkpoints landed in vLLM and Ollama on day one alongside OpenAI-compatible endpoints, so moving a workload off a closed API is a config change rather than a migration. Apache 2.0 also removes the acceptable-use and reporting clauses that turned some earlier open weights into a legal review, and the small dense models exist mainly as distillation targets for teams that want the behavior at a fraction of the footprint.

On April 29, researchers from Cohere Labs and academic collaborators posted The Leaderboard Illusion (arXiv:2504.20879, 68 pages), an audit of Chatbot Arena's ranking pipeline. They document an undisclosed practice that let a small group of preferred providers test many variants privately and publish only the best-scoring checkpoint, and at the extreme they identify 27 private LLM variants Meta tested in the lead-up to the Llama-4 release. On data, they estimate Google and OpenAI received 19.2% and 20.4% of all arena battles respectively, while 83 open-weight models combined received 29.7%. Simulations put the payoff of ten private variants at roughly 100 Arena points, and two identical checkpoints of the same model scored 17 points apart purely from sampling randomness.

This lands three weeks after Llama 4's own leaderboard problem, when Meta submitted a non-public experimental chat version of Maverick to LMArena and LMArena replied that Meta's reading of its policy did not match what it expects from model providers. The paper's contribution is that the anecdote generalizes: sampling rates, model deprecation and score retraction are all asymmetric, so single-digit rank gaps carry no signal.

At its first LlamaCon on April 29, Meta opened a limited free preview of the Llama API with Python and TypeScript SDKs, OpenAI-SDK compatibility, and hosted fine-tuning plus an evaluation suite starting with Llama 3.3 8B — with the explicit promise that models built there remain portable to self-hosting. The same day it shipped the standalone Meta AI app, pitched on the personalization it can draw from a user's Facebook and Instagram context, and said Meta AI now spans WhatsApp, Instagram, Messenger and Facebook at almost a billion monthly actives.

The developer-facing releases were the security tools, all open source: Llama Guard 4, Prompt Guard 2 and LlamaFirewall. LlamaFirewall is an agent guardrail framework with three parts — PromptGuard 2 as a universal jailbreak detector, Agent Alignment Checks as a chain-of-thought auditor that inspects agent reasoning for prompt injection and goal misalignment, and CodeShield, an online static analyzer that blocks coding agents from emitting insecure code. Meta says the framework runs in production internally.