Reddit AI - 2026-04-22

1. What People Are Talking About

1.1 GPT Image 2 Dominates the Visual AI Conversation (🡕)

OpenAI's GPT Image 2 launch generated the single largest cluster of engagement across all topics today, with at least twelve posts spanning singularity, ArtificialInteligence, and generativeAI exceeding a combined score of 4,500.

u/Glittering-Neck-2505 posted The new ChatGPT images model is the new standard in photorealistic image generation (score 1,308, 306 comments), triggering broad reaction. u/TheRanker13 followed with Gpt image 2 has the biggest jump in quality ever recorded (score 1,070, 120 comments), sharing benchmark comparisons and photorealistic samples.

Key capabilities that users highlighted:

• Self-review iteration loop: u/Plane_Garbage reported in GPT-Image-2 now reviews its own output and iterates until it is satisfied (score 505) that a single image took approximately 11 minutes while the model reviewed and revised its own output multiple times. u/Worried-Squirrel2023 noted: "the self-review loop is interesting but 11 minutes per image is rough for any real workflow."
• Text rendering: u/Thatunkownuser2465 observed: "I'am shocked how good is this model in text rendering and photorealism." Multiple users confirmed the model handles complex infographics with accurate text.
• Multiple quality tiers: u/FateOfMuffins identified instant and medium modes, comparing it to "the o1 reasoning model of AI images."
• Character consistency: u/kaldeqca showed in the characters can stay extremely consistent (score 124) that characters maintain appearance across multiple generated images.

Skeptics were present. u/Calm_Opportunist warned: "it'll go back to normal in a week or so. Always does. They pull you in with the improvement then nerf it down once they grab the headlines." u/Sharp-Dog545 noted: "The better the models become, the less people are impressed by it."

Compared to yesterday: GPT Image 2 was teased on April 21 but launched fully today, making this a new top-tier discussion. Yesterday's image generation conversation was minimal.

1.2 Qwen 3.6 27B Drops as a Dense Powerhouse (🡕)

The release of Qwen 3.6 27B was the top single post of the day. u/NoConcert8847 announced Qwen 3.6 27B is out (score 1,325, 469 comments), linking to the Hugging Face model card. The official announcement from u/ResearchCrafty1804 in Qwen3.6-27B released! (score 546, 141 comments) detailed the benchmarks:

Benchmark	Qwen3.6-27B	Qwen3.5-397B-A17B
SWE-bench Verified	77.2	76.2
SWE-bench Pro	53.5	50.9
Terminal-Bench 2.0	59.3	52.5
SkillsBench	48.2	30.0

A 27B dense model outperforming a 397B MoE model on coding benchmarks stunned the community. u/adam_suncrest celebrated: "densocrats it's time to eat." u/Guilty_Rooster_6708 wrote: "Wake up my 16gb VRAM GPU. Get ready buddy."

u/Creative-Regular6799 followed up with Qwen3.6-35B becomes competitive with cloud models when paired with the right agent (score 481, 125 comments), showing that the little-coder scaffold pushed Qwen3.6 35B to 78.7% on Polyglot, making it competitive with frontier cloud models. u/DependentBat5432 reacted: "going from 19% to 45 to 78 just by changing the scaffold is kind of terrifying."

Same-day GGUF quants from Unsloth were posted by u/jacek2023 in unsloth Qwen3.6-27B-GGUF (score 350).

Compared to yesterday: Yesterday focused on Qwen 3.6 35B MoE; today's 27B dense release shifts the conversation to the dense-vs-MoE architecture debate with new evidence.

1.3 Claude Code Removed from Pro Plan Sparks Backlash (🡕)

Three high-engagement posts documented Anthropic testing the removal of Claude Code from the $20/month Pro plan. u/bigboyparpa posted Claude Code removed from Claude Pro plan (score 1,302, 383 comments), including a screenshot of the pricing page showing Claude Code marked with an X for both Free and Pro tiers.

u/mhamza_hashim posted the same finding in Claude Code no longer listed as a feature for Claude Pro (score 638, 165 comments). u/Just_Stretch5492 added in Anthropic has appeared to begin testing removing Claude Code (score 455) that OpenAI employees were already mocking the move publicly.

