AI labs testify under oath in NYC | The Austin Wilson Show, Oct 6, 2026
2026-10-06
https://www.youtube.com/watch?v=22-ybU6bTeE

[0:03] Every morning the future moves faster,
[0:08] Every morning the future moves faster, the models, the money,
[0:10] the models, the money,
[0:10] the models, the money, the power and the people building what
[0:13] the power and the people building what
[0:13] the power and the people building what comes next.
[0:46] from Austin, Texas to the world.
[0:53] AI news, real context. This is the
[0:57] AI news, real context. This is the Austin Wilson Show. I'm Austin Wilson,
[1:01] Austin Wilson Show. I'm Austin Wilson,
[1:01] Austin Wilson Show. I'm Austin Wilson, your human in the loop.
[1:09] Good morning and welcome to the Austin
[1:12] Good morning and welcome to the Austin Wilson Show. It's October 6th, 2026.
[1:17] Wilson Show. It's October 6th, 2026.
[1:17] Wilson Show. It's October 6th, 2026. Welcome. We are breaking the AI
[1:19] Welcome. We are breaking the AI
[1:19] Welcome. We are breaking the AI headlines that matter to you today. Hey,
[1:22] headlines that matter to you today. Hey,
[1:22] headlines that matter to you today. Hey, I'm Austin Wilson coming to you live
[1:24] I'm Austin Wilson coming to you live
[1:24] I'm Austin Wilson coming to you live from the AI capital of the world,
[1:26] from the AI capital of the world,
[1:26] from the AI capital of the world, Austin, Texas. And today we've got an
[1:28] Austin, Texas. And today we've got an
[1:28] Austin, Texas. And today we've got an exciting show for you. We're covering AI
[1:31] exciting show for you. We're covering AI
[1:31] exciting show for you. We're covering AI labs testifying under oath in New York
[1:33] labs testifying under oath in New York
[1:33] labs testifying under oath in New York City. Chat GPT text gets a watermark. AI
[1:38] City. Chat GPT text gets a watermark. AI
[1:38] City. Chat GPT text gets a watermark. AI now writes prescriptions in Utah. XRock
[1:41] now writes prescriptions in Utah. XRock
[1:41] now writes prescriptions in Utah. XRock engineers sue over Nvidia's $20 billion
[1:44] engineers sue over Nvidia's $20 billion
[1:44] engineers sue over Nvidia's $20 billion deal. Etched reportedly fields bids of
[1:47] deal. Etched reportedly fields bids of
[1:47] deal. Etched reportedly fields bids of up to $50 billion. and US startup takes
[1:51] up to $50 billion. and US startup takes
[1:52] up to $50 billion. and US startup takes on China's open models. Let's get into
[1:55] on China's open models. Let's get into
[1:55] on China's open models. Let's get into it. First up, we are talking about Open
[1:59] it. First up, we are talking about Open
[1:59] it. First up, we are talking about Open AI. Anthropic, Google, and Meta sent
[2:02] AI. Anthropic, Google, and Meta sent
[2:02] AI. Anthropic, Google, and Meta sent executives to testify under oath before
[2:05] executives to testify under oath before
[2:05] executives to testify under oath before the New York City Council and only after
[2:08] the New York City Council and only after
[2:08] the New York City Council and only after the council threatens subpoenas. We saw
[2:11] the council threatens subpoenas. We saw
[2:11] the council threatens subpoenas. We saw subpoenas recently out of California
[2:13] subpoenas recently out of California
[2:13] subpoenas recently out of California last week. Wonder if it'll lead to a
[2:15] last week. Wonder if it'll lead to a
[2:15] last week. Wonder if it'll lead to a similar outcome. All 51 council members
[2:18] similar outcome. All 51 council members
[2:18] similar outcome. All 51 council members sat as a committee of the whole on
[2:20] sat as a committee of the whole on
[2:20] sat as a committee of the whole on Monday asked the chance of an AI
[2:23] Monday asked the chance of an AI
[2:23] Monday asked the chance of an AI catastrophe. Open AAI's Morgan Dwire
[2:26] catastrophe. Open AAI's Morgan Dwire
[2:26] catastrophe. Open AAI's Morgan Dwire said, "I don't know. I also don't think
[2:28] said, "I don't know. I also don't think
[2:28] said, "I don't know. I also don't think it matters whether it's 1% or 10% or a
[2:31] it matters whether it's 1% or 10% or a
[2:32] it matters whether it's 1% or 10% or a 20% chance," adding that no level is
[2:34] 20% chance," adding that no level is
[2:34] 20% chance," adding that no level is acceptable. Speaker Julie Menon called
[2:37] acceptable. Speaker Julie Menon called
[2:37] acceptable. Speaker Julie Menon called that flipping. Anthropics Logan Graham
[2:40] that flipping. Anthropics Logan Graham
[2:40] that flipping. Anthropics Logan Graham and Meta's Shane Cahill gave no number.
[2:44] and Meta's Shane Cahill gave no number.
[2:44] and Meta's Shane Cahill gave no number. Google's Alex Friend said, "No rigorous
[2:47] Google's Alex Friend said, "No rigorous
[2:47] Google's Alex Friend said, "No rigorous method exists yet." And former uh Google
[2:51] method exists yet." And former uh Google
[2:51] method exists yet." And former uh Google DeepMind researcher Alex Turner put the
[2:54] DeepMind researcher Alex Turner put the
[2:54] DeepMind researcher Alex Turner put the odds of an eventual AI takeover at
[2:56] odds of an eventual AI takeover at
[2:56] odds of an eventual AI takeover at roughly 1 in3. Wow, these are uh
[3:00] roughly 1 in3. Wow, these are uh
[3:00] roughly 1 in3. Wow, these are uh interesting numbers. uh city council got
[3:02] interesting numbers. uh city council got
[3:02] interesting numbers. uh city council got the labs on the record under oath and
[3:05] the labs on the record under oath and
[3:05] the labs on the record under oath and none of them would put a specific number
[3:08] none of them would put a specific number
[3:08] none of them would put a specific number on a catastroph catastrophe or say who
[3:11] on a catastroph catastrophe or say who
[3:11] on a catastroph catastrophe or say who pays if an AI goes rogue. The company
[3:14] pays if an AI goes rogue. The company
[3:14] pays if an AI goes rogue. The company say they test their models for risks
[3:17] say they test their models for risks
[3:17] say they test their models for risks from cyber attacks to loss of control on
[3:20] from cyber attacks to loss of control on
[3:20] from cyber attacks to loss of control on liability. Open AAI, Anthropic, and
[3:23] liability. Open AAI, Anthropic, and
[3:23] liability. Open AAI, Anthropic, and Medic gave no direct answer. Only Google
[3:25] Medic gave no direct answer. Only Google
[3:26] Medic gave no direct answer. Only Google was clear and they said, "If it's
[3:27] was clear and they said, "If it's
[3:28] was clear and they said, "If it's illegal without AI, it's illegal with
[3:30] illegal without AI, it's illegal with
[3:30] illegal without AI, it's illegal with AI." Menon demanded written follow-ups.
[3:33] AI." Menon demanded written follow-ups.
[3:33] AI." Menon demanded written follow-ups. The bills on the table are city bills, a
[3:36] The bills on the table are city bills, a
[3:36] The bills on the table are city bills, a whistleblower reward program, a right to
[3:38] whistleblower reward program, a right to
[3:38] whistleblower reward program, a right to sue over harm from AI agents, and
[3:42] sue over harm from AI agents, and
[3:42] sue over harm from AI agents, and independent testing. The models are
[3:44] independent testing. The models are
[3:44] independent testing. The models are built far outside of New York City. Will
[3:47] built far outside of New York City. Will
[3:47] built far outside of New York City. Will their jurisdiction have an effect here?
[3:50] their jurisdiction have an effect here?
[3:50] their jurisdiction have an effect here? This is interesting to see as we saw
[3:52] This is interesting to see as we saw
[3:52] This is interesting to see as we saw Gavin Newsome sign uh uh uh some bills
[3:57] Gavin Newsome sign uh uh uh some bills
[3:57] Gavin Newsome sign uh uh uh some bills last week around AI and AI compliance
[4:01] last week around AI and AI compliance
[4:01] last week around AI and AI compliance and and how these frontier companies
[4:03] and and how these frontier companies
[4:03] and and how these frontier companies must uh act in the state of California.
[4:06] must uh act in the state of California.
