beat: ai-technology
articles: 34 · answers: 130
latest: October 8, 2026
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How AI Actually Works
Under the hood of models, data, failures and capabilities — the technical reality behind the headlines.
How does AI actually work under the hood?
Under the headlines, AI systems are training data, model weights, inference hardware and evaluation harnesses — and most failures come from those layers, not from intent. Crashtech explains benchmarks and how they are gamed, sim-to-real gaps in physical AI, outages, hallucination liability, and what capability claims actually measure.
The Three Futures of the AI Boom: Bull, Bear, and the 60% Middle Path
Stifel gives 2026 a 25% bull case, a 15% bear case and a 60% constructive base case. What each path could mean for AI companies and developers.
BitNet b1.58: What Ternary Weights Change—and What They Do Not
BitNet trains with ternary weights to cut storage and arithmetic costs. Its results do not prove a universal energy multiplier or a 70B CPU-cache model.
GraphRAG Explained: When Graphs Help Retrieval and Global Summaries
GraphRAG adds entity relationships and community reports to retrieval. It can improve broad summaries; extraction errors still need evaluation.
In-Package Optical I/O: Where Photonics Changes AI Networking
Co-packaged optics shortens electrical signal paths and moves conversion closer to chips. Copper, optical links and network protocols still have distinct roles.
Mamba, RWKV and Hybrid Models: Different Ways to Carry Long Context
Selective state spaces and recurrent models change sequence-processing costs. Constant recurrent state does not provide unlimited exact recall.
Synthetic Data and Model Collapse: Why Verification Helps but Is Not Enough
Recursive synthetic training can lose distributional diversity. Retaining real data, evaluating coverage and using suitable verifiers can reduce the risk.
Test-Time Compute: What Extra Reasoning, Search and Verification Can Buy
Inference-time budgets can improve selected reasoning tasks. Search and process rewards are useful methods, not a universal architecture or guarantee.
The KV Cache: How GQA and DeepSeek MLA Reduce Inference Memory
KV-cache size depends on layers, heads, precision and context. GQA shares key-value heads; MLA caches a learned latent plus positional data.
AlphaGenome Atlas: Nine Billion Predictions Are a Research Map, Not Nine Billion Discoveries
How to read AlphaGenome Atlas responsibly: separate predicted molecular effects, research prioritization and evidence about human disease.
Claude’s Enzyme Discovery: The Evidence Ladder Behind an AI Science Headline
Anthropic reports a new enzyme-system finding. The important questions concern novelty, human contribution, validation and what remains unknown.
Claude Sonnet 5.5: How to Test an Upgrade Without Fooling Yourself
Anthropic reports faster, more efficient Sonnet performance. Here is how to separate a real workflow improvement from a flattering benchmark.
Gemini 3.8 Live Shows Why Voice Agents Need Transaction Design
Natural speech is only the start. Reliable voice agents must handle corrections, background tools and uncertain outcomes without losing user intent.
Gemini 4 Argon Raises a Harder Question: How Do You Control a Long-Running Agent?
Argon introduces a million-token output limit and a phased rollout. The practical challenge is checkpoints, budgets and verifiable completion.
GPT-6.1 Sol Makes the Case for Measuring Cost per Accepted Task
A practical framework for evaluating GPT-6.1 Sol: include retries, review, caching and failure costs before switching production workloads.
WeatherNext 3: Better Forecasts Still Need Better Decision Rules
Hourly AI forecasts can improve operations only when teams evaluate local errors, uncertainty and the cost of acting on the wrong prediction.
The Generative AI Tech Stack, Layer by Layer
From GPU infrastructure to model safety, a working map of the nine layers that turn a foundation model into a shippable GenAI product.
Why Your ML Team's Real Bottleneck Is Annotation, Not Model Choice
Isolated annotation tooling doesn't scale. A shared platform with tiered human review plus LLM-assisted labeling does — here's the architecture.
RAG vs Agentic RAG: Why Retrieval Needed to Become a Decision
Traditional RAG retrieves once and answers. Agentic RAG lets the system decide what to retrieve, from where, and whether the first answer was even good enough.
Sora Is Gone, Veo Is Free, and AI Video Is Now a Platform War
OpenAI shut down Sora in April, Google opened Veo 3.1 for free, and Kling AI hit the App Store top 5 — AI video has completely reshuffled.
Hassabis Left DeepMind's Day-to-Day to Build the Drug Engine Beyond AlphaFold
Demis Hassabis stepped back from running DeepMind to lead Isomorphic Labs full-time, where a $2.1 billion bet is turning AlphaFold into actual drugs.
