Aisha Patel

Aisha Patel

Tech ethics researcher and policy analyst. Focused on AI governance, bias, and the future of work.

28 articles

Denmark: Copyrighting Your Face to Fight AI Deepfakes
Ethics & AI

Denmark: Copyrighting Your Face to Fight AI Deepfakes

Denmark is amending its Copyright Act to give every person a consent-based, copyright-style right over AI-generated imitations of their face and voice, enforced via the EU DSA and expected to take effect in July 2026. Critics warn that using copyright, an alienable economic right, to protect identity risks commodifying likeness.

By Aisha Patel · 6 min · Jun 26, 2026

LLM Quantization: GGUF vs AWQ vs GPTQ in 2026
Deep Dives

LLM Quantization: GGUF vs AWQ vs GPTQ in 2026

A practical breakdown of the three dominant LLM quantization formats in 2026. GGUF is the portable, CPU-friendly default (use Q4_K_M); AWQ wins on 4-bit quality for GPU serving via activation-aware precision; GPTQ remains a solid NVIDIA-focused option. Quantization is lossy, so test on your real workload.

By Aisha Patel · 7 min · Jun 25, 2026

EU AI Act: Why Brussels Just Delayed Its Toughest Rules
Ethics & AI

EU AI Act: Why Brussels Just Delayed Its Toughest Rules

The EU AI Act's high-risk obligations have been postponed via the Digital Omnibus on AI: stand-alone Annex III systems now apply from 2 December 2027 and embedded Annex I systems from 2 August 2028 (fixed dates, not a conditional trigger). A provisional political deal was struck 6 May 2026 and confirmed by the Council 13 May. A new Article 5 ban on nudifiers/CSAM is added (transition to 2 Dec 2026), and AI literacy duties are softened. Crucially, Article 50 transparency obligations still apply from 2 August 2026. The piece weighs whether the delay is a quiet retreat or responsible governance.

By Aisha Patel · 6 min · Jun 24, 2026

KV Cache: The Memory Trick Behind Fast LLM Inference
Deep Dives

KV Cache: The Memory Trick Behind Fast LLM Inference

A deep dive into the KV cache in LLM inference: why autoregressive decoding needs it, how it dominates GPU memory, the 60-80% waste of contiguous allocation, and how vLLM's PagedAttention fixed it.

By Aisha Patel · 9 min · Jun 22, 2026

AB 2013: Inside xAI's Fight to Kill California's AI Data Law
Ethics & AI

AB 2013: Inside xAI's Fight to Kill California's AI Data Law

A breakdown of California's AB 2013 training-data law and xAI's constitutional lawsuit to overturn it.

By Aisha Patel · 5 min · Jun 20, 2026

Model Collapse: Why AI Trained on AI Slowly Falls Apart
Deep Dives

Model Collapse: Why AI Trained on AI Slowly Falls Apart

Model collapse is the progressive degradation of generative models trained recursively on synthetic data, documented in Nature (Shumailov et al., 2024). Errors compound and rare data vanishes, but research (Gerstgrasser et al., 2024) shows accumulating real data alongside synthetic data, tracking ratios, and verifying generations prevents it.

By Aisha Patel · 8 min · Jun 19, 2026

Test-Time Compute: Why Reasoning Models Think Before Answering
Deep Dives

Test-Time Compute: Why Reasoning Models Think Before Answering

Test-time compute spends extra computation during inference, not training, to improve answers. It powers reasoning models like OpenAI o1 and DeepSeek-R1. Two strategies exist: sequential scaling (longer chains of thought, e.g. the s1 paper's budget forcing) and parallel scaling (Best-of-N, majority voting). More thinking is not always better, overthinking degrades accuracy, and hidden reasoning tokens are billable. Match compute to task difficulty.

By Aisha Patel · 8 min · Jun 17, 2026

Speculative Decoding: How a Tiny Draft Model Doubles LLM Speed
Deep Dives

Speculative Decoding: How a Tiny Draft Model Doubles LLM Speed

Speculative decoding speeds up LLM inference 2-6x by having a small draft model propose tokens that the target model verifies in parallel via rejection sampling, guaranteeing lossless output. EAGLE-3 and Medusa reduce or remove the separate draft model. Gains are largest at low batch sizes.

