That's fair, but how do you grep down to the right 100-200 documents from millions without semantic understanding? If someone asks "What's our supply chain exposure?" grep won't find documents discussing "vendor dependencies" or "sourcing risks."
You could expand grep queries with synonyms, but now you're reimplementing query expansion, which is already part of modern RAG. And doing that intelligently means you're back to using embeddings anyway.
The workflow works great for codebases with consistent terminology. For enterprise knowledge bases with varied language and conceptual queries, grep alone can't get you to the right candidates.
This glosses over a fundamental scaling problem that undermines the entire argument. The author's main example is Claude Code searching through local codebases with grep and ripgrep, then extrapolates this to claim RAG is dead for all document retrieval. That's a massive logical leap.
Grep works great when you have thousands of files on a local filesystem that you can scan in milliseconds. But most enterprise RAG use cases involve millions of documents across distributed systems. Even with 2M token context windows, you can't fit an entire enterprise knowledge base into context. The author acknowledges this briefly ("might still use hybrid search") but then continues arguing RAG is obsolete.
The bigger issue is semantic understanding. Grep does exact keyword matching. If a user searches for "revenue growth drivers" and the document discusses "factors contributing to increased sales," grep returns nothing. This is the vocabulary mismatch problem that embeddings actually solve. The author spent half the article complaining about RAG's limitations with this exact scenario (his $5.1B litigation example), then proposes grep as the solution, which would perform even worse.
Also, the claim that "agentic search" replaces RAG is misleading. Recent research shows agentic RAG systems embed agents INTO the RAG pipeline to improve retrieval, they don't replace chunking and embeddings. LlamaIndex's "agentic retrieval" still uses vector databases and hybrid search, just with smarter routing.
Context windows are impressive, but they're not magic. The article reads like someone who solved a specific problem (code search) and declared victory over a much broader domain.
Having technical access to prompts doesn't equal knowledge for criminal liability. Under 18 USC § 842, you need actual knowledge that specific information is being provided to someone who intends to use it for a crime. The fact that OpenAI's servers process millions of queries doesn't mean they have criminal knowledge of each one. That's not how mens rea works.
Prior restraint is presumptively unconstitutional. The burden is on the government to justify it under strict scrutiny. You don't have to prove something is protected speech first. The government has to prove it's unprotected and that prior restraint is narrowly tailored and the least restrictive means. SB 53 fails that test.
The FCC comparison doesn't help you. In Red Lion Broadcasting Co. v. FCC, the Supreme Court allowed broadcast regulation only because of spectrum scarcity, the physical limitation that there aren't enough radio frequencies for everyone. AI doesn't use a scarce public resource. There's no equivalent justification for content regulation. The FCC hasn't even enforced the fairness doctrine since 1987.
The real issue is you're trying to carve out AI as a special category with weaker First Amendment protection. That's exactly what I'm arguing against. The government doesn't get to create new exceptions to prior restraint doctrine just because the technology is new. If AI produces unprotected speech, prosecute it after the fact under existing law. You don't build mandatory filtering infrastructure and hand the government the power to define what's "dangerous."
You're conflating individual criminal liability with mandated prior restraint. If someone tells a chatbot they're going to commit a crime and the AI helps them, prosecute under existing law. But the company doesn't have knowledge of every individual interaction. That's not how the knowledge requirement works. You can't bootstrap individual criminal use into "the company should have known someone might use this for crimes, therefore they must filter everything."
The "companies want this" argument is irrelevant. Even if true, it doesn't make prior restraint constitutional. The government can't delegate its censorship powers to willing corporations. If companies are worried about liability, the answer is tort reform or clarifying safe harbor provisions, not building state-mandated filtering infrastructure.
On whether AI output is the company's speech: The First Amendment issue here isn't whose speech it is. It's that the government is compelling content-based restrictions. SB 53 doesn't just hold companies liable after harm occurs. It requires them to assess "dangerous capabilities" and implement "mitigations" before anyone gets hurt. That's prior restraint regardless of whether you call it the company's speech or not.
Your argument about LLMs being imperfect actually proves my point. You're saying mistakes will happen, so we need a framework. But the framework you're defending says the government gets to define what counts as dangerous and mandate filtering for it. That's exactly the infrastructure I'm warning about. Today it's "we can't perfectly control the models." Tomorrow it's "since we have to filter anyway, here are some other categories the state defines as harmful."
Given companies can't control their models perfectly due to the nature of AI technology, that's a product liability question, not a reason to establish government-mandated content filtering.
There's no contradiction. "True threats" is already a narrow exception defined by decades of Supreme Court precedent. It means statements where the speaker intends to communicate a serious expression of intent to commit unlawful violence against a person or group. That's it. It's not a blank check for the government to decide what counts as dangerous.
Brandenburg gives us the standard: speech can only be restricted if it's directed to inciting imminent lawless action and is likely to produce that action. True threats, child porn, fraud, these are all narrow, well-defined categories that survived strict scrutiny. They don't support creating broad new regulatory authority to filter outputs based on "dangerous capabilities."
