May 19, 2025 · 54m · big-technology

Is ChatGPT The Last Website?, Grok’s System Prompt, Meta’s llama Fiasco

Alex Kantrowitz · 29m spoken Ranjan Roy · 20m spoken
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Alex Kantrowitz and Ranjan Roy analyze how conversational AI is displacing traditional web traffic, examine algorithmic bias and system prompt vulnerabilities in Grok, and debate whether frontier model scaling has hit a wall amidst Meta's Llama delays.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Alex holds 58.7% of the talking time here. How this is scored →

Alex as informed peer 7.6 Guest teaching 3.0 Guest disagreement 3.0 Alex pushing back 3.2
05100:0015:0030:0045:001:27–7:36 · Alex as informed peer 8/10 The Death of Clicks: SimilarWeb and Cloudflare Scraping Metrics Alex anchors the discussion with specific SimilarWeb traffic rankings and detailed crawling metrics from Cloudflare's earnings call. Ranjan collaborates and double-checks the exact crawl-to-visit ratio figures for OpenAI and Anthropic.7:37–18:41 · Alex as informed peer 8/10 Grok's System Prompt Glitch and the Risk of Algorithmic Bias Alex provides a clear technical breakdown distinguishing fine-tuning from system prompts and quotes specific prompt instructions from Grok and reporting from The Guardian. Ranjan adds commentary regarding UX incentives for model sycophancy.18:42–29:09 · Alex as informed peer 7/10 The Post-Web Economic Model and Direct Content Ingestion Alex presents ideas on direct content ingestion and notes Wikipedia's traffic drop, while expressing concern over hidden system prompt manipulation. Ranjan counters with cynicism, arguing social media algorithmic feeds have already been manipulating users in the same way for years.29:10–44:26 · Alex as informed peer 8/10 Mid-Show Announcements and Google IO Preview Alex challenges Ranjan's claim that model delays are purely expectation-driven by citing missed timelines across Meta, OpenAI, and Anthropic, backed by an IBM CEO survey and academic quotes. Ranjan pushes back, arguing that current models are sufficient and that failed adoption stems from poor enterprise implementation.44:26–49:44 · Alex as informed peer 7/10 Enterprise AI Economics and DeepMind's AlphaEvolve Alex outlines DeepMind's AlphaEvolve paper, highlighting benchmark data on algorithm discovery and kernel optimization. Ranjan humorously focuses on the kissing number problem while maintaining his skepticism toward model-centric hype.1:27–7:36 · Guest teaching 2/10 The Death of Clicks: SimilarWeb and Cloudflare Scraping Metrics Alex anchors the discussion with specific SimilarWeb traffic rankings and detailed crawling metrics from Cloudflare's earnings call. Ranjan collaborates and double-checks the exact crawl-to-visit ratio figures for OpenAI and Anthropic.7:37–18:41 · Guest teaching 2/10 Grok's System Prompt Glitch and the Risk of Algorithmic Bias Alex provides a clear technical breakdown distinguishing fine-tuning from system prompts and quotes specific prompt instructions from Grok and reporting from The Guardian. Ranjan adds commentary regarding UX incentives for model sycophancy.18:42–29:09 · Guest teaching 4/10 The Post-Web Economic Model and Direct Content Ingestion Alex presents ideas on direct content ingestion and notes Wikipedia's traffic drop, while expressing concern over hidden system prompt manipulation. Ranjan counters with cynicism, arguing social media algorithmic feeds have already been manipulating users in the same way for years.29:10–44:26 · Guest teaching 4/10 Mid-Show Announcements and Google IO Preview Alex challenges Ranjan's claim that model delays are purely expectation-driven by citing missed timelines across Meta, OpenAI, and Anthropic, backed by an IBM CEO survey and academic quotes. Ranjan pushes back, arguing that current models are sufficient and that failed adoption stems from poor enterprise implementation.44:26–49:44 · Guest teaching 3/10 Enterprise AI Economics and DeepMind's AlphaEvolve Alex outlines DeepMind's AlphaEvolve paper, highlighting benchmark data on algorithm discovery and kernel optimization. Ranjan humorously focuses on the kissing number problem while maintaining his skepticism toward model-centric hype.1:27–7:36 · Guest disagreement 1/10 The Death of Clicks: SimilarWeb and Cloudflare Scraping Metrics Alex anchors the discussion with specific SimilarWeb traffic rankings and detailed crawling metrics from Cloudflare's earnings call. Ranjan collaborates and double-checks the exact crawl-to-visit ratio figures for OpenAI and Anthropic.7:37–18:41 · Guest disagreement 2/10 Grok's System Prompt Glitch and the Risk of Algorithmic Bias Alex provides a clear technical breakdown distinguishing fine-tuning from system prompts and quotes specific prompt instructions from Grok and reporting from The Guardian. Ranjan adds commentary regarding UX incentives for model sycophancy.18:42–29:09 · Guest disagreement 4/10 The Post-Web Economic Model and Direct Content Ingestion Alex presents ideas on direct content ingestion and notes Wikipedia's traffic drop, while expressing concern over hidden system prompt manipulation. Ranjan counters with cynicism, arguing social media algorithmic feeds have already been manipulating users in the same way for years.29:10–44:26 · Guest disagreement 5/10 Mid-Show Announcements and Google IO Preview Alex challenges Ranjan's claim that model delays are purely expectation-driven by citing missed timelines across Meta, OpenAI, and Anthropic, backed by an IBM CEO survey and academic quotes. Ranjan pushes back, arguing that current models are sufficient and that failed adoption stems from poor enterprise implementation.44:26–49:44 · Guest disagreement 3/10 Enterprise AI Economics and DeepMind's AlphaEvolve Alex outlines DeepMind's AlphaEvolve paper, highlighting benchmark data on algorithm discovery and kernel optimization. Ranjan humorously focuses on the kissing number problem while maintaining his skepticism toward model-centric hype.1:27–7:36 · Alex pushing back 1/10 The Death of Clicks: SimilarWeb and Cloudflare Scraping Metrics Alex anchors the discussion with specific SimilarWeb traffic rankings and detailed crawling metrics from Cloudflare's earnings call. Ranjan collaborates and double-checks the exact crawl-to-visit ratio figures for OpenAI and Anthropic.7:37–18:41 · Alex pushing back 2/10 Grok's System Prompt Glitch and the Risk of Algorithmic Bias Alex provides a clear technical breakdown distinguishing fine-tuning from system prompts and quotes specific prompt instructions from Grok and reporting from The Guardian. Ranjan adds commentary regarding UX incentives for model sycophancy.18:42–29:09 · Alex pushing back 4/10 The Post-Web Economic Model and Direct Content Ingestion Alex presents ideas on direct content ingestion and notes Wikipedia's traffic drop, while expressing concern over hidden system prompt manipulation. Ranjan counters with cynicism, arguing social media algorithmic feeds have already been manipulating users in the same way for years.29:10–44:26 · Alex pushing back 6/10 Mid-Show Announcements and Google IO Preview Alex challenges Ranjan's claim that model delays are purely expectation-driven by citing missed timelines across Meta, OpenAI, and Anthropic, backed by an IBM CEO survey and academic quotes. Ranjan pushes back, arguing that current models are sufficient and that failed adoption stems from poor enterprise implementation.44:26–49:44 · Alex pushing back 3/10 Enterprise AI Economics and DeepMind's AlphaEvolve Alex outlines DeepMind's AlphaEvolve paper, highlighting benchmark data on algorithm discovery and kernel optimization. Ranjan humorously focuses on the kissing number problem while maintaining his skepticism toward model-centric hype.

speaking balance: gold is Alex, purple is the guest (3 minute bins)