Anthropic responded via a tweet cited by u/bigredsun: "For clarity, we're running a small test on ~2% of new prosumer signups. Existing Pro and Max subscribers aren't affected." The community was broadly skeptical. u/Shot_Illustrator4264 said: "it's not a test, they clearly removed it from the comparison page. They are just backtracking given the immense backlash." u/rpkarma summed up the sentiment: "Of course, the rug pull begins lmao."

The LocalLLaMA moderator u/rm-rf-rm approved the post despite being about a closed-source product, noting it "further underscores the importance of local inference."

Compared to yesterday: Not present in yesterday's report. This is a new development that amplifies existing migration-to-local trends.

1.4 Open Model Rivalry: Qwen vs Gemma Intensifies (🡒)

The Qwen-vs-Gemma debate continued from yesterday with sharper data. u/FullChampionship7564 captured the mood with a Toy Story meme post Every time a new model comes out, the old one is obsolete of course (score 970, 177 comments) showing Qwen 3.6 replacing Gemma 4.

The comments revealed a more nuanced view than the meme suggests. u/MexInAbu argued: "Gemma 4 is superior for creative writing and there's no contest." u/markole added: "Coding? Sure. Translating? Nah, qwen sucks for translating." u/ComplexType568 offered the consensus: "these two models cover each other's weaknesses. Coding and development for qwen, creativity and languages for Gemma."

u/ThisGonBHard provided detailed translation testing in Gemma 4 beats both Chat GPT and Gemini Chat (score 246, 48 comments), finding Gemma 4 31B at Q4 quantization outperformed ChatGPT 5.3 and Gemini Chat on Chinese-to-English novel translation. u/Uncle___Marty reflected: "Google did something with the language abilities of Gemma 4 which really puts it in a class of its own."

u/seamonn revealed in Gemma 4 Vision (score 294, 55 comments) that the default vision token budget of 280 is far too low, and setting it to 2240 makes Gemma 4 "pretty much SOTA for Vision," especially for OCR.

u/Lowkey_LokiSN posted detailed evaluations in Personal Eval follow-up (score 143) showing both Qwen 3.5 27B and Gemma 4 31B achieving 100% test fix rate (37/37), compared to Qwen 3.6 35B at 86.5%.

Compared to yesterday: Yesterday covered architecture tradeoffs between MoE and dense models. Today the conversation sharpens with new real-world test data and practical recommendations.

1.5 Kimi K2.6 Cements Position as Cost-Effective Frontier (🡒)

Kimi K2.6 continued its strong showing from yesterday's launch. u/Snoo26837 posted Kimi K2.6 lands at #4 on the Artificial Analysis Intelligence Index (score 258, 75 comments), showing K2.6 scoring 54 -- beating Claude Opus 4.6 Max.

u/mxforest noted: "Beating Opus 4.6 Max is crazy." u/sb5550 contextualized: "Kimi is just a 1T model, and Opus is 5T, let that sink in."

u/meaningego provided a practitioner perspective in Opus 4.7 Max subscriber. Switching to Kimi 2.6 (score 245, 78 comments), describing the switch for their entire team due to cost and Opus 4.7 laziness. u/Ok-Contest-5856 extrapolated: "Private Equity who dumped billions into anthropic and openai are in for a really bad time."

u/ENT_Alam benchmarked K2.6 on MineBench (score 267), showing massive improvement over K2.5, with the total benchmark cost at just $2.35.

On the infrastructure side, u/Cosmicdev_058 shared Moonshot open-sourced FlashKDA (score 130), CUTLASS kernels for Kimi Delta Attention achieving up to 2.22x speedup over Triton baseline on H20.

Compared to yesterday: Yesterday was launch-day excitement. Today shifts to benchmark confirmation and real-world migration stories.

1.6 Robotics, Hardware, and AI in the Physical World (🡕)

A cluster of posts showed accelerating AI embodiment. u/Distinct-Question-16 posted two high-engagement items: A Chinese startup sells a $3 companion AI device that generates interactive holograms of deceased loved ones (score 717, 228 comments), and Another CyberNani face spotted (score 692, 99 comments) showing humanoid robot faces approaching the uncanny valley. u/TwoFluid4446 wrote: "This is definitely uncanny valley territory square on the nose, but... still impressive."

u/Distinct-Question-16 also posted AheadForm Origin F1 returns with new look (score 401, 97 comments), a humanoid robot with improved appearance.

u/WhyLifeIs4 shared Google introduces TPU 8t and TPU 8i (score 355, 47 comments). The TPU 8i specs show 1,152 pod size, 11.6 FP8 EFlops per pod, and 331.8 TB HBM capacity -- massive jumps from Ironwood (2025). u/Worried-Squirrel2023 observed: "nvidia finally has a real second source problem. every hyperscaler now either has their own silicon or is seriously working on it."

u/mientosiempre posted China training for urban warfare with armed robot dogs and attack drones (score 342, 62 comments), adding a military dimension to the embodied AI discussion.