[4:06] must uh act in the state of California. Uh we also saw a subpoena issued by the
[4:09] Uh we also saw a subpoena issued by the
[4:09] Uh we also saw a subpoena issued by the state of California uh which we suppose
[4:12] state of California uh which we suppose
[4:12] state of California uh which we suppose might lead to a similar uh kind of
[4:14] might lead to a similar uh kind of
[4:14] might lead to a similar uh kind of council here that we're seeing with uh
[4:17] council here that we're seeing with uh
[4:17] council here that we're seeing with uh New York City and getting some
[4:19] New York City and getting some
[4:19] New York City and getting some interesting takes. The the what does
[4:23] interesting takes. The the what does
[4:23] interesting takes. The the what does this matter though? Like I don't
[4:24] this matter though? Like I don't
[4:24] this matter though? Like I don't understand why they're pressing them on
[4:26] understand why they're pressing them on
[4:26] understand why they're pressing them on what are the odds that the world is
[4:27] what are the odds that the world is
[4:28] what are the odds that the world is going to end. The fact of the matter is
[4:29] going to end. The fact of the matter is
[4:29] going to end. The fact of the matter is this technology is here. It is
[4:31] this technology is here. It is
[4:31] this technology is here. It is fantastic. it is going to change
[4:33] fantastic. it is going to change
[4:33] fantastic. it is going to change everybody's lives and net
[4:37] everybody's lives and net
[4:37] everybody's lives and net net on net it's going to change lives
[4:39] net on net it's going to change lives
[4:39] net on net it's going to change lives for the better. So this is a a benefit
[4:41] for the better. So this is a a benefit
[4:41] for the better. So this is a a benefit to society. This is something that's uh
[4:44] to society. This is something that's uh
[4:44] to society. This is something that's uh excellent. Um do we know exactly where
[4:46] excellent. Um do we know exactly where
[4:46] excellent. Um do we know exactly where it's going to go? No, we we don't. But
[4:49] it's going to go? No, we we don't. But
[4:49] it's going to go? No, we we don't. But that's the important thing is we start
[4:51] that's the important thing is we start
[4:51] that's the important thing is we start to see safety and guard rails built
[4:53] to see safety and guard rails built
[4:53] to see safety and guard rails built around the technology which helps keep
[4:56] around the technology which helps keep
[4:56] around the technology which helps keep it in check as we do develop it. Nobody
[4:58] it in check as we do develop it. Nobody
[4:58] it in check as we do develop it. Nobody knew where the internet was going 30
[5:00] knew where the internet was going 30
[5:00] knew where the internet was going 30 years ago. And yet we created an open
[5:03] years ago. And yet we created an open
[5:03] years ago. And yet we created an open environment where people could go in,
[5:05] environment where people could go in,
[5:05] environment where people could go in, use the internet, use the technology and
[5:08] use the internet, use the technology and
[5:08] use the internet, use the technology and uncover vast uh opportunities for
[5:12] uncover vast uh opportunities for
[5:12] uncover vast uh opportunities for individuals, for companies, for
[5:14] individuals, for companies, for
[5:14] individuals, for companies, for countries. And it the internet has
[5:17] countries. And it the internet has
[5:17] countries. And it the internet has changed our lives uh night and day
[5:19] changed our lives uh night and day
[5:19] changed our lives uh night and day compared to 30 days or 30 years ago. And
[5:22] compared to 30 days or 30 years ago. And
[5:22] compared to 30 days or 30 years ago. And it's all to the benefit of society as a
[5:24] it's all to the benefit of society as a
[5:24] it's all to the benefit of society as a whole. And we expect to see the same
[5:26] whole. And we expect to see the same
[5:26] whole. And we expect to see the same thing with AI here. And so when you ask
[5:29] thing with AI here. And so when you ask
[5:29] thing with AI here. And so when you ask these executives at frontier companies
[5:32] these executives at frontier companies
[5:32] these executives at frontier companies what their number is on the chance that
[5:34] what their number is on the chance that
[5:34] what their number is on the chance that AI is going to end the world, it's what
[5:37] AI is going to end the world, it's what
[5:37] AI is going to end the world, it's what are we talking about? We're focusing on
[5:39] are we talking about? We're focusing on
[5:39] are we talking about? We're focusing on the wrong things here. We should be
[5:41] the wrong things here. We should be
[5:41] the wrong things here. We should be focusing on how to u skill up everybody
[5:45] focusing on how to u skill up everybody
[5:45] focusing on how to u skill up everybody that we can and get people uh using AI
[5:48] that we can and get people uh using AI
[5:48] that we can and get people uh using AI and understanding how to use AI and how
[5:50] and understanding how to use AI and how
[5:50] and understanding how to use AI and how AI can change uh their lives. So sad to
[5:54] AI can change uh their lives. So sad to
[5:54] AI can change uh their lives. So sad to see this coming out of New York City and
[5:56] see this coming out of New York City and
[5:56] see this coming out of New York City and the questions here in I think um moving
[5:59] the questions here in I think um moving
[5:59] the questions here in I think um moving forward. Um you know I think about if
[6:02] forward. Um you know I think about if
[6:02] forward. Um you know I think about if the state of Texas did something like
[6:03] the state of Texas did something like
[6:03] the state of Texas did something like this it's like what does this matter?
[6:05] this it's like what does this matter?
[6:05] this it's like what does this matter? they don't have any jurisdiction around
[6:07] they don't have any jurisdiction around
[6:07] they don't have any jurisdiction around the development of uh of this software
[6:09] the development of uh of this software
[6:10] the development of uh of this software of this technology and so to suppose
[6:13] of this technology and so to suppose
[6:13] of this technology and so to suppose that maybe New York would uh you know
[6:15] that maybe New York would uh you know
[6:15] that maybe New York would uh you know strap some sort of restriction on uh the
[6:18] strap some sort of restriction on uh the
[6:18] strap some sort of restriction on uh the the open AI or or anthropic or or or
[6:21] the open AI or or anthropic or or or
[6:21] the open AI or or anthropic or or or meta or Google labs uh is uh a little
[6:24] meta or Google labs uh is uh a little
[6:24] meta or Google labs uh is uh a little bit absurd and if anything else they
[6:26] bit absurd and if anything else they
[6:26] bit absurd and if anything else they were they would limit the the
[6:29] were they would limit the the
[6:29] were they would limit the the possibilities and capabilities of their
[6:32] possibilities and capabilities of their
[6:32] possibilities and capabilities of their own people. So, I hope that that doesn't
[6:34] own people. So, I hope that that doesn't
[6:34] own people. So, I hope that that doesn't occur.
[6:35] occur.
[6:35] occur. Moving on to our next story. Open AI
[6:38] Moving on to our next story. Open AI
[6:38] Moving on to our next story. Open AI will start hiding an invisible watermark
[6:41] will start hiding an invisible watermark
[6:41] will start hiding an invisible watermark in the text chat GPT and codecs uh that
[6:46] in the text chat GPT and codecs uh that
[6:46] in the text chat GPT and codecs uh that that is written for users in the
[6:48] that is written for users in the
[6:48] that is written for users in the European Union. OpenAI said Monday that
[6:52] European Union. OpenAI said Monday that
[6:52] European Union. OpenAI said Monday that over the coming weeks, eligible chat GPT
[6:55] over the coming weeks, eligible chat GPT
[6:55] over the coming weeks, eligible chat GPT and codeex output in the EU will carry
[6:59] and codeex output in the EU will carry
[6:59] and codeex output in the EU will carry text grain, a statistical signal hidden
[7:01] text grain, a statistical signal hidden
[7:01] text grain, a statistical signal hidden in the model's word choices. Readers
[7:04] in the model's word choices. Readers
[7:04] in the model's word choices. Readers can't see it. A detector can. API
[7:07] can't see it. A detector can. API
[7:07] can't see it. A detector can. API customers worldwide can switch it on,
[7:09] customers worldwide can switch it on,
[7:09] customers worldwide can switch it on, but it's off by default. OpenAI plans to
[7:12] but it's off by default. OpenAI plans to
[7:12] but it's off by default. OpenAI plans to open source uh the technology and at
[7:16] open source uh the technology and at
[7:16] open source uh the technology and at first only approved researchers and
[7:19] first only approved researchers and
[7:19] first only approved researchers and organizations get the detector.
[7:22] organizations get the detector.
[7:22] organizations get the detector. This is a curious story. Teachers,
[7:24] This is a curious story. Teachers,
[7:24] This is a curious story. Teachers, employers, and platforms want a way to
[7:27] employers, and platforms want a way to
[7:27] employers, and platforms want a way to tell AI writing from human writing. Chat
[7:30] tell AI writing from human writing. Chat
[7:30] tell AI writing from human writing. Chat GPT now joins Anthropics Cloud and
[7:32] GPT now joins Anthropics Cloud and
[7:32] GPT now joins Anthropics Cloud and Google's Gemini in marking its own text.
[7:35] Google's Gemini in marking its own text.
[7:35] Google's Gemini in marking its own text. The claim, OpenAI says this meets the EU
[7:39] The claim, OpenAI says this meets the EU
[7:39] The claim, OpenAI says this meets the EU AI acts transparency rules for AI
[7:41] AI acts transparency rules for AI
[7:41] AI acts transparency rules for AI generated content. The evidence OpenAI's
[7:44] generated content. The evidence OpenAI's
[7:44] generated content. The evidence OpenAI's own test at a 1% false positive rate.
[7:47] own test at a 1% false positive rate.
[7:47] own test at a 1% false positive rate. The detector found the mark in about 95%
[7:50] The detector found the mark in about 95%
[7:50] The detector found the mark in about 95% of 400 token passages and about 80% of
[7:52] of 400 token passages and about 80% of
[7:52] of 400 token passages and about 80% of 200 token passages. The catch swabbing
[7:55] 200 token passages. The catch swabbing
[7:55] 200 token passages. The catch swabbing 25% of the words only 25% of the words
[7:58] 25% of the words only 25% of the words
[7:58] 25% of the words only 25% of the words for synonyms drop the text to 17%. and
[8:01] for synonyms drop the text to 17%. and
[8:01] for synonyms drop the text to 17%. and OpenAI says, "A missing watermark, quote
[8:02] OpenAI says, "A missing watermark, quote
[8:02] OpenAI says, "A missing watermark, quote unquote, does not prove human
[8:05] unquote, does not prove human
[8:05] unquote, does not prove human authorship."
[8:07] authorship."
[8:07] authorship." Let's get into it. Uh to me, this makes
[8:10] Let's get into it. Uh to me, this makes
[8:10] Let's get into it. Uh to me, this makes zero sense at all. Um I I understand,
[8:13] zero sense at all. Um I I understand,
[8:13] zero sense at all. Um I I understand, you know, uh protecting against
[8:16] you know, uh protecting against
[8:16] you know, uh protecting against uh you know, false flags or or AI, you
[8:20] uh you know, false flags or or AI, you
[8:20] uh you know, false flags or or AI, you know, misleading people or or anything
[8:23] know, misleading people or or anything
[8:23] know, misleading people or or anything like that, any dangers around this. But
[8:26] like that, any dangers around this. But
[8:26] like that, any dangers around this. But at this point in time, AI is extremely
[8:29] at this point in time, AI is extremely
[8:29] at this point in time, AI is extremely detectable. AI writing is very very
[8:31] detectable. AI writing is very very
[8:31] detectable. AI writing is very very detectable by humans. Um and forcing the
[8:34] detectable by humans. Um and forcing the
[8:34] detectable by humans. Um and forcing the labs to put a watermark on this. I think
[8:36] labs to put a watermark on this. I think
[8:36] labs to put a watermark on this. I think this is just an example of governments
[8:38] this is just an example of governments
[8:38] this is just an example of governments make moving way slower than uh than
[8:42] make moving way slower than uh than
[8:42] make moving way slower than uh than technology and really than than the
[8:44] technology and really than than the
[8:44] technology and really than than the market. Um this might have been
[8:46] market. Um this might have been
[8:46] market. Um this might have been impactful 6 months ago or or something
[8:49] impactful 6 months ago or or something
[8:49] impactful 6 months ago or or something like that. But there are already
[8:51] like that. But there are already
[8:51] like that. But there are already solutions in uh the market available.