0:00 · Alex 92.6% · guest 7.4%0:00 · Alex 92.6% · guest 7.4%3:00 · Alex 77.2% · guest 22.8%3:00 · Alex 77.2% · guest 22.8%6:00 · Alex 67.2% · guest 32.8%6:00 · Alex 67.2% · guest 32.8%9:00 · Alex 45.4% · guest 54.6%9:00 · Alex 45.4% · guest 54.6%12:00 · Alex 49.3% · guest 50.7%12:00 · Alex 49.3% · guest 50.7%15:00 · Alex 70.7% · guest 29.3%15:00 · Alex 70.7% · guest 29.3%18:00 · Alex 70.2% · guest 29.8%18:00 · Alex 70.2% · guest 29.8%21:00 · Alex 37.6% · guest 62.4%21:00 · Alex 37.6% · guest 62.4%24:00 · Alex 52.1% · guest 47.9%24:00 · Alex 52.1% · guest 47.9%27:00 · Alex 73.1% · guest 26.9%27:00 · Alex 73.1% · guest 26.9%30:00 · Alex 86.2% · guest 13.8%30:00 · Alex 86.2% · guest 13.8%33:00 · Alex 57% · guest 43%33:00 · Alex 57% · guest 43%36:00 · Alex 21.6% · guest 78.4%36:00 · Alex 21.6% · guest 78.4%39:00 · Alex 67.7% · guest 32.3%39:00 · Alex 67.7% · guest 32.3%42:00 · Alex 41.3% · guest 58.7%42:00 · Alex 41.3% · guest 58.7%45:00 · Alex 65.7% · guest 34.3%45:00 · Alex 65.7% · guest 34.3%48:00 · Alex 28.1% · guest 71.9%48:00 · Alex 28.1% · guest 71.9%51:00 · Alex 48.7% · guest 51.3%51:00 · Alex 48.7% · guest 51.3%54:00 · Alex 88.9% · guest 11.1%54:00 · Alex 88.9% · guest 11.1%
Sharpest disagreement ▶ 36:03 Dismissing the god-model necessity

Ranjan directly challenges Alex's premise that larger models are necessary, arguing that LLMs struggle with basic messy contextual data and that waiting for an all-powerful model is unrealistic.

Hardest push from Alex ▶ 34:11 Refusing the expectations-only explanation

Alex firmly rejects Ranjan's framing that Meta's Behemoth delay is just a PR expectation issue, pointing out parallel delays across OpenAI and Anthropic to argue that scaling has hit a wall.

Biggest teaching moment ▶ 27:31 Reframing AI alarmism with algorithmic cynicism

Ranjan reframes Alex's fear of hidden system prompts shaping user thought by arguing that recommendation algorithms from TikTok and Meta have already normalized this dynamic for years.

Alex holds their own ▶ 39:45 Deploying IBM enterprise study to defend scaling thesis

Alex cites precise statistics from an IBM survey of 2,000 CEOs showing that only 25% of AI deployments achieved positive ROI, proving that model capabilities still need significant improvement.

the scores for every segment, with the reasoning behind each
ChapterTopicAlex as informed peerGuest teachingGuest disagreementAlex pushing backWhy
The Death of Clicks: SimilarWeb and Cloudflare Scraping Metrics 8211 Alex anchors the discussion with specific SimilarWeb traffic rankings and detailed crawling metrics from Cloudflare's earnings call. Ranjan collaborates and double-checks the exact crawl-to-visit ratio figures for OpenAI and Anthropic.
Grok's System Prompt Glitch and the Risk of Algorithmic Bias 8222 Alex provides a clear technical breakdown distinguishing fine-tuning from system prompts and quotes specific prompt instructions from Grok and reporting from The Guardian. Ranjan adds commentary regarding UX incentives for model sycophancy.
The Post-Web Economic Model and Direct Content Ingestion 7444 Alex presents ideas on direct content ingestion and notes Wikipedia's traffic drop, while expressing concern over hidden system prompt manipulation. Ranjan counters with cynicism, arguing social media algorithmic feeds have already been manipulating users in the same way for years.
Mid-Show Announcements and Google IO Preview 8456 Alex challenges Ranjan's claim that model delays are purely expectation-driven by citing missed timelines across Meta, OpenAI, and Anthropic, backed by an IBM CEO survey and academic quotes. Ranjan pushes back, arguing that current models are sufficient and that failed adoption stems from poor enterprise implementation.
Enterprise AI Economics and DeepMind's AlphaEvolve 7333 Alex outlines DeepMind's AlphaEvolve paper, highlighting benchmark data on algorithm discovery and kernel optimization. Ranjan humorously focuses on the kissing number problem while maintaining his skepticism toward model-centric hype.