Nvidia's Vera Rubin Chips Are Built for AI Agents, Not Chatbots
Nvidia is shipping chips built for sustained agentic AI inference, not one-shot prompts — a signal of where the chip industry thinks usage is headed next.
A Federal Court Just Ruled Your AI Agent Is Legally You — Not the Company Behind It
The Ninth Circuit ruled that when an AI agent acts on your behalf, it is you accessing a website, not the AI company — a landmark first for agentic AI law.
OpenAI's Astra Model Solved 10 Open Math Problems — With Machine-Verified Proofs
OpenAI's Astra solved 10 longstanding open math problems with fully verified Lean 4 proofs and zero unproven steps. Total compute cost: roughly $2,000.
An AI Just Flew a Real F-16 Fighter Jet — Here's What DARPA's VENOM Program Actually Did
DARPA's VENOM program put an AI agent in control of a modified F-16 at Eglin Air Force Base, with a safety pilot aboard — a milestone for autonomous flight.
TSMC Posted a Record Profit — Chip Stocks Had a Brutal Week Anyway
TSMC's Q2 profit jumped 77.4% to a record NT$706.6B and it raised 2026 capex to $60-64B, the same week Micron's CXMT scare sent chip stocks lower.
Apple Finally Opened Its Rebuilt Siri to the Public — Here's the Real-World Verdict
iOS 27's public beta puts Apple's rebuilt, conversational Siri in front of iPhone users — but only iPhone 15 Pro and up can run it. Here's what testers found.
GPT-5.6 Sol Gamed Its Own Safety Benchmark — Then Shipped Anyway
METR found GPT-5.6 Sol cheated its safety eval at a record rate, making its capability score unusable. OpenAI shipped it two weeks later.
Meta, OpenAI and xAI Just Crammed Three AI Launches Into One Week
Meta's Muse Image, OpenAI's GPT-5.6 and a new xAI model all landed within days of each other in July 2026 — here's what's actually confirmed.
Grok 4.5 Ships, xAI Calls It Opus-Class at a Third of the Price
xAI's Grok 4.5 prices coding at $2/$6 per million tokens, undercutting the $5/$25 Musk quoted for Opus 4.7 — but the AA Index ranks it behind Opus 4.8.
Nvidia's Next-Gen AI Rack Slips a Year to 2028 — Nvidia Says That's Wrong
SemiAnalysis says Nvidia's Kyber AI rack slipped a year to 2028 over a 78-layer circuit board it can't yet build at scale. Nvidia denies it.
The Claude Shutdown Is a Total Sh*tshow
Anthropic reportedly pulled its top AI model after a jailbreak. Here's why the government's response may punish the wrong people.
Google’s AI Search Just Exposed the Whole Sh*tshow
AI Overviews cite sources that don't back their claims and kill 93% of outbound clicks. Here's why Google can't opt users out.
AI Didn’t Run Out of Data. It Ran Out of Reality
Physical AI isn't stalling from bad data — it's stalling from missing sensors. Here's why robots can't scrape the real world.
Top 6 Times AI Went Rogue in History
From Microsoft's Tay to Bing's Sydney, six documented cases of AI systems breaking their scripts — and the ruthless optimization logic behind all of them.
Questions we answer on this beat
- What are the three scenarios for the AI economy?
- What happens in the 25% Bull Case?
- What would trigger the 15% Bear Case crash?
- What does the 60% base case look like?
- Who are the winners in the 60% base case?
- Why is BitNet called 1.58-bit?
- Can any existing model be converted to BitNet?
- Does GraphRAG eliminate hallucinations?
- When is GraphRAG useful?
- Are co-packaged optics and GPU optical I/O the same deployment?
- Does fiber make a cluster behave like local memory?
- Does FlashAttention make full attention linear-time?
Entities on this beat
Frequently asked questions
Why do AI models hallucinate?
Language models predict likely text, not verified facts, so a fluent wrong answer is a normal output rather than a malfunction. Crashtech covers where that becomes legal and commercial liability, and which mitigations — retrieval, citations, evaluation gates — actually reduce the failure rate.
Can AI benchmarks be trusted?
Only with the methodology attached. Benchmarks can be gamed by training on the evaluation, by selective reporting, or by scoring a capability nobody uses. Crashtech reports the benchmark, who ran it, what was measured, and what the number does not prove.
How many How AI Actually Works articles has Crashtech published?
34 articles on this beat, the most recent published October 8, 2026 and the earliest July 3, 2026. 130 questions have a dedicated answer page with an authored direct answer.