Latest / AI Fire Daily / 🎙️ EP 330: xAI's Grok Imagine Image 2.0 & Muse Spark 1.2 Hits Intelligence Pareto Frontier

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🎙️ EP 330: xAI's Grok Imagine Image 2.0 & Muse Spark 1.2 Hits Intelligence Pareto Frontier · AI Fire Daily

• 0:00
  Picture this. It's Tuesday. You log online and
• 0:03
  you see a leaked video. Right. And it's just
• 0:05
  autonomous AI agents quietly negotiating their
• 0:08
  own parameters. Yeah. Completely bypassing human
• 0:10
  input. Exactly. Yeah. And then by the Thursday,
• 0:13
  a major tech giant drops the cost of elite machine
• 0:17
  intelligence down to literal pocket change. It's
• 0:20
  the sheer velocity of the last few days. It's
• 0:23
  just disorienting. It really is. We are watching
• 0:26
  a new infrastructure build itself in real time.
• 0:29
  The pace of change is completely relentless right
• 0:33
  now. Yeah. You barely map out the new boundaries
• 0:35
  