You're asking how I define true threats. I don't. The Supreme Court does. That's the point. We have a constitutional framework for unprotected speech. It's extremely limited. The government can't just expand it because they think AI is scary.
"This technology is different" is what every regulator says about every new technology. Print was different. Radio was different. The internet was different. The First Amendment applies regardless. If AI enables someone to commit a crime, prosecute the crime. You don't get to regulate the information itself.
And yes, I want the government to stay out of mandating content restrictions. Not because I trust corporations, but because I trust the government even less with the power to define what information is too dangerous to share. You say governments are meant to serve citizens. Tell that to every government that's used "safety" as justification for censorship.
The issue isn't whether we need any AI regulation. It's whether we want to establish that the government can force companies to implement filtering systems based on the state's assessment of what capabilities are dangerous. That's the precedent SB 53 creates. Once that infrastructure exists, it will be used for whatever the government decides needs "safety mitigations" next.
You're actually making my point for me. 18 USC § 842 criminalizes distributing information with knowledge or intent that it will be used to commit a crime. That's criminal liability for completed conduct with a specific mens rea requirement. You have to actually know or intend the criminal use.
SB 53 is different. It requires companies to implement filtering systems before anyone commits a crime or demonstrates criminal intent. Companies must assess whether their models can "provide expert-level assistance" in creating weapons or "engage in conduct that would constitute a crime," then implement controls to prevent those outputs. That's not punishing distribution to someone you know will commit a crime. It's mandating prior restraint based on what the government defines as potentially dangerous.
Brandenburg already handles this. If someone uses an AI to help commit a crime, prosecute them. If a company knowingly provides a service to facilitate imminent lawless action, that's already illegal. We don't need a regulatory framework that treats the capability itself as the threat.
The "AIs don't have speech rights" argument misses the point. The First Amendment question isn't about the AI's rights. It's about the government compelling companies (or anyone) to restrict information based on content. When the state mandates that companies must identify and filter certain types of information because the government deemed them "dangerous capabilities," that's a speech restriction on the companies.
And yes, companies control their outputs now. The problem is SB 53 removes that discretion by legally requiring them to "mitigate" government-defined risks. That's compelled filtering. The government is forcing companies to build censorship infrastructure instead of letting them make editorial choices.
The real issue is precedent. Today it's bioweapons and cyberattacks. But once we establish that government can mandate "safety" assessments and require mitigation of "dangerous capabilities," that framework applies to whatever gets defined as dangerous tomorrow.
Look at what the bill actually requires. Companies have to publish frameworks showing how they "mitigate catastrophic risk" and implement "safety protocols" for "dangerous capabilities." That sounds reasonable until you realize the government is now defining what counts as dangerous and requiring private companies to build systems that restrict those outputs.
The Supreme Court already settled this. Brandenburg gives us the standard: imminent lawless action. Add in the narrow exceptions like child porn and true threats, and that's it. The government doesn't get to create new categories of "dangerous speech" just because the technology is new.
But here we have California mandating that AI companies assess whether their models can "provide expert-level assistance" in creating weapons or "engage in conduct that would constitute a crime." Then they have to implement mitigations and report to the state AG. That's prior restraint. The state is compelling companies to filter outputs based on potential future harm, which is exactly what the First Amendment prohibits.
Yes, bioweapons and cyberattacks are scary. But the solution isn't giving the government power to define "safety" and force companies to censor accordingly. If someone actually uses AI to commit a crime, prosecute them under existing law. You don't need a new regulatory framework that treats information itself as the threat.
This creates the infrastructure. Today it's "catastrophic risks." Tomorrow it's misinformation, hate speech, or whatever else the state decides needs "safety mitigations." Once you accept the premise that government can mandate content restrictions for safety, you've lost the argument.
This really captures something I've been experiencing with Gemini lately. The models are genuinely capable when they work properly, but there's this persistent truncation issue that makes them unreliable in practice.
I've been running into it consistently, responses that just stop mid-sentence, not because of token limits or content filters, but what appears to be a bug in how the model signals completion. It's been documented on their GitHub and dev forums for months as a P2 issue.
The frustrating part is that when you compare a complete Gemini response to Claude or GPT-4, the quality is often quite good. But reliability matters more than peak performance. I'd rather work with a model that consistently delivers complete (if slightly less brilliant) responses than one that gives me half-thoughts I have to constantly prompt to continue.
It's a shame because Google clearly has the underlying tech. But until they fix these basic conversation flow issues, Gemini will keep feeling broken compared to the competition, regardless of how it performs on benchmarks.
CTO of PolicyGenius (NYC) here. We'd love to have former SoundClouders come join. Not just for engineering, we have a bunch of openings across the business. You can also email [email protected].
I actually specifically wrote about this after receiving an email calling imposter syndrome out as the reason the person wanted to stop the interview process. Some thoughts on how to overcome it are in the article.
You could expand grep queries with synonyms, but now you're reimplementing query expansion, which is already part of modern RAG. And doing that intelligently means you're back to using embeddings anyway.
The workflow works great for codebases with consistent terminology. For enterprise knowledge bases with varied language and conceptual queries, grep alone can't get you to the right candidates.