[8:54] solutions in uh the market available.
[8:54] solutions in uh the market available. panagramgram uh users can use to
[8:57] panagramgram uh users can use to
[8:57] panagramgram uh users can use to identify AI writing. I think I think
[9:00] identify AI writing. I think I think
[9:00] identify AI writing. I think I think this becomes more important when you're
[9:02] this becomes more important when you're
[9:02] this becomes more important when you're talking about deep fakes, fake videos,
[9:04] talking about deep fakes, fake videos,
[9:04] talking about deep fakes, fake videos, fake voice, fake images uh that look
[9:09] fake voice, fake images uh that look
[9:09] fake voice, fake images uh that look indistinguishable to um um a a real life
[9:13] indistinguishable to um um a a real life
[9:13] indistinguishable to um um a a real life human or sound like a real life human
[9:16] human or sound like a real life human
[9:16] human or sound like a real life human and that becomes dangerous and having
[9:18] and that becomes dangerous and having
[9:18] and that becomes dangerous and having watermarks on that I think become really
[9:20] watermarks on that I think become really
[9:20] watermarks on that I think become really important. Um and I believe that those
[9:24] important. Um and I believe that those
[9:24] important. Um and I believe that those watermarks are already in effect or uh
[9:27] watermarks are already in effect or uh
[9:27] watermarks are already in effect or uh you know already in place. Even still on
[9:30] you know already in place. Even still on
[9:30] you know already in place. Even still on that front we don't need the government
[9:31] that front we don't need the government
[9:31] that front we don't need the government to to tell us to watermark those things
[9:34] to to tell us to watermark those things
[9:34] to to tell us to watermark those things because there are already technologies
[9:36] because there are already technologies
[9:36] because there are already technologies that can identify AI generated image AI
[9:39] that can identify AI generated image AI
[9:40] that can identify AI generated image AI generated uh video and and voice because
[9:43] generated uh video and and voice because
[9:43] generated uh video and and voice because of the pattern that these uh
[9:45] of the pattern that these uh
[9:45] of the pattern that these uh technologies use were able to detect
[9:48] technologies use were able to detect
[9:48] technologies use were able to detect that. So um you know while I appreciate
[9:51] that. So um you know while I appreciate
[9:51] that. So um you know while I appreciate the safety effort coming out of the EU
[9:55] the safety effort coming out of the EU
[9:55] the safety effort coming out of the EU and again I appreciate how slow Open AI
[9:58] and again I appreciate how slow Open AI
[9:58] and again I appreciate how slow Open AI is too, you know, the last ones to adopt
[10:01] is too, you know, the last ones to adopt
[10:01] is too, you know, the last ones to adopt any safety uh measures and yet they seem
[10:04] any safety uh measures and yet they seem
[10:04] any safety uh measures and yet they seem to be the ones proliferating a lot of
[10:06] to be the ones proliferating a lot of
[10:06] to be the ones proliferating a lot of the safety issues. Um but uh to me it
[10:10] the safety issues. Um but uh to me it
[10:10] the safety issues. Um but uh to me it doesn't really have much of an impact.
[10:12] doesn't really have much of an impact.
[10:12] doesn't really have much of an impact. Uh, and I can guarantee you that we'll
[10:15] Uh, and I can guarantee you that we'll
[10:15] Uh, and I can guarantee you that we'll continue to see um, you know, headlines
[10:19] continue to see um, you know, headlines
[10:19] continue to see um, you know, headlines around safety efforts and safety
[10:21] around safety efforts and safety
[10:21] around safety efforts and safety mechanisms that really actually don't
[10:23] mechanisms that really actually don't
[10:23] mechanisms that really actually don't mean anything, unfortunately.
[10:27] mean anything, unfortunately.
[10:27] mean anything, unfortunately. This is a fun one. Utah is letting a uh,
[10:30] This is a fun one. Utah is letting a uh,
[10:30] This is a fun one. Utah is letting a uh, startups AI write acne prescriptions on
[10:34] startups AI write acne prescriptions on
[10:34] startups AI write acne prescriptions on its own. Uh, and after it writes
[10:38] its own. Uh, and after it writes
[10:38] its own. Uh, and after it writes prescriptions for its first 100
[10:39] prescriptions for its first 100
[10:39] prescriptions for its first 100 patients, no doctor needs to be involved
[10:42] patients, no doctor needs to be involved
[10:42] patients, no doctor needs to be involved before the prescription goes out, which
[10:44] before the prescription goes out, which
[10:44] before the prescription goes out, which is kind of crazy. Nola Health said
[10:46] is kind of crazy. Nola Health said
[10:46] is kind of crazy. Nola Health said Monday, "It's Nola Derm app can issue
[10:49] Monday, "It's Nola Derm app can issue
[10:49] Monday, "It's Nola Derm app can issue topical acne prescriptions to Utah
[10:52] topical acne prescriptions to Utah
[10:52] topical acne prescriptions to Utah adults under an agreement with the
[10:54] adults under an agreement with the
[10:54] adults under an agreement with the state's office of artificial
[10:56] state's office of artificial
[10:56] state's office of artificial intelligence policy at a pilot price of
[10:59] intelligence policy at a pilot price of
[10:59] intelligence policy at a pilot price of $4.99 a month." Wow.
[11:02] $4.99 a month." Wow.
[11:02] $4.99 a month." Wow. Two physicians approve every
[11:04] Two physicians approve every
[11:04] Two physicians approve every prescription for at least the first 100
[11:06] prescription for at least the first 100
[11:06] prescription for at least the first 100 patients. Then the AI prescribes
[11:08] patients. Then the AI prescribes
[11:08] patients. Then the AI prescribes directly with weekly doctor review up to
[11:12] directly with weekly doctor review up to
[11:12] directly with weekly doctor review up to 500 patients. After that, doctors audit
[11:15] 500 patients. After that, doctors audit
[11:15] 500 patients. After that, doctors audit at least 10% each month. Wow. No pills,
[11:20] at least 10% each month. Wow. No pills,
[11:20] at least 10% each month. Wow. No pills, no uh uh no extra review, no severe
[11:24] no uh uh no extra review, no severe
[11:24] no uh uh no extra review, no severe acne. founder uh Luis Wenus uh until now
[11:29] acne. founder uh Luis Wenus uh until now
[11:29] acne. founder uh Luis Wenus uh until now says until now a licensed clinician
[11:32] says until now a licensed clinician
[11:32] says until now a licensed clinician signed every single prescription before
[11:34] signed every single prescription before
[11:34] signed every single prescription before it went out. Our AI can now make that
[11:36] it went out. Our AI can now make that
[11:36] it went out. Our AI can now make that prescribing decision itself.
[11:40] prescribing decision itself.
[11:40] prescribing decision itself. Wow, this is fascinating. Uh currently
[11:43] Wow, this is fascinating. Uh currently
[11:43] Wow, this is fascinating. Uh currently this is this is a template, right?
[11:45] this is this is a template, right?
[11:45] this is this is a template, right? Nola's medical advisor already names the
[11:48] Nola's medical advisor already names the
[11:48] Nola's medical advisor already names the flu and uncomplicated urinary tract
[11:51] flu and uncomplicated urinary tract
[11:51] flu and uncomplicated urinary tract infections as possible next steps. Nola
[11:55] infections as possible next steps. Nola
[11:56] infections as possible next steps. Nola says the AI chooses among preapproved
[11:59] says the AI chooses among preapproved
[11:59] says the AI chooses among preapproved treatment protocols rather than making
[12:01] treatment protocols rather than making
[12:01] treatment protocols rather than making free form decisions. The staged doctor
[12:04] free form decisions. The staged doctor
[12:04] free form decisions. The staged doctor oversight is spelled out in Nola's
[12:06] oversight is spelled out in Nola's
[12:06] oversight is spelled out in Nola's announcement and Silicon Angle's report.
[12:09] announcement and Silicon Angle's report.
[12:09] announcement and Silicon Angle's report. The waiver runs for one year. Okay, so
[12:12] The waiver runs for one year. Okay, so
[12:12] The waiver runs for one year. Okay, so today it's acne cream. The real question
[12:15] today it's acne cream. The real question
[12:15] today it's acne cream. The real question is who signs off when the list of
[12:17] is who signs off when the list of
[12:17] is who signs off when the list of conditions grows. Um, let's get into it.
[12:21] conditions grows. Um, let's get into it.