Compared to yesterday: Yesterday had Apple hardware AI strategy. Today broadens to physical robotics and custom silicon.

1.7 Agentic Tools Face a Reckoning (🡒)

A critical thread emerged around the utility of current agentic tools. u/pacmanpill posted Unpopular opinion: OpenClaw and all its clones are almost useless tools for those who know what they're doing (score 572, 229 comments). u/swiebertjee agreed with a detailed example: "I tried Openclaw last weekend and was surprised by how utterly useless it is."

u/superloser48 shared OpenRouter ranking data (score 202, 118 comments) showing 6 of the top 10 apps by token usage are non-coding tools. OpenClaw leads at 345B tokens, followed by Hermes Agent at 268B and Kilo Code at 179B.

u/FullstackSensei reported that Roo Code hit 3 million installs then announced shutdown (score 80, 70 comments) to pivot to cloud-based "Roomote." u/mikael110 responded: "this is how pretty much all corporate backed OSS AI projects ends up these days."

Meanwhile, u/My_Unbiased_Opinion announced Open WebUI Desktop Released! (score 273, 102 comments), bundling llama.cpp for local inference. Reception was mixed, with u/Danmoreng noting: "Calling it desktop app but it's a packaged web frontend."

Compared to yesterday: Yesterday covered agentic tool safety failures. Today the critique deepens with usage data and project abandonments.

1.8 AI and Society: Surveillance, Deepfakes, and Authenticity (🡒)

Several posts examined AI's societal footprint. u/EmbarrassedStudent10 reported Meta is reportedly forcing U.S. employees to train their own AI replacements via "Keylogger" surveillance (score 321, 53 comments), detailing Meta's "Model Capability Initiative" that captures keystrokes, mouse movements, and screen snapshots ahead of planned 8,000 layoffs. u/heavy-minium was skeptical: "Likely nothing is going to come out of that. They lag so far behind in terms of AI models now."

u/EchoOfOppenheimer shared Hundreds of Fake Pro-Trump Avatars Emerge on Social Media (score 213, 49 comments), linking to NYT reporting.

u/JackFisherBooks posted Deezer says 44% of new music uploads are AI-generated, most streams are fraudulent (score 219, 43 comments). u/KoaKumaGirls pushed back with Deezer's own data: "despite a large number of uploads being AI music, an incredibly small amount of subsequent listens are AI music."

u/iamMARX argued in people won't "return to authenticity" as AI gets better (score 113, 79 comments), comparing AI content to ultra-processed food: "Authenticity won't disappear. It'll just become something people have to consciously choose."

Compared to yesterday: Yesterday discussed AI productivity paradox. Today adds concrete surveillance evidence from Meta and deeper cultural analysis.

2. What Frustrates People

Claude Code Pricing Shift

Severity: High -- Combined score exceeding 2,300 across three posts.

The potential removal of Claude Code from the Pro tier ($20/month) forces users to the $100 or $200 Max tiers for coding agent access. u/hacketyapps wrote: "Are fucking kidding me? I'm on Pro and still using Claude Code, probably when my sub is due I won't have access anymore... I hope they lose a shit ton of customers." u/Super_Push7794 called it "classic enshittification."

Coping strategies: Users immediately pointed to Kimi K2.6 via OpenCode Go ($5 first month, then $10) and running Qwen 3.6 locally. u/bigboyparpa outlined the alternative: "for $20 a month of tokens of Kimi K2.6 you're basically getting the equivalent amount of tokens of the $100 plan."

Opus 4.7 Laziness and Quality Regression

Severity: Medium-High -- SimpleBench data confirms subjective complaints.

u/EducationalCicada posted SimpleBench results (score 241) showing Opus 4.7 scoring 61.7%, below both Opus 4.6 (67.6%) and Opus 4.5 (62.0%). u/Worried-Squirrel2023 described the core issue: "it's not even quality, it's that it stops mid-task or wraps things up before they're actually done."