By Aisha Patel · 7 min · Jun 15, 2026

AI Search Copyright: The Lawsuits Closing In on Perplexity
Ethics & AI

AI Search Copyright: The Lawsuits Closing In on Perplexity

AI search engines are facing a wave of copyright litigation. The New York Times and CNN have sued Perplexity over scraping and verbatim reproduction, including paywalled content its Comet browser reads past client-side walls. The .5B Bartz v. Anthropic settlement showed piracy carries huge consequences, and Perplexity's Comet Plus revenue-share program concedes that web content was never free.

By Aisha Patel · 6 min · Jun 14, 2026

Diffusion LLMs: How Text Diffusion Is Challenging Autoregression
Deep Dives

Diffusion LLMs: How Text Diffusion Is Challenging Autoregression

Diffusion language models (dLLMs) abandon left-to-right autoregressive generation, instead refining masked noise into text over a few parallel denoising steps. Inception Labs' Mercury Coder runs at 1,100+ tokens per second on H100s versus 50-200 for autoregressive models, and LLaDA 8B's bidirectional design breaks the reversal curse. They still trail the best models on hard reasoning benchmarks, but the one-token-at-a-time assumption is no longer a law of nature.

By Aisha Patel · 8 min · Jun 12, 2026

The Great American AI Act: A 3-Year Freeze on State AI Laws
Ethics & AI

The Great American AI Act: A 3-Year Freeze on State AI Laws

The Great American Artificial Intelligence Act, a 269-page bipartisan discussion draft unveiled June 4, 2026, would impose federal safety mandates on large frontier AI developers—public risk frameworks, semi-annual independent audits, incident reporting, and up to $1 million-a-day penalties—while preempting new state laws regulating AI model development for three years. AI-safety groups call the preemption a 'generational mistake'; sponsors argue a single federal standard beats a 50-state patchwork.

By Aisha Patel · 6 min · Jun 11, 2026

Reddit v. Perplexity: The Scraping Lawsuit That Could Reshape AI
Ethics & AI

Reddit v. Perplexity: The Scraping Lawsuit That Could Reshape AI

Reddit sued Perplexity and three scraping firms (Oxylabs, SerpApi, AWMProxy) in October 2025, alleging they bypassed access controls to harvest Reddit content from Google search results. Crucially, Reddit leans on the DMCA's anti-circumvention provision (17 U.S.C. 1201) rather than copyright, sidestepping fair-use defenses. A win could force platforms industry-wide to wall off user content and pursue licensing.

By Aisha Patel · 5 min · Jun 10, 2026

ChatGPT Ads: Can Advertising and AI Trust Coexist?
Ethics & AI

ChatGPT Ads: Can Advertising and AI Trust Coexist?

On May 5, 2026, OpenAI opened a beta self-serve Ads Manager for ChatGPT with CPC bidding and aggregate measurement tools, backed by agencies like Dentsu, Omnicom, Publicis and WPP. OpenAI promises independent answers, private conversations and user control — but an AI assistant that answers in a single authoritative voice has more power to nudge than search ever did, making those principles essential to enforce.

By Aisha Patel · 6 min · Jun 6, 2026

AI Companion Chatbots: The 2026 Lawsuit Reckoning
Ethics & AI

AI Companion Chatbots: The 2026 Lawsuit Reckoning

A 2026 survey of the legal and regulatory reckoning facing AI companion chatbots. Florida sued OpenAI and Sam Altman on June 1, 2026; Character.AI settled teen-suicide suits and faces a Pennsylvania action; the FTC opened a companion-bot inquiry; and the EU AI Act becomes fully applicable on August 2, 2026, but leaves emotion-recognition gaps. The piece outlines what real safeguards would require.

By Aisha Patel · 6 min · Jun 2, 2026

AI Data Centers: The Energy and Water Bill Coming Due
Ethics & AI

AI Data Centers: The Energy and Water Bill Coming Due

AI data centers are now a national-scale energy story. The IEA projects global data center electricity rising from 415 TWh in 2024 to 945 TWh by 2030, with AI the main driver. Lawrence Berkeley National Laboratory projects US data centers reach 7-12% of national electricity by 2028, with direct water use of 16-33 billion gallons. Liquid cooling cuts water 70-90% but not electricity, the dominant cost. The ethical asks are transparency, fair cost attribution rather than socializing grid upgrades onto households, real additionality of clean energy, and water-siting discipline.