[12:21] conditions grows. Um, let's get into it. This is very interesting. Um, I love to
[12:23] This is very interesting. Um, I love to
[12:24] This is very interesting. Um, I love to see uh AI and this technology being used
[12:27] see uh AI and this technology being used
[12:27] see uh AI and this technology being used in a new use case uh a new field. We
[12:30] in a new use case uh a new field. We
[12:30] in a new use case uh a new field. We haven't seen um uh a lot used um in in
[12:36] haven't seen um uh a lot used um in in
[12:36] haven't seen um uh a lot used um in in direct clinical practice yet. So, this
[12:38] direct clinical practice yet. So, this
[12:38] direct clinical practice yet. So, this is really interesting and exciting. Um,
[12:41] is really interesting and exciting. Um,
[12:41] is really interesting and exciting. Um, and a really great use case where I
[12:44] and a really great use case where I
[12:44] and a really great use case where I imagine um the majority of these
[12:47] imagine um the majority of these
[12:47] imagine um the majority of these prescriptions are refills and folks just
[12:49] prescriptions are refills and folks just
[12:49] prescriptions are refills and folks just coming in uh to say, "Hey, hey doc, I
[12:52] coming in uh to say, "Hey, hey doc, I
[12:52] coming in uh to say, "Hey, hey doc, I you know, I need a refill on my acne
[12:54] you know, I need a refill on my acne
[12:54] you know, I need a refill on my acne prescription. uh let's get that filled
[12:57] prescription. uh let's get that filled
[12:57] prescription. uh let's get that filled and and it takes the doctor's time um
[13:00] and and it takes the doctor's time um
[13:00] and and it takes the doctor's time um and uh in and effort to review these
[13:03] and uh in and effort to review these
[13:03] and uh in and effort to review these whereas it it's probably pretty standard
[13:06] whereas it it's probably pretty standard
[13:06] whereas it it's probably pretty standard case by case. Uh really interesting to
[13:09] case by case. Uh really interesting to
[13:09] case by case. Uh really interesting to see the escalation you know the doctors
[13:12] see the escalation you know the doctors
[13:12] see the escalation you know the doctors involved in the first 100 patients uh
[13:15] involved in the first 100 patients uh
[13:15] involved in the first 100 patients uh and then it scales to uh you know 500
[13:18] and then it scales to uh you know 500
[13:18] and then it scales to uh you know 500 and then checking
[13:21] and then checking
[13:21] and then checking you know a certain percentage beyond
[13:22] you know a certain percentage beyond
[13:22] you know a certain percentage beyond that. So it's interesting to see the
[13:24] that. So it's interesting to see the
[13:24] that. So it's interesting to see the scale where uh we have human in the loop
[13:27] scale where uh we have human in the loop
[13:27] scale where uh we have human in the loop and then gradually move the doctor out
[13:31] and then gradually move the doctor out
[13:31] and then gradually move the doctor out uh where AI is predominantly uh taking
[13:33] uh where AI is predominantly uh taking
[13:34] uh where AI is predominantly uh taking over and making the decisions and really
[13:36] over and making the decisions and really
[13:36] over and making the decisions and really interesting to see how efficient this
[13:38] interesting to see how efficient this
[13:38] interesting to see how efficient this will make doctors and hopefully free
[13:40] will make doctors and hopefully free
[13:40] will make doctors and hopefully free these doctors up to spend more time on
[13:43] these doctors up to spend more time on
[13:43] these doctors up to spend more time on more serious conditions and provide a
[13:46] more serious conditions and provide a
[13:46] more serious conditions and provide a higher level of care to uh other
[13:49] higher level of care to uh other
[13:50] higher level of care to uh other patients. Um I think probably the
[13:52] patients. Um I think probably the
[13:52] patients. Um I think probably the concern here maybe the the obvious uh
[13:55] concern here maybe the the obvious uh
[13:55] concern here maybe the the obvious uh thing is um does this increase the rate
[13:59] thing is um does this increase the rate
[13:59] thing is um does this increase the rate of prescriptions being written um if you
[14:02] of prescriptions being written um if you
[14:02] of prescriptions being written um if you adopt this to uh to additional
[14:05] adopt this to uh to additional
[14:06] adopt this to uh to additional modalities uh from a um you know a
[14:10] modalities uh from a um you know a
[14:10] modalities uh from a um you know a prescribers perspective does that get
[14:13] prescribers perspective does that get
[14:13] prescribers perspective does that get really dangerous when we've had issues
[14:15] really dangerous when we've had issues
[14:15] really dangerous when we've had issues with prescription drugs in this country
[14:17] with prescription drugs in this country
[14:18] with prescription drugs in this country historically now do we have AI just uh
[14:22] historically now do we have AI just uh
[14:22] historically now do we have AI just uh kind of freewheeling and writing
[14:25] kind of freewheeling and writing
[14:25] kind of freewheeling and writing prescriptions that it shouldn't be. It
[14:27] prescriptions that it shouldn't be. It
[14:27] prescriptions that it shouldn't be. It seems like they've got the guardrails in
[14:28] seems like they've got the guardrails in
[14:28] seems like they've got the guardrails in place. They've got the oversight and
[14:31] place. They've got the oversight and
[14:31] place. They've got the oversight and will be reviewing these cases. But
[14:33] will be reviewing these cases. But
[14:33] will be reviewing these cases. But you've got to imagine that this
[14:34] you've got to imagine that this
[14:34] you've got to imagine that this incentivizes
[14:36] incentivizes
[14:36] incentivizes uh doctors that, you know, maybe they do
[14:39] uh doctors that, you know, maybe they do
[14:40] uh doctors that, you know, maybe they do have a financial incentive for writing
[14:41] have a financial incentive for writing
[14:41] have a financial incentive for writing some of these prescriptions. And now
[14:43] some of these prescriptions. And now
[14:43] some of these prescriptions. And now they just went from having to manually
[14:46] they just went from having to manually
[14:46] they just went from having to manually review and write uh you know 100, 500,
[14:50] review and write uh you know 100, 500,
[14:50] review and write uh you know 100, 500, 1,000 prescriptions and now they can
[14:53] 1,000 prescriptions and now they can
[14:53] 1,000 prescriptions and now they can have AI do that and um uh you know
[14:57] have AI do that and um uh you know
[14:57] have AI do that and um uh you know presumably benefit without having to uh
[15:02] presumably benefit without having to uh
[15:02] presumably benefit without having to uh you know do the linear amount of work.
[15:05] you know do the linear amount of work.
[15:05] you know do the linear amount of work. So uh interesting to see this uh
[15:08] So uh interesting to see this uh
[15:08] So uh interesting to see this uh interesting to see the use case. Um, I
[15:11] interesting to see the use case. Um, I
[15:11] interesting to see the use case. Um, I think this all in all will have a net
[15:13] think this all in all will have a net
[15:13] think this all in all will have a net positive impact on the medical community
[15:16] positive impact on the medical community
[15:16] positive impact on the medical community and we hope to see more advancements
[15:19] and we hope to see more advancements
[15:19] and we hope to see more advancements like this and and shout out to Utah. I
[15:21] like this and and shout out to Utah. I
[15:21] like this and and shout out to Utah. I mean, what a really cool um uh
[15:26] mean, what a really cool um uh
[15:26] mean, what a really cool um uh cool that they have an AI policy
[15:28] cool that they have an AI policy
[15:28] cool that they have an AI policy department. Uh, congrats to Utah there
[15:31] department. Uh, congrats to Utah there
[15:31] department. Uh, congrats to Utah there and congrats to the team at NOLA. Uh, as
[15:36] and congrats to the team at NOLA. Uh, as
[15:36] and congrats to the team at NOLA. Uh, as we move on to the next story, two former
[15:39] we move on to the next story, two former
[15:39] we move on to the next story, two former Grock GRQ engineers are suing Grock's
[15:43] Grock GRQ engineers are suing Grock's
[15:43] Grock GRQ engineers are suing Grock's board, saying Nvidia's 20 billion dollar
[15:46] board, saying Nvidia's 20 billion dollar
[15:46] board, saying Nvidia's 20 billion dollar deal for its tech and talent
[15:49] deal for its tech and talent
[15:49] deal for its tech and talent shortchanged ordinary shareholders.
[15:54] Benjamin Sarah Brin and Joshua Rubin
[15:57] Benjamin Sarah Brin and Joshua Rubin filed suit Monday in Delaware's Court of
[15:59] filed suit Monday in Delaware's Court of
[15:59] filed suit Monday in Delaware's Court of Chancery against Grock's board, naming
[16:02] Chancery against Grock's board, naming
[16:02] Chancery against Grock's board, naming founder Jonathan Ross and President New
[16:05] founder Jonathan Ross and President New
[16:05] founder Jonathan Ross and President New Money Sunny Madra. In December 2025,
[16:09] Money Sunny Madra. In December 2025,
[16:09] Money Sunny Madra. In December 2025, Nvidia paid about $17 billion in
[16:11] Nvidia paid about $17 billion in
[16:11] Nvidia paid about $17 billion in licensing fees, plus roughly $3 billion
[16:15] licensing fees, plus roughly $3 billion
[16:15] licensing fees, plus roughly $3 billion in stock bonuses to bring about 200
[16:19] in stock bonuses to bring about 200
[16:19] in stock bonuses to bring about 200 Grock engineers over uh about $20
[16:23] Grock engineers over uh about $20
[16:23] Grock engineers over uh about $20 billion in all in a deal called uh a
[16:26] billion in all in a deal called uh a
[16:26] billion in all in a deal called uh a non-exclusive.
[16:28] non-exclusive.
[16:28] non-exclusive. The suit says the board skipped the
[16:31] The suit says the board skipped the
[16:31] The suit says the board skipped the stockholder vote uh that Delaware law
[16:34] stockholder vote uh that Delaware law
[16:34] stockholder vote uh that Delaware law requires and left Grock a hollowedout
[16:37] requires and left Grock a hollowedout
[16:37] requires and left Grock a hollowedout shell.
[16:40] shell.