Agentic Tool Immaturity

Severity: Medium -- Widespread dissatisfaction but workarounds exist.

u/swiebertjee detailed OpenClaw failures: "it only triggers on incoming messages... I asked it to make notes only, it confirmed, yet it started replying to my mother." u/cosimoiaia extended the critique to n8n: "useless. You can make any workflow you want with a few prompts if you know a little what you're doing."

Gemma 4 Vision Misconfiguration

Severity: Medium -- Fixable but poorly documented.

u/seamonn found Gemma 4's default vision budget (280 tokens, approximately 645K pixels) makes it "essentially blind." The fix requires manually setting --image-max-tokens 2240 in llama.cpp, plus adjusting batch sizes. Ollama users are "SOL until and if they care to fix this."

Quantization Confusion

Severity: Low-Medium -- Affects model selection decisions.

u/LawyerCompetitive478 found in Did Google hide the best version of Gemma 4 e4b in Android? (score 274) that Google AI Edge Gallery's LiteRT model outperforms community GGUF quants. u/Fit-Produce420 explained bluntly: "Gemma 4 was made by highly paid engineers at google who designed the model, the edge app, and understand how to properly serve it. Your community fine tune was made by random strangers."

3. What People Wish Existed

Dense Qwen3.6-27B That Fits Consumer GPUs Effortlessly

The excitement around Qwen 3.6 27B showed strong demand for dense models at consumer hardware scale. u/Guilty_Rooster_6708 said: "Wake up my 16gb VRAM GPU." Users want models that require no compromises on quantization and still outperform much larger MoE architectures.

Mid-Range Gemma (60-70B)

In Which Gemma model do you want next? (score 187, 103 comments), the community loudly asked for larger Gemma models. u/DelKarasique wrote: "Midrange one. Like 70b. I think that's a sweet and empty spot right now." u/ResidentPositive4122 pushed further: "The small models are already good. Let's see what 124B was all about."

Affordable Coding Agent Access

The Claude Code removal crystallized a desire for reliable coding agents at the $20/month price point. Users want Kimi K2.6-level performance accessible without cloud vendor lock-in. u/meaningego noted frustration that Kimi does not "work out of the box with Forge" and submitted a PR to fix it.

Better Agent Scaffolds for Local Models

u/Creative-Regular6799 demonstrated that scaffold choice matters more than model choice, going from 19% to 78.7% on the same benchmark by changing the agent harness. Users want standardized, well-tested scaffolds specifically designed for local models rather than repurposed cloud model harnesses.

Reliable Vision Configuration Defaults

u/seamonn showed Gemma 4 vision is dramatically undertuned by default. Users want model providers to ship sensible defaults and want serving frameworks like Ollama and LM Studio to expose vision budget knobs.

4. Tools and Methods in Use

Tool	Category	Sentiment	Strengths	Limitations
Qwen 3.6 (27B/35B)	Local LLM	Very positive	Dense model beating larger MoE; strong coding benchmarks; Apache 2.0	Brand new, limited long-term testing
Gemma 4 (31B/26B/E4B)	Local LLM	Positive	Creative writing, translation, vision (when tuned); free	Vision defaults too low; slow dense inference
Kimi K2.6	Cloud/Local LLM	Very positive	#4 on Artificial Analysis Index; $2.35 for full benchmark run	256K context limit; inconsistent output quality
Claude Opus 4.7	Cloud LLM	Mixed-negative	Strong at agentic coding when committed	Laziness; SimpleBench regression; pricing concerns
llama.cpp	Inference engine	Very positive	Auto-fit feature; broad hardware support; fast iteration	Requires manual tuning for vision models
GPT Image 2	Image generation	Very positive	Photorealism; text rendering; self-review loop; character consistency	11-minute generation for complex images; cost unknown
Unsloth	Quantization	Positive	Same-day GGUF releases; dynamic quantization	Community quants may lag behind vendor quants
little-coder	Agent scaffold	Positive	Pushed Qwen3.6 35B to 78.7% on Polyglot	New, limited to specific benchmarks
pi coding agent	Agent scaffold	Positive	Extensible; local-first; works well with Qwen 3.6 and Gemma 4	Smaller user base
Open WebUI	LLM interface	Mixed	Desktop app with bundled llama.cpp	Electron-based; MCP integration criticized
OpenClaw	Automation agent	Negative	Accessible to beginners	"Utterly useless" per experienced users; unsafe behaviors
Roo Code	Coding agent	Negative (shutting down)	3 million installs; user control	Pivoting to cloud; abandoning OSS project

The local inference stack (llama.cpp + Unsloth quants + Qwen/Gemma models) continues to consolidate as the default pathway. The scaffold layer (little-coder, pi, OpenCode) is emerging as the critical differentiator for local model performance.