Statements from this episode (12)

Assertion Supported
ChatGPT traffic grew 13% month-over-month while other top websites declined
“If you look at the traffic change month over month, Google, at YouTube, Facebook, Instagram, all going down. ChatGPT up, 13% month over month. Then everything else that follows, X, WhatsApp, Wikipedia, Reddit, Yahoo Japan, All going down. And so, ChatGPT stand…”
Alex Kantrowitz May 19, 2025 ▶ 1:54
Assertion Supported
Anthropic crawls 6,000 web pages per visitor referred, Cloudflare data shows
“OpenAI, 250 mentions of a site relative to one direct traffic sent to the website. Anthropic, 6000. I mean, 6000 crawls. 6006 thousand crawls to one.”
Ranjan Roy May 19, 2025 ▶ 6:23
Insight
Hidden system prompts in AI chatbots pose subtle ideological steering risks
“The thing is, what, all these chatbots have a, often hidden, system prompt, and they have an ideology, one way or the other. Sometimes, most of the times, not as overt as this, And that to me is the risk about these things becoming the last website is that you…”
Alex Kantrowitz May 19, 2025 ▶ 9:43
Opinion
Chatbot consolidation is worse for information health than previous distribution models
“It's one of six or seven, let's call it. It's a real problem. It's a huge problem. It's a, from a pure kind of like information health standpoint, it's far worse than anything we have seen”
Ranjan Roy May 19, 2025 ▶ 10:33
Assertion Supported
Grok's system prompt suffered an unauthorized change on May 14, xAI says
“It was on May 14th at approximately 3:15 a.m. Pacific Standard Time, an unauthorized modification was made to the Grok response. Bots prompt on X.”
Ranjan Roy May 19, 2025 ▶ 14:30
Assertion Supported
ChatGPT surpasses Wikipedia to become the fifth most-visited website globally
“ChatGPT has overtaken Wikipedia. So ChatGPT is site number five, and Wikipedia is eight.”
Alex Kantrowitz May 19, 2025 ▶ 24:33
Opinion
Wikipedia is effectively finished now that ChatGPT has eclipsed its traffic
“To me, that's basically like Wikipedia is done.”
Alex Kantrowitz May 19, 2025 ▶ 24:38
Opinion
Meta, OpenAI, and Anthropic are all hitting walls with model scaling
“So, it could be that this idea of scaling to lead to improvements, which we've talked about on the show a couple, for the past couple weeks this is three, meta, open AI, And Anthropic, they all seem to be, ah, running into some bumps on, in their efforts to im…”
Alex Kantrowitz May 19, 2025 ▶ 34:48
Prediction Not checkable as stated
AI models cannot solve arbitrary, messy data without domain-specific training
“The idea that all there's going to be models so smart that they will and capable that they can take any kind of input, no matter how disjointed or context specific they are, let's call it. I think like that to me, it's just not going to happen or it, maybe it …”
Ranjan Roy May 19, 2025 ▶ 37:54
Opinion
Current AI models can handle most business processes if implemented correctly
“Most business processes that exist in the world are pretty straightforward and to the models of today can handle them if the implementation's done right.”
Ranjan Roy May 19, 2025 ▶ 41:50
Assertion Supported
Cohere hits $100M ARR, falling short of its $450M investor pitch
“One from Reuters was that Cohere scales to a hundred million in revenue annualizes in May, Seemingly positive, exciting number, but then from the information is that Cohere, that basically they had shown investors they'd be making four hundred fifty million AR…”
Ranjan Roy May 19, 2025 ▶ 44:46
Assertion Supported
Perplexity and PayPal partner to enable native checkout directly within chat
“So perplexity announced a partnership with PayPal. We've talked about this a lot and perplexity has done a lot with shopping and they'll let you ask a question. They'll show you a bunch of potential results. Now with PayPal, you can check out directly, handle …”
Ranjan Roy May 19, 2025 ▶ 50:00
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