[16:40] shell. What happened here? This uh is a quote
[16:43] What happened here? This uh is a quote
[16:43] What happened here? This uh is a quote unquote license and hire move that big
[16:46] unquote license and hire move that big
[16:46] unquote license and hire move that big tech um uses to buy AI startups without
[16:50] tech um uses to buy AI startups without
[16:50] tech um uses to buy AI startups without technically buying them. uh if a court
[16:53] technically buying them. uh if a court
[16:53] technically buying them. uh if a court says it needs a shareholder vote, the
[16:55] says it needs a shareholder vote, the
[16:55] says it needs a shareholder vote, the playbook gets much much more difficult,
[16:58] playbook gets much much more difficult,
[16:58] playbook gets much much more difficult, right? So, the Justice Department is
[17:00] right? So, the Justice Department is
[17:00] right? So, the Justice Department is already looking at this deal. The New
[17:02] already looking at this deal. The New
[17:02] already looking at this deal. The New York Times uh reported this back in
[17:04] York Times uh reported this back in
[17:04] York Times uh reported this back in September. The plaintiffs say board
[17:06] September. The plaintiffs say board
[17:06] September. The plaintiffs say board conflicts left common stockholders
[17:09] conflicts left common stockholders
[17:09] conflicts left common stockholders billions of dollars worse off. The
[17:12] billions of dollars worse off. The
[17:12] billions of dollars worse off. The complaint as reported by the Financial
[17:13] complaint as reported by the Financial
[17:13] complaint as reported by the Financial Times and Law.com's Delaware Business
[17:16] Times and Law.com's Delaware Business
[17:16] Times and Law.com's Delaware Business Court. These are allegations. Grock and
[17:19] Court. These are allegations. Grock and
[17:19] Court. These are allegations. Grock and Nvidia hadn't responded in the reports
[17:22] Nvidia hadn't responded in the reports
[17:22] Nvidia hadn't responded in the reports and both plaintiffs left Grock before
[17:24] and both plaintiffs left Grock before
[17:24] and both plaintiffs left Grock before the deal. Uh but they kept their stock.
[17:29] the deal. Uh but they kept their stock.
[17:29] the deal. Uh but they kept their stock. Let's get into this one. Okay, this is
[17:31] Let's get into this one. Okay, this is
[17:31] Let's get into this one. Okay, this is interesting. Um sorry to the fellas that
[17:34] interesting. Um sorry to the fellas that
[17:34] interesting. Um sorry to the fellas that quit Grock before the aqua hire
[17:36] quit Grock before the aqua hire
[17:36] quit Grock before the aqua hire occurred, but it looks like they might
[17:38] occurred, but it looks like they might
[17:38] occurred, but it looks like they might be getting the short end of the stick
[17:40] be getting the short end of the stick
[17:40] be getting the short end of the stick here and maybe a good reason uh to to be
[17:44] here and maybe a good reason uh to to be
[17:44] here and maybe a good reason uh to to be loyal and stick around. I don't know. Um
[17:47] loyal and stick around. I don't know. Um
[17:47] loyal and stick around. I don't know. Um you know unfortunately or maybe
[17:49] you know unfortunately or maybe
[17:49] you know unfortunately or maybe fortunately we see have seen these more
[17:52] fortunately we see have seen these more
[17:52] fortunately we see have seen these more recently. This happened at Meta. This
[17:54] recently. This happened at Meta. This
[17:54] recently. This happened at Meta. This would be called an aqua hire where uh a
[17:58] would be called an aqua hire where uh a
[17:58] would be called an aqua hire where uh a larger business goes in and instead of
[18:01] larger business goes in and instead of
[18:01] larger business goes in and instead of buying the a company and you know doing
[18:05] buying the a company and you know doing
[18:05] buying the a company and you know doing the general and and typical mergers and
[18:07] the general and and typical mergers and
[18:07] the general and and typical mergers and acquisitions process, they'll just say
[18:10] acquisitions process, they'll just say
[18:10] acquisitions process, they'll just say hey let let me hire the 200 people that
[18:12] hey let let me hire the 200 people that
[18:12] hey let let me hire the 200 people that matter at this company. bring them over.
[18:14] matter at this company. bring them over.
[18:14] matter at this company. bring them over. And in this case at Grock, they uh
[18:18] And in this case at Grock, they uh
[18:18] And in this case at Grock, they uh licensed the Grock technology for $20
[18:23] licensed the Grock technology for $20
[18:23] licensed the Grock technology for $20 billion. It's exclusive license, I
[18:25] billion. It's exclusive license, I
[18:25] billion. It's exclusive license, I believe. I'm sure has a a really uh
[18:27] believe. I'm sure has a a really uh
[18:27] believe. I'm sure has a a really uh longdated expiration on it. So that
[18:30] longdated expiration on it. So that
[18:30] longdated expiration on it. So that essentially in in effect they own it,
[18:32] essentially in in effect they own it,
[18:32] essentially in in effect they own it, but they don't have to buy out the
[18:35] but they don't have to buy out the
[18:35] but they don't have to buy out the common shareholders. And this is, you
[18:37] common shareholders. And this is, you
[18:37] common shareholders. And this is, you know, kind of a bummer, but it's kind of
[18:39] know, kind of a bummer, but it's kind of
[18:39] know, kind of a bummer, but it's kind of the way the world works as well, too. is
[18:41] the way the world works as well, too. is
[18:42] the way the world works as well, too. is like, you know, first money in, first
[18:43] like, you know, first money in, first
[18:43] like, you know, first money in, first money out. So, the the investors, the
[18:45] money out. So, the the investors, the
[18:45] money out. So, the the investors, the the founders, uh, of Grock, um, they
[18:50] the founders, uh, of Grock, um, they
[18:50] the founders, uh, of Grock, um, they have preferred shares, and I'm sure they
[18:52] have preferred shares, and I'm sure they
[18:52] have preferred shares, and I'm sure they got paid out on this deal, and, uh, and
[18:54] got paid out on this deal, and, uh, and
[18:54] got paid out on this deal, and, uh, and I believe it was a a great deal for
[18:57] I believe it was a a great deal for
[18:57] I believe it was a a great deal for these folks, including, you know,
[18:58] these folks, including, you know,
[18:58] these folks, including, you know, Chimath Poly Hapatia of the All-In
[19:01] Chimath Poly Hapatia of the All-In
[19:01] Chimath Poly Hapatia of the All-In podcast, uh, and others. But when you
[19:04] podcast, uh, and others. But when you
[19:04] podcast, uh, and others. But when you talk about common employees that held
[19:06] talk about common employees that held
[19:06] talk about common employees that held some shares, of course, I don't know the
[19:07] some shares, of course, I don't know the
[19:08] some shares, of course, I don't know the details. You know, how much how much
[19:09] details. You know, how much how much
[19:09] details. You know, how much how much stock did these fellas own? Um how long
[19:12] stock did these fellas own? Um how long
[19:12] stock did these fellas own? Um how long were they at Grock? Were they there for
[19:14] were they at Grock? Were they there for
[19:14] were they at Grock? Were they there for 3 months? And uh and then, you know,
[19:17] 3 months? And uh and then, you know,
[19:17] 3 months? And uh and then, you know, we're hoping that their deal vested. I
[19:19] we're hoping that their deal vested. I
[19:19] we're hoping that their deal vested. I don't know. Um,
[19:21] don't know. Um,
[19:21] don't know. Um, this is uh unreported yet, but um, you
[19:26] this is uh unreported yet, but um, you
[19:26] this is uh unreported yet, but um, you know, it seems like maybe they missed
[19:28] know, it seems like maybe they missed
[19:28] know, it seems like maybe they missed the boat and they see a 20 billion
[19:31] the boat and they see a 20 billion
[19:31] the boat and they see a 20 billion payday and they say, "Hey, let's see if
[19:33] payday and they say, "Hey, let's see if
[19:33] payday and they say, "Hey, let's see if we can go get a piece of this."
[19:35] we can go get a piece of this."
[19:35] we can go get a piece of this." Interesting to note, um, this is in the
[19:37] Interesting to note, um, this is in the
[19:37] Interesting to note, um, this is in the state of Delaware where um, in past
[19:40] state of Delaware where um, in past
[19:40] state of Delaware where um, in past years there's been conflict with Elon
[19:42] years there's been conflict with Elon
[19:42] years there's been conflict with Elon Musk and Tesla and Tesla shareholders
[19:45] Musk and Tesla and Tesla shareholders
[19:45] Musk and Tesla and Tesla shareholders and Elon Musk's pay package. And it, you
[19:48] and Elon Musk's pay package. And it, you
[19:48] and Elon Musk's pay package. And it, you know, obviously when you get really big
[19:50] know, obviously when you get really big
[19:50] know, obviously when you get really big numbers with lots of zeros and a handful
[19:52] numbers with lots of zeros and a handful
[19:52] numbers with lots of zeros and a handful of commas, um, you you get a little bit,
[19:57] of commas, um, you you get a little bit,
[19:57] of commas, um, you you get a little bit, um,
[19:58] um,
[19:58] um, opportunistic, I think. And so, uh,
[20:01] opportunistic, I think. And so, uh,
[20:01] opportunistic, I think. And so, uh, can't blame these guys for trying, I
[20:03] can't blame these guys for trying, I
[20:03] can't blame these guys for trying, I guess. And, and you got to expect this
[20:04] guess. And, and you got to expect this
[20:04] guess. And, and you got to expect this sort of thing and, um, you know,
[20:07] sort of thing and, um, you know,
[20:07] sort of thing and, um, you know, corporate mergers and acquisitions or
[20:09] corporate mergers and acquisitions or
[20:09] corporate mergers and acquisitions or aqua hires and that that there is going
[20:11] aqua hires and that that there is going
[20:11] aqua hires and that that there is going to be some litigation here. So, we'll
[20:13] to be some litigation here. So, we'll
[20:13] to be some litigation here. So, we'll see where this story goes. Wish best of
[20:16] see where this story goes. Wish best of
[20:16] see where this story goes. Wish best of luck to those guys. uh wish a little bit
[20:18] luck to those guys. uh wish a little bit
[20:18] luck to those guys. uh wish a little bit more luck to Brock and the team at
[20:20] more luck to Brock and the team at
[20:20] more luck to Brock and the team at Nvidia because they are the ones pushing
[20:22] Nvidia because they are the ones pushing
[20:22] Nvidia because they are the ones pushing technology forward instead of uh trying
[20:25] technology forward instead of uh trying
[20:25] technology forward instead of uh trying to distract these folks from uh getting
[20:28] to distract these folks from uh getting
[20:28] to distract these folks from uh getting their work done. Uh here we go. This
[20:32] their work done. Uh here we go. This
[20:32] their work done. Uh here we go. This one's great. Chip startup Etched is
[20:34] one's great. Chip startup Etched is
[20:34] one's great. Chip startup Etched is reportedly fielding offers that value it
[20:36] reportedly fielding offers that value it
[20:36] reportedly fielding offers that value it at $4 to $50 billion.
