Paying multi‐million‐dollar annual run‐rates to call giant models? Over the last 14 days the field has accelerated toward systematically distilling big models into compact specialists you can run cheaply on commodity hardware or on‐device, and this summary shows what’s changed and what to do. Recent preprints (2025‐10 to 2025‐12) and reproductions show 1–7B‐parameter students matching teachers on narrow domains while using 4–10× less memory and often 2–5× faster with under 5–10% loss; FinOps reports (through 2025‐11) flag multi‐million‐dollar inference costs; OEM benchmarks show sub‐3B models can hit interactive latency on devices with tens–low‐hundreds TOPS NPUs. Why it matters: lower cost, better latency, and privacy transform trading, biotech, and dev tools. Immediate moves: define task constraints (latency <50–100 ms, memory <1–2 GB), build distillation pipelines, centralize registries, and enforce monitoring/MBOMs.

Are you quietly burning multi‐million dollars a year on LLM inference while latency kills real‐time use cases? In the past 14 days (FinOps reports from 2025‐11–2025‐12), distillation, quantization, and edge NPUs have converged to make “right‐sized AI” the new priority — this summary tells you what that means and what to do. Big models (70B+) stay for research and synthetic data; teams are compressing them (7B→3B, 13B→1–2B) and keeping 90–95% task performance while slashing cost and latency. Quantization (int8/int4, GGUF) and device NPUs mean 1–3B‐parameter models can hit sub‐100 ms on phones and laptops. Impact: lower inference cost, on‐device privacy for trading and medical apps, and a shift to fleets of specialist models. Immediate moves: set latency/energy constraints, treat small models like APIs, harden evaluation and SBOMs, and close the distill→deploy→monitor loop.

Code reviewers are burning 12–15 hours a week on low‐signal, AI‐generated PRs—so what should you do? Over the last two weeks (with practitioner threads on Reddit: 2025‐11‐21, 2025‐11‐22, 2025‐12‐05) senior engineers in finance, infra, and public‐sector data say the problem isn’t models but broken workflows: tool sprawl, “vibe‐coded” over‐abstracted changes, slower iteration, and higher maintenance risk. The practical fix that’s emerging: pick one primary assistant and master it (a month trial delivered edits that fell from 2–3 minutes to under 10 seconds), treat others as specialists, and map your repo into green/yellow/red AI zones enforced by CI and access controls. Measure outcomes (lead time, change‐failure rate, review time), lock down AI use via operating policies, and ban unsupervised AI in high‐risk flows—these are the immediate steps to turn hype into reliable productivity.

Worried AI will quietly eat your engineering org? In the past two weeks (high‐signal Reddit threads around 2025‐12‐06), senior engineers using Claude Opus 4.5, GPT‐5.1 and Gemini 3 Pro say state‐of‐the‐art LLMs already handle complex coding, refactoring, test generation and incident writeups—acting like a tireless junior—forcing a shift from “if” to “how fast.” That matters because mechanical coding is being commoditized while value moves to domain modeling, system architecture, production risk, and team leadership; firms are redesigning senior roles as AI stewards, investing in platform engineering, and rethinking interviews to assess AI orchestration. Immediate actions: treat LLMs as core infrastructure, invest in LLM engineering, domain expertise, distributed systems and AI security, and redraw accountability so senior staff add leverage, not just lines of code.

Is your team drowning in huge, AI‐generated PRs? In the past 14 days engineers have reported a surge of “vibe coding” — heavy LLM‐authored code dumped into massive pull requests (Reddit, r/ExperiencedDevs, 2025‐12‐05; 2025‐11‐21) that add unnecessary abstractions and misaligned APIs, forcing seniors to spend 12–15 hours/week on reviews (Reddit, 2025‐11‐20). That mismatch — fast generation, legacy review norms — raises operational and market risk for fintech, quant, and production systems. Teams are responding with clear fixes: green/yellow/red zoning for AI use, hard limits on PR diff size, mandatory design docs and tests, and treating AI like a junior that must be specified and validated. For leaders: codify machine‐readable architecture guides, add AI‐aware CI checks, and log AI involvement — those steps turn a short‐term bottleneck into durable advantage.

In the past two weeks attackers rapidly exploited zero‐day flaws in foundational network infrastructure: Cisco Secure Firewall ASA/FTD vulnerabilities (CVE‐2025‐20333 and CVE‐2025‐20362) were disclosed on 2025‐11‐05 and tied to a campaign active since May 2025 attributed to UAT4356/Storm‐1849, prompting CISA Emergency Directive ED 25‐03; a WSUS flaw (CVE‐2025‐59287) has been actively exploited since 2025‐10‐24—one day after Microsoft patched it—and impacted at least 50 organizations across healthcare, manufacturing, education and tech. Separately, the Congressional Budget Office disclosed a breach on 2025‐11‐06, potentially exposing communications with Senate offices; the CBO contained the incident and enhanced monitoring. These events amplify systemic risk because compromised patching or edge devices enable wide lateral control; immediate actions cited include auditing patch distribution and firmware, validating deployments, strengthening detection/isolation, and tightening regulatory enforceability.