5. What People Are Building

Project	Who	What It Does	Problem It Solves	Stack	Stage	Links
Koharu	u/mayocream39	Local manga/image translator with built-in LLM	Manga translation pipeline lacks performant local tooling	Rust, llama.cpp, Gemma 4, Qwen3.5, object detection, inpainting	Active, 1 year polish	GitHub
little-coder	u/Creative-Regular6799	Agent scaffold that makes local models competitive	Cloud-designed scaffolds underperform with local models	Python, Qwen3.6	Active, benchmarking	GitHub
MineBench	u/ENT_Alam	3D Minecraft structure generation benchmark	No spatial reasoning benchmark for LLMs	JSON coordinate mapping	Active, public benchmark	minebench.ai
1386.ai / Plasma	u/ExcellentTip9926	235M param LLM trained from scratch	Learning full LLM pipeline end-to-end	PyTorch, SentencePiece, FineWeb-Edu	v1.0, training v1.1 (500M)	GitHub
simple_dlm	u/Encrux615	Diffusion language model from scratch	Understanding diffusion LM architecture without AI code assistance	PyTorch, 7.5M params	Educational prototype	GitHub
FlashKDA	Moonshot AI	CUTLASS kernels for Kimi Delta Attention	Triton baseline too slow for linear attention	CUTLASS, C++, SM90+	Released, forward-pass only	GitHub
Forge PR for Kimi	u/meaningego	Kimi K2.6 support for Forge coding tool	Kimi not working out-of-box with Forge	--	PR submitted	PR #3098

6. New and Notable

Qwen 3.6 27B: Dense Model Beats Its Own 397B MoE Predecessor

A 27B dense model surpassing a 397B (17B active) MoE on every major coding benchmark is a milestone for efficient model architecture. The Apache 2.0 license and immediate GGUF availability means it will be running on consumer hardware within hours of release. This challenges the assumption that MoE architectures are the path to efficient frontier performance.

Mozilla Uses Mythos to Find 271 Firefox Bugs

u/Tinac4 shared Mozilla Used Anthropic's Mythos to Find and Fix 271 Bugs in Firefox (score 649), citing Wired reporting. Firefox CTO Bobby Holley stated: "now we have automated techniques that can cover, as far as we can tell, the full space of vulnerability-inducing bugs." This is the largest publicly reported AI-driven vulnerability discovery in a production browser. However, u/helg0ret noted the Firefox 150 changelog only lists 3 vulnerabilities found with Claude, questioning the 271 figure.

OpenAI Codex Model Leak

u/DavidAGMM captured a massive OpenAI Codex model leak on video (score 129) before it was patched, revealing unreleased internal models including GPT-5.5, "Arcanine," "glacier-alpha," and "Heisenberg" (described as "Latest frontier life science research model").

Google Drops Deep Research Max and TPU 8

Two major Google announcements on the same day: Google introduces TPU 8t and TPU 8i with 11.6 FP8 EFlops per pod, and Deep Research Max (score 224) surpassing GPT 5.4 on research benchmarks. u/FateOfMuffins noted Google "dropped this blog post now purely because if they don't then they just couldn't drop it at all given this week."

MiMo-V2.5 Released

u/WhyLifeIs4 shared MiMo-V2.5 Has released (score 97) from Xiaomi, now available on OpenRouter. This was flagged as upcoming in the prior day's open model timeline.

Roo Code Shuts Down After 3 Million Installs

Roo Code's pivot from open-source VS Code extension to cloud-based "Roomote" marks another OSS AI project abandoning its community. With Kilo Code reportedly "taking a similar route," the community-maintained coding agent space is fragmenting.

7. Where the Opportunities Are

[+++] Local Coding Agent Scaffolds That Close the Gap to Cloud

Evidence: u/Creative-Regular6799 showed scaffold choice alone moved benchmark performance from 19% to 78.7%. Qwen 3.6 27B matches or exceeds cloud models on coding benchmarks. The Claude Code pricing change is actively pushing users toward alternatives. Multiple comments across threads confirm the scaffold layer is the primary bottleneck, not model quality. The gap between local and cloud is now primarily a tooling problem.