[20:40] at $4 to $50 billion.
[20:40] at $4 to $50 billion. Uh that's about double the $21 billion
[20:44] Uh that's about double the $21 billion
[20:44] Uh that's about double the $21 billion that it was worth just one month ago.
[20:47] that it was worth just one month ago.
[20:47] that it was worth just one month ago. Techrunch reported Monday citing sources
[20:49] Techrunch reported Monday citing sources
[20:49] Techrunch reported Monday citing sources that established investors
[20:52] that established investors
[20:52] that established investors offers value etched at about $40 billion
[20:57] offers value etched at about $40 billion
[20:57] offers value etched at about $40 billion and lesserk known uh offers have come in
[21:00] and lesserk known uh offers have come in
[21:00] and lesserk known uh offers have come in valuing it at about $50 billion. Etched
[21:03] valuing it at about $50 billion. Etched
[21:03] valuing it at about $50 billion. Etched was valued at $10.3 billion in July
[21:07] was valued at $10.3 billion in July
[21:07] was valued at $10.3 billion in July after a $300 million round led by Seoia
[21:12] after a $300 million round led by Seoia
[21:12] after a $300 million round led by Seoia and at $21 billion in September when
[21:15] and at $21 billion in September when
[21:15] and at $21 billion in September when Jane Street led $700 million.
[21:20] Jane Street led $700 million.
[21:20] Jane Street led $700 million. My goodness, this is great to see.
[21:22] My goodness, this is great to see.
[21:22] My goodness, this is great to see. Nearly fivefold in 3 months shows how
[21:26] Nearly fivefold in 3 months shows how
[21:26] Nearly fivefold in 3 months shows how hard money is chasing alternatives to
[21:29] hard money is chasing alternatives to
[21:29] hard money is chasing alternatives to Nvidia for running AI. Uh or is it the
[21:33] Nvidia for running AI. Uh or is it the
[21:33] Nvidia for running AI. Uh or is it the fact that uh these folks were on with
[21:35] fact that uh these folks were on with
[21:35] fact that uh these folks were on with Patrick Oshanosy on Invest Like the Best
[21:38] Patrick Oshanosy on Invest Like the Best
[21:38] Patrick Oshanosy on Invest Like the Best and uh and and now they're just getting
[21:41] and uh and and now they're just getting
[21:41] and uh and and now they're just getting a skyrocket just going vertical from uh
[21:44] a skyrocket just going vertical from uh
[21:44] a skyrocket just going vertical from uh that audience. Who knows? Uh Etch says
[21:47] that audience. Who knows? Uh Etch says
[21:47] that audience. Who knows? Uh Etch says its chips process tokens faster and
[21:50] its chips process tokens faster and
[21:50] its chips process tokens faster and cheaper than Nvidia. Really cool story
[21:53] cheaper than Nvidia. Really cool story
[21:53] cheaper than Nvidia. Really cool story by the way. Look up the Edge story. Go
[21:54] by the way. Look up the Edge story. Go
[21:54] by the way. Look up the Edge story. Go listen to that podcast on Invest Like
[21:57] listen to that podcast on Invest Like
[21:57] listen to that podcast on Invest Like the Best with uh Patrick. Uh really cool
[22:00] the Best with uh Patrick. Uh really cool
[22:00] the Best with uh Patrick. Uh really cool to see what these guys have done. Young
[22:03] to see what these guys have done. Young
[22:03] to see what these guys have done. Young guys, super smart and have gone to
[22:05] guys, super smart and have gone to
[22:05] guys, super smart and have gone to hustle. Um uh the report said in July
[22:09] hustle. Um uh the report said in July
[22:09] hustle. Um uh the report said in July that it had um a one $1 billion in
[22:13] that it had um a one $1 billion in
[22:13] that it had um a one $1 billion in customer orders uh at Etched and Jane
[22:16] customer orders uh at Etched and Jane
[22:16] customer orders uh at Etched and Jane Street is both an investor and a
[22:18] Street is both an investor and a
[22:18] Street is both an investor and a customer. Also really cool what Jane
[22:20] customer. Also really cool what Jane
[22:20] customer. Also really cool what Jane Street is doing. No round has closed and
[22:23] Street is doing. No round has closed and
[22:23] Street is doing. No round has closed and the report doesn't say who's bidding or
[22:26] the report doesn't say who's bidding or
[22:26] the report doesn't say who's bidding or whether the money goes to the company
[22:29] whether the money goes to the company
[22:29] whether the money goes to the company directly or what that process is. This
[22:32] directly or what that process is. This
[22:32] directly or what that process is. This is really interesting. Um
[22:36] is really interesting. Um
[22:36] is really interesting. Um just on a number of different planes.
[22:39] just on a number of different planes.
[22:40] just on a number of different planes. Super exciting and huge congrats to the
[22:43] Super exciting and huge congrats to the
[22:43] Super exciting and huge congrats to the team at Etch for what they've
[22:45] team at Etch for what they've
[22:45] team at Etch for what they've accomplished. Um, and just to see the
[22:49] accomplished. Um, and just to see the
[22:49] accomplished. Um, and just to see the answer in the marketplace, what their
[22:51] answer in the marketplace, what their
[22:51] answer in the marketplace, what their technology is enabling and what they
[22:53] technology is enabling and what they
[22:53] technology is enabling and what they focused on, uh, and have been working on
[22:56] focused on, uh, and have been working on
[22:56] focused on, uh, and have been working on for several years now, uh, is is really
[22:59] for several years now, uh, is is really
[22:59] for several years now, uh, is is really paying off and just, uh, you know,
[23:02] paying off and just, uh, you know,
[23:02] paying off and just, uh, you know, really, um, a a very cool outcome. And
[23:05] really, um, a a very cool outcome. And
[23:05] really, um, a a very cool outcome. And to see this happen this fast, to go
[23:07] to see this happen this fast, to go
[23:08] to see this happen this fast, to go from, you know, $1 billion in orders uh
[23:10] from, you know, $1 billion in orders uh
[23:10] from, you know, $1 billion in orders uh at the beginning of the summer to 10
[23:13] at the beginning of the summer to 10
[23:13] at the beginning of the summer to 10 billion, 20 billion, 40, and $50 billion
[23:16] billion, 20 billion, 40, and $50 billion
[23:16] billion, 20 billion, 40, and $50 billion valuations is is phenomenal. So really
[23:19] valuations is is phenomenal. So really
[23:19] valuations is is phenomenal. So really exciting. Why does this matter? Why is
[23:20] exciting. Why does this matter? Why is
[23:20] exciting. Why does this matter? Why is this important? Um to put it simply, uh
[23:23] this important? Um to put it simply, uh
[23:23] this important? Um to put it simply, uh Etched has um a unique process of
[23:27] Etched has um a unique process of
[23:27] Etched has um a unique process of building chips that our AI runs on uh
[23:31] building chips that our AI runs on uh
[23:31] building chips that our AI runs on uh that is more efficient and faster than
[23:34] that is more efficient and faster than
[23:34] that is more efficient and faster than traditional Nvidia chips. Nvidia, as we
[23:37] traditional Nvidia chips. Nvidia, as we
[23:37] traditional Nvidia chips. Nvidia, as we know, is uh the
[23:41] know, is uh the
[23:41] know, is uh the um
[23:43] um
[23:43] um they're the they're the big boss in the
[23:44] they're the they're the big boss in the
[23:44] they're the they're the big boss in the space, right? um phenomenal company,
[23:47] space, right? um phenomenal company,
[23:47] space, right? um phenomenal company, have done a wonderful job, have been
[23:49] have done a wonderful job, have been
[23:49] have done a wonderful job, have been around for 30 plus years, uh and have
[23:52] around for 30 plus years, uh and have
[23:52] around for 30 plus years, uh and have led the uh AI wave and and opened up a
[23:56] led the uh AI wave and and opened up a
[23:56] led the uh AI wave and and opened up a lot of capabilities through their
[23:58] lot of capabilities through their
[23:58] lot of capabilities through their different chip platforms and and and
[24:00] different chip platforms and and and
[24:00] different chip platforms and and and software models that enable um AI
[24:03] software models that enable um AI
[24:03] software models that enable um AI companies to build these advanced
[24:05] companies to build these advanced
[24:05] companies to build these advanced models. because of their success um we
[24:09] models. because of their success um we
[24:09] models. because of their success um we we see competitors arising to try to
[24:12] we see competitors arising to try to
[24:12] we see competitors arising to try to figure out how to do a better job than
[24:14] figure out how to do a better job than
[24:14] figure out how to do a better job than Nvidia and seeing the opportunity
[24:17] Nvidia and seeing the opportunity
[24:17] Nvidia and seeing the opportunity therein and
[24:19] therein and
[24:19] therein and case in point etched. So these guys um
[24:22] case in point etched. So these guys um
[24:22] case in point etched. So these guys um obviously their technology is uh
[24:26] obviously their technology is uh
[24:26] obviously their technology is uh something that is really impactful and
[24:29] something that is really impactful and
[24:29] something that is really impactful and doing some amazing things for its
[24:31] doing some amazing things for its
[24:31] doing some amazing things for its customers. Jane Street is a
[24:35] customers. Jane Street is a
[24:36] customers. Jane Street is a a a very interesting company. Um very
[24:39] a a very interesting company. Um very
[24:39] a a very interesting company. Um very very smart people work there and they
[24:42] very smart people work there and they
[24:42] very smart people work there and they are using uh etched etched. Wow, that's
[24:46] are using uh etched etched. Wow, that's
[24:46] are using uh etched etched. Wow, that's difficult to say. They're using the
[24:47] difficult to say. They're using the
[24:47] difficult to say. They're using the chips that Etch created uh to um build
[24:52] chips that Etch created uh to um build
[24:52] chips that Etch created uh to um build their own AI on top of to use that uh in
[24:57] their own AI on top of to use that uh in
[24:57] their own AI on top of to use that uh in trading in markets um as they are a
[25:00] trading in markets um as they are a
[25:00] trading in markets um as they are a quant firm and this is a big piece of
[25:02] quant firm and this is a big piece of
[25:02] quant firm and this is a big piece of their business. So just really cool to
[25:04] their business. So just really cool to
[25:04] their business. So just really cool to see this story, really cool to see um uh
[25:08] see this story, really cool to see um uh
[25:08] see this story, really cool to see um uh the success here and awesome to see
[25:10] the success here and awesome to see
[25:10] the success here and awesome to see these guys pushing the envelope forward.