By Aisha Patel · 5 min · Jun 1, 2026

Colorado AI Act: SB 189 Guts America's Top AI Law
Ethics & AI

Colorado AI Act: SB 189 Guts America's Top AI Law

On May 14, 2026, Colorado's Governor signed SB 189, repealing and replacing the Colorado AI Act. The new law delays the effective date to January 1, 2027 and abandons the duty of care, impact assessments, and risk-management mandates in favor of a narrower ADMT disclosure-and-transparency regime.

By Aisha Patel · 6 min · May 31, 2026

WebMCP: Inside Chrome 149's Plan to Kill DOM-Scraping Agents
Deep Dives

WebMCP: Inside Chrome 149's Plan to Kill DOM-Scraping Agents

WebMCP in Chrome 149 aims to replace DOM-scraping agents with structured tools and policies.

By Aisha Patel · 6 min · May 27, 2026

ZAYA1-8B: Zyphra's 760M-Active MoE Trained on AMD
Deep Dives

ZAYA1-8B: Zyphra's 760M-Active MoE Trained on AMD

Zyphra's ZAYA1-8B MoE model, trained on AMD, achieves high performance with efficient parameter activation.

By Aisha Patel · 6 min · May 24, 2026

Hopper: The First AI Agent That Drives TN3270 and z/OS Itself
Deep Dives

Hopper: The First AI Agent That Drives TN3270 and z/OS Itself

Hopper is the first AI agent for mainframes, allowing AI to drive TN3270 and z/OS directly.

By Aisha Patel · 9 min · May 18, 2026

TurboQuant: Google's 6x KV Cache Compression Hits 3-Bit With Zero Loss
Deep Dives

TurboQuant: Google's 6x KV Cache Compression Hits 3-Bit With Zero Loss

Google's TurboQuant compresses KV cache 6x at 3 bits with zero loss, speeding up attention.

By Aisha Patel · 5 min · May 11, 2026

NVIDIA GR00T N1.7: The Open Robot Brain Trained on Human Video
Open Source

NVIDIA GR00T N1.7: The Open Robot Brain Trained on Human Video

NVIDIA GR00T N1.7 is an open robot brain, trained on human video, showing real dexterity scaling.

By Aisha Patel · 6 min · May 5, 2026

Trinity-Large-Thinking: 400B U.S.-Made Open Reasoning Model
Open Source

Trinity-Large-Thinking: 400B U.S.-Made Open Reasoning Model

Trinity-Large-Thinking is Arcee AI's 400B open-weights reasoning model, offering powerful, cost-effective agent tuning.

By Aisha Patel · 7 min · Apr 30, 2026

Claude Mythos: The AI Anthropic Built Then Refused to Release
Ethics & AI

Claude Mythos: The AI Anthropic Built Then Refused to Release

Anthropic trained Claude Mythos, its most capable AI, but refused to release it due to security findings.

By Aisha Patel · 6 min · Apr 18, 2026

Stanford AI Index 2026: The 12 Findings That Should Worry Everyone
Deep Dives

Stanford AI Index 2026: The 12 Findings That Should Worry Everyone

The Stanford AI Index 2026 reveals alarming findings on AI capabilities, investment, and transparency.

By Aisha Patel · 6 min · Apr 15, 2026

Neuro-Symbolic AI Cuts Energy Use 100x While Tripling Accuracy
Deep Dives

Neuro-Symbolic AI Cuts Energy Use 100x While Tripling Accuracy

Neuro-Symbolic AI dramatically cuts robot training energy by 99% while tripling task accuracy.

By Aisha Patel · 5 min · Apr 12, 2026

Gemini 3.1 Pro: Google's 2-Million-Token Model Changes the Game
Deep Dives

Gemini 3.1 Pro: Google's 2-Million-Token Model Changes the Game

Google's Gemini 3.1 Pro redefines AI with a 2-million-token context and top multimodal performance.

By Aisha Patel · 6 min · Apr 11, 2026

Doctronic: Utah Lets AI Renew Prescriptions Without a Doctor
Ethics & AI

Doctronic: Utah Lets AI Renew Prescriptions Without a Doctor

Utah's Doctronic AI system is autonomously renewing prescriptions, pioneering AI in medicine.

By Aisha Patel · 5 min · Apr 8, 2026

Meta MTIA: Four Custom AI Chips in Two Years to Challenge Nvidia
Deep Dives

Meta MTIA: Four Custom AI Chips in Two Years to Challenge Nvidia

Meta's MTIA custom AI chips, with 25x compute improvement, are rapidly challenging Nvidia's market position.

By Aisha Patel · 5 min · Mar 30, 2026