[+++] AI-Powered Security Auditing Tools

Evidence: Mozilla found 271 bugs in Firefox using Mythos, with the CTO claiming full vulnerability coverage. Only 50 companies have Mythos access today, but u/shadow-knight-cz noted open-weight models can find the same vulnerabilities. Democratizing this capability with open models is a clear near-term opportunity -- the demand signal from the 649-score post is strong.

[++] Gemma 4 Vision Configuration and Tooling

Evidence: u/seamonn showed Gemma 4 vision goes from "essentially blind" to "SOTA" by changing a single parameter. Ollama has an open issue for this. LM Studio does not expose the knob. Any tool that automatically optimizes vision model configuration will address a documented, high-value gap.

[++] Cost-Effective Model Serving for Teams Migrating from Anthropic

Evidence: Multiple posts document team-level migration from Opus Max ($200/seat) to Kimi K2.6. u/meaningego switched their entire team. MineBench full run cost $2.35 on Kimi. Demand exists for managed deployment that bridges the gap between raw API access and a polished team experience.

[+] Content Authentication and Provenance

Evidence: Deezer reports 44% AI-generated uploads with bot-farm fraud. Hundreds of fake political avatars reported by NYT. u/iamMARX argues authenticity will become a conscious opt-in, not a default. Tools that verify human origin or flag synthetic content have growing demand across music, social media, and journalism.

[+] Specialized Translation Pipelines Using Local Models

Evidence: u/ThisGonBHard showed Gemma 4 31B outperforms all tested cloud models for Chinese-to-English translation. u/mayocream39 built Koharu for manga translation. Cloud models are regressing (Qwen censorship, GPT A/B degradation) while local models improve, creating a window for specialized translation tools.

8. Takeaways

1. Dense models are back. Qwen 3.6 27B beating its own 397B MoE predecessor on all coding benchmarks challenges the MoE-dominant narrative. The community celebrated with 1,300+ upvotes and 469 comments, and same-day GGUF quants mean immediate local deployment. (Qwen 3.6 27B is out)

2. The scaffold matters more than the model. Going from 19% to 78.7% on the same benchmark by changing only the agent harness is a wake-up call. Local models are closer to frontier performance than benchmarks suggest -- the tooling layer is the real bottleneck. (Qwen3.6-35B becomes competitive with cloud models)

3. Anthropic is testing the limits of its user base. The Claude Code removal from the Pro plan, combined with Opus 4.7 SimpleBench regression and user-reported laziness, is driving measurable migration. The backlash was instant and loud across three subreddits. (Claude Code removed from Pro plan)

4. GPT Image 2 sets a new visual AI benchmark. The self-review iteration loop, accurate text rendering, and character consistency represent a generational improvement. The 11-minute generation time for complex images signals a trade-off between quality and throughput that will shape production workflows. (GPT Image 2 has the biggest jump in quality ever recorded)

5. Kimi K2.6 is repricing the frontier. Ranking #4 on Artificial Analysis Intelligence Index as a 1T model against 5T competitors, with a full MineBench run costing $2.35, K2.6 is concretely demonstrating that frontier-adjacent performance no longer requires frontier-level spending. (Kimi K2.6 lands at #4)

6. AI security tooling has crossed a threshold. Mozilla finding 271 bugs in Firefox using Mythos, with their CTO stating automated techniques can now cover "the full space of vulnerability-inducing bugs," is a concrete signal that AI-assisted security is moving from experimental to production. (Mozilla Used Anthropic's Mythos)

7. Open model complementarity is emerging as the practical strategy. Rather than one model to rule them all, practitioners are converging on Qwen for coding and Gemma for creative writing, translation, and vision -- with each covering the other's weaknesses. This complementary approach may be more durable than chasing a single frontier model. (Every time a new model comes out)

8. Physical AI is accelerating on multiple fronts simultaneously. Google TPU 8 with 11.6 EFlops per pod, CyberNani faces approaching uncanny valley, armed robot dogs in urban warfare training, and $3 AI hologram companions all appeared on the same day. The hardware substrate for embodied AI is scaling faster than the software discussion suggests. (Google introduces TPU 8t and TPU 8i)