[25:12] these guys pushing the envelope forward.
[25:12] these guys pushing the envelope forward. The question is, you know, to piggyback
[25:14] The question is, you know, to piggyback
[25:14] The question is, you know, to piggyback on our our uh previous story about Grock
[25:18] on our our uh previous story about Grock
[25:18] on our our uh previous story about Grock and that acquisition there, obviously
[25:21] and that acquisition there, obviously
[25:21] and that acquisition there, obviously Nvidia is willing to spend $20 billion
[25:23] Nvidia is willing to spend $20 billion
[25:23] Nvidia is willing to spend $20 billion on a chip company. Uh are they looking
[25:27] on a chip company. Uh are they looking
[25:27] on a chip company. Uh are they looking at a $50 billion acquisition of Etched?
[25:31] at a $50 billion acquisition of Etched?
[25:31] at a $50 billion acquisition of Etched? Um would that be a natural acquisition
[25:34] Um would that be a natural acquisition
[25:34] Um would that be a natural acquisition play for them? Would Etched sell to
[25:37] play for them? Would Etched sell to
[25:37] play for them? Would Etched sell to them? um or do they want to keep all the
[25:41] them? um or do they want to keep all the
[25:41] them? um or do they want to keep all the chips on their end of the table and keep
[25:43] chips on their end of the table and keep
[25:43] chips on their end of the table and keep stacking and see where they go from
[25:45] stacking and see where they go from
[25:45] stacking and see where they go from there? $50 billion is a pretty
[25:47] there? $50 billion is a pretty
[25:47] there? $50 billion is a pretty phenomenal uh valuation. Of course, we
[25:50] phenomenal uh valuation. Of course, we
[25:50] phenomenal uh valuation. Of course, we did see the cursor acquisition for $60
[25:52] did see the cursor acquisition for $60
[25:52] did see the cursor acquisition for $60 billion earlier this year. Uh so
[25:56] billion earlier this year. Uh so
[25:56] billion earlier this year. Uh so there's, you know, that's not off the
[25:58] there's, you know, that's not off the
[25:58] there's, you know, that's not off the table for Nvidia, I would say. Uh but
[26:01] table for Nvidia, I would say. Uh but
[26:01] table for Nvidia, I would say. Uh but it's interesting to see where it goes
[26:03] it's interesting to see where it goes
[26:03] it's interesting to see where it goes from here. will continue to track this
[26:05] from here. will continue to track this
[26:05] from here. will continue to track this story and report on uh any interesting
[26:10] story and report on uh any interesting
[26:10] story and report on uh any interesting outcomes as they develop.
[26:13] outcomes as they develop.
[26:13] outcomes as they develop. Reflection AI unveiled unveiled bang
[26:16] Reflection AI unveiled unveiled bang
[26:16] Reflection AI unveiled unveiled bang beam. Wow. Okay. A 501 billion parameter
[26:20] beam. Wow. Okay. A 501 billion parameter
[26:20] beam. Wow. Okay. A 501 billion parameter open model that it says matches top
[26:23] open model that it says matches top
[26:23] open model that it says matches top Chinese open models on a fraction of the
[26:27] Chinese open models on a fraction of the
[26:27] Chinese open models on a fraction of the hardware. Beam is a mixture of experts
[26:30] hardware. Beam is a mixture of experts
[26:30] hardware. Beam is a mixture of experts model uh 501 billion parameters in total
[26:34] model uh 501 billion parameters in total
[26:34] model uh 501 billion parameters in total 23 billion active per token. Wow.
[26:38] 23 billion active per token. Wow.
[26:38] 23 billion active per token. Wow. Trained on 23.8 trillion tokens.
[26:41] Trained on 23.8 trillion tokens.
[26:41] Trained on 23.8 trillion tokens. Reflection says it beats GLM 5.2
[26:45] Reflection says it beats GLM 5.2
[26:45] Reflection says it beats GLM 5.2 on some tasks using a third to a quarter
[26:49] on some tasks using a third to a quarter
[26:49] on some tasks using a third to a quarter of the hardware and approaches Quinn 3.8
[26:52] of the hardware and approaches Quinn 3.8
[26:52] of the hardware and approaches Quinn 3.8 8 Max while still trailing Frontier
[26:54] 8 Max while still trailing Frontier
[26:54] 8 Max while still trailing Frontier models like Anthropics Cloud Fable 5.1.
[26:58] models like Anthropics Cloud Fable 5.1.
[26:58] models like Anthropics Cloud Fable 5.1. Uh early access open Monday. The weights
[27:01] Uh early access open Monday. The weights
[27:01] Uh early access open Monday. The weights come later in October under the Apache
[27:04] come later in October under the Apache
[27:04] come later in October under the Apache 2.0 license uh after red teaming.
[27:07] 2.0 license uh after red teaming.
[27:07] 2.0 license uh after red teaming. Reflection was recently valued at $25
[27:11] Reflection was recently valued at $25
[27:11] Reflection was recently valued at $25 billion.
[27:13] billion.
[27:13] billion. This is phenomenal. What an exciting
[27:15] This is phenomenal. What an exciting
[27:15] This is phenomenal. What an exciting story to talk about here. Uh openweight
[27:17] story to talk about here. Uh openweight
[27:18] story to talk about here. Uh openweight AI has been China's game. This is the
[27:20] AI has been China's game. This is the
[27:20] AI has been China's game. This is the strongest US bid to change that if the
[27:23] strongest US bid to change that if the
[27:23] strongest US bid to change that if the weights actually ship. Uh Reflection
[27:25] weights actually ship. Uh Reflection
[27:25] weights actually ship. Uh Reflection says Beam is the first open model from a
[27:28] says Beam is the first open model from a
[27:28] says Beam is the first open model from a US startup to match the best Chinese
[27:31] US startup to match the best Chinese
[27:31] US startup to match the best Chinese models. Uh Reflection's own benchmarks,
[27:34] models. Uh Reflection's own benchmarks,
[27:34] models. Uh Reflection's own benchmarks, independent tests wait for uh the actual
[27:37] independent tests wait for uh the actual
[27:38] independent tests wait for uh the actual weights to come out and then we'll be
[27:40] weights to come out and then we'll be
[27:40] weights to come out and then we'll be able to test this with third party
[27:42] able to test this with third party
[27:42] able to test this with third party testing and see where that lands. Uh you
[27:45] testing and see where that lands. Uh you
[27:45] testing and see where that lands. Uh you can't download it yet. It looks like the
[27:47] can't download it yet. It looks like the
[27:47] can't download it yet. It looks like the weights are planned for later this
[27:49] weights are planned for later this
[27:49] weights are planned for later this month. Um, and let's get into this
[27:54] month. Um, and let's get into this
[27:54] month. Um, and let's get into this topic. Uh, this is a little bit more
[27:57] topic. Uh, this is a little bit more
[27:57] topic. Uh, this is a little bit more technical, so we'll try to explain it.
[27:58] technical, so we'll try to explain it.
[27:58] technical, so we'll try to explain it. But netnet, the really exciting thing
[28:01] But netnet, the really exciting thing
[28:02] But netnet, the really exciting thing here is open source. And why does open
[28:04] here is open source. And why does open
[28:04] here is open source. And why does open source matter? Um, and why does US open
[28:08] source matter? Um, and why does US open
[28:08] source matter? Um, and why does US open source matter? Uh, there's a number of
[28:10] source matter? Uh, there's a number of
[28:10] source matter? Uh, there's a number of reasons here. We we want technology. We
[28:13] reasons here. We we want technology. We
[28:13] reasons here. We we want technology. We want this intelligence to be easily
[28:15] want this intelligence to be easily
[28:15] want this intelligence to be easily distributed and open and available to
[28:18] distributed and open and available to
[28:18] distributed and open and available to everybody. And when you have constraints
[28:20] everybody. And when you have constraints
[28:20] everybody. And when you have constraints around um uh you know whether it's
[28:25] around um uh you know whether it's
[28:25] around um uh you know whether it's gating who can access your your models
[28:29] gating who can access your your models
[28:29] gating who can access your your models uh um protecting against uh foreign
[28:33] uh um protecting against uh foreign
[28:33] uh um protecting against uh foreign access or uh uh preventing people based
[28:37] access or uh uh preventing people based
[28:37] access or uh uh preventing people based on financial ability whatever it may be.
[28:40] on financial ability whatever it may be.
[28:40] on financial ability whatever it may be. We want to have open-source so that
[28:43] We want to have open-source so that
[28:43] We want to have open-source so that everybody has access to the same
[28:45] everybody has access to the same
[28:45] everybody has access to the same technology. It offers a level playing
[28:48] technology. It offers a level playing
[28:48] technology. It offers a level playing field. Uh nobody gets um intelligence
[28:51] field. Uh nobody gets um intelligence
[28:51] field. Uh nobody gets um intelligence greater than somebody else. And that's
[28:53] greater than somebody else. And that's
[28:53] greater than somebody else. And that's the idea here. And so when we see a US
[28:56] the idea here. And so when we see a US
[28:56] the idea here. And so when we see a US open- source model uh come out that has
[29:00] open- source model uh come out that has
[29:00] open- source model uh come out that has uh near frontier level intelligence,
[29:02] uh near frontier level intelligence,
[29:02] uh near frontier level intelligence, this is really important. Frontier is
[29:05] this is really important. Frontier is
[29:05] this is really important. Frontier is think of anthropic and open AI. They are
[29:09] think of anthropic and open AI. They are
[29:09] think of anthropic and open AI. They are frontier. they are leading the uh the
[29:12] frontier. they are leading the uh the
[29:12] frontier. they are leading the uh the the pack with the most intelligent, most
[29:15] the pack with the most intelligent, most
[29:15] the pack with the most intelligent, most advanced technology. But you move down
[29:17] advanced technology. But you move down
[29:17] advanced technology. But you move down just a little bit and you have some
[29:19] just a little bit and you have some
[29:19] just a little bit and you have some really phenomenal open- source
[29:21] really phenomenal open- source
[29:21] really phenomenal open- source technologies out of China uh that are
[29:25] technologies out of China uh that are
[29:25] technologies out of China uh that are extremely capable, extremely low cost.
[29:29] extremely capable, extremely low cost.
[29:29] extremely capable, extremely low cost. Uh the problem is they're based out of
[29:32] Uh the problem is they're based out of
[29:32] Uh the problem is they're based out of China, our adversary. uh and being able
[29:36] China, our adversary. uh and being able
[29:36] China, our adversary. uh and being able to ensure that those models remain open,
[29:40] to ensure that those models remain open,
[29:40] to ensure that those models remain open, remain accessible to the US. China kind
[29:42] remain accessible to the US. China kind
[29:42] remain accessible to the US. China kind of has a history of offering o
[29:46] of has a history of offering o
[29:46] of has a history of offering o open-source solutions, gaining wide
[29:49] open-source solutions, gaining wide
[29:49] open-source solutions, gaining wide adoption and then um making sure people
[29:52] adoption and then um making sure people
[29:52] adoption and then um making sure people are locked into those solutions and then
[29:56] are locked into those solutions and then
[29:56] are locked into those solutions and then charging them for it and it's hard to
[29:58] charging them for it and it's hard to
[29:58] charging them for it and it's hard to get off. And so when we see uh US model
[30:01] get off. And so when we see uh US model
[30:02] get off. And so when we see uh US model companies release an open model solution
[30:05] companies release an open model solution
[30:05] companies release an open model solution that is of the same technology when we
[30:07] that is of the same technology when we
[30:07] that is of the same technology when we reference uh GLM or Quen those are
[30:11] reference uh GLM or Quen those are
[30:11] reference uh GLM or Quen those are Chinese model companies and so um when
[30:14] Chinese model companies and so um when
[30:14] Chinese model companies and so um when we see a US model uh come out that
[30:17] we see a US model uh come out that
[30:17] we see a US model uh come out that matches the advanced uh capabilities of
[30:21] matches the advanced uh capabilities of
[30:21] matches the advanced uh capabilities of Chinese models that's really good.
[30:23] Chinese models that's really good.
[30:23] Chinese models that's really good. Getting into a couple of the the numbers
[30:25] Getting into a couple of the the numbers
[30:25] Getting into a couple of the the numbers I think is interesting. Um it's a uh
[30:30] I think is interesting. Um it's a uh
[30:30] I think is interesting. Um it's a uh mixture of experts model. What does that
[30:33] mixture of experts model. What does that
[30:33] mixture of experts model. What does that mean? It means that you've got a full
[30:36] mean? It means that you've got a full
[30:36] mean? It means that you've got a full model set. So uh the the the the model,
[30:39] model set. So uh the the the the model,
[30:39] model set. So uh the the the the model, the technology, the intelligence, think
[30:41] the technology, the intelligence, think
[30:41] the technology, the intelligence, think of it as a whole brain like this. The
[30:44] of it as a whole brain like this. The
[30:44] of it as a whole brain like this. The problem is um what you would do in this
[30:47] problem is um what you would do in this
[30:47] problem is um what you would do in this case or in in most case with LLMs is
[30:51] case or in in most case with LLMs is
[30:51] case or in in most case with LLMs is you're going to ask it a question,
[30:53] you're going to ask it a question,
[30:53] you're going to ask it a question, right? um uh when was Abraham Lincoln
[30:56] right? um uh when was Abraham Lincoln
[30:56] right? um uh when was Abraham Lincoln president? And it's going to search all
[30:59] president? And it's going to search all
[30:59] president? And it's going to search all of that brain uh for that information.
[31:04] of that brain uh for that information.
[31:04] of that brain uh for that information. It it it's going to compute the whole
[31:07] It it it's going to compute the whole
[31:07] It it it's going to compute the whole all the parameters within that brain to
[31:10] all the parameters within that brain to
[31:10] all the parameters within that brain to understand Abraham Lincoln. Really
[31:12] understand Abraham Lincoln. Really
[31:12] understand Abraham Lincoln. Really simple question. uh mixture of experts
[31:16] simple question. uh mixture of experts
[31:16] simple question. uh mixture of experts actually decides hey let me split this
[31:19] actually decides hey let me split this
[31:19] actually decides hey let me split this up into sections and then I'll only use
[31:21] up into sections and then I'll only use
[31:21] up into sections and then I'll only use the section that really matters for this
[31:23] the section that really matters for this
[31:23] the section that really matters for this specific question instead of running
[31:26] specific question instead of running
[31:26] specific question instead of running compute across the whole thing. So it's
[31:27] compute across the whole thing. So it's
[31:27] compute across the whole thing. So it's much more efficient. Uh and then to be
[31:30] much more efficient. Uh and then to be
[31:30] much more efficient. Uh and then to be able to run that on I think it was 23
[31:32] able to run that on I think it was 23
[31:32] able to run that on I think it was 23 billion uh parameters. It sounds like a
[31:35] billion uh parameters. It sounds like a
[31:35] billion uh parameters. It sounds like a very large number but um that's actually
[31:38] very large number but um that's actually
[31:38] very large number but um that's actually extremely small for uh these LLMs such
[31:42] extremely small for uh these LLMs such
[31:42] extremely small for uh these LLMs such that um an open- source model like this
[31:45] that um an open- source model like this
[31:45] that um an open- source model like this you could run on your own MacBook
[31:47] you could run on your own MacBook
[31:47] you could run on your own MacBook potentially very slowly. it wouldn't be
[31:51] potentially very slowly. it wouldn't be
[31:51] potentially very slowly. it wouldn't be uh you know instant answers that you
[31:53] uh you know instant answers that you
[31:53] uh you know instant answers that you would get but you could run it locally
[31:54] would get but you could run it locally
[31:54] would get but you could run it locally which is really exciting for a bunch of
[31:56] which is really exciting for a bunch of
[31:56] which is really exciting for a bunch of different use cases and and overall you
[31:59] different use cases and and overall you
[31:59] different use cases and and overall you know congratulations to uh the the the
[32:03] know congratulations to uh the the the
[32:03] know congratulations to uh the the the team at Reflection and and uh seeing
[32:06] team at Reflection and and uh seeing
[32:06] team at Reflection and and uh seeing this open source model come out uh is
[32:09] this open source model come out uh is
[32:09] this open source model come out uh is just an encouragement I think to the
[32:10] just an encouragement I think to the
[32:10] just an encouragement I think to the rest of the open source community to the
[32:13] rest of the open source community to the
[32:13] rest of the open source community to the US open source community uh to the AI
[32:16] US open source community uh to the AI
[32:16] US open source community uh to the AI community as a whole and it's a big
[32:18] community as a whole and it's a big
[32:18] community as a whole and it's a big impact Um, at some point in time
[32:20] impact Um, at some point in time
[32:20] impact Um, at some point in time hopefully, uh, anybody will be able to
[32:23] hopefully, uh, anybody will be able to
[32:23] hopefully, uh, anybody will be able to go and download this for free and run it
[32:26] go and download this for free and run it
[32:26] go and download this for free and run it on their own computer and have
[32:28] on their own computer and have
[32:28] on their own computer and have technology, uh, and this intelligence at
[32:31] technology, uh, and this intelligence at
[32:31] technology, uh, and this intelligence at their fingertips, which is really
[32:33] their fingertips, which is really
[32:33] their fingertips, which is really exciting. Uh, well, thank you guys so
[32:36] exciting. Uh, well, thank you guys so
[32:36] exciting. Uh, well, thank you guys so much for tuning in today. We had an
[32:38] much for tuning in today. We had an
[32:38] much for tuning in today. We had an amazing show. We covered a lot of really
[32:40] amazing show. We covered a lot of really
[32:40] amazing show. We covered a lot of really impactful topics I think from what's
[32:43] impactful topics I think from what's
[32:43] impactful topics I think from what's going on with the New York City uh
[32:45] going on with the New York City uh
[32:45] going on with the New York City uh council to Grock acquisition etched
[32:48] council to Grock acquisition etched
[32:48] council to Grock acquisition etched success um and much more. We are
[32:52] success um and much more. We are
[32:52] success um and much more. We are continually researching and targeting
[32:55] continually researching and targeting
[32:56] continually researching and targeting the most important AI news that impacts
[32:58] the most important AI news that impacts
[32:58] the most important AI news that impacts you and we want to get it to you in a
[33:01] you and we want to get it to you in a
[33:01] you and we want to get it to you in a clear and understandable fashion. If
[33:03] clear and understandable fashion. If
[33:03] clear and understandable fashion. If you'd like more detail or more
[33:04] you'd like more detail or more
[33:04] you'd like more detail or more information, please go to to.live.
[33:08] information, please go to to.live.
[33:08] information, please go to to.live. That's t aws.live
[33:10] That's t aws.live
[33:10] That's t aws.live the austinwilsonshow.live
[33:13] the austinwilsonshow.live
[33:13] the austinwilsonshow.live and sign up for our newsletter the loop
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[33:26] and I'm your human in the loop Austin
[33:26] and I'm your human in the loop Austin Wilson. We'll see you tomorrow at 6 a.m.
[33:29] Wilson. We'll see you tomorrow at 6 a.m.
[33:30] Wilson. We'll see you tomorrow at 6 a.m. Central time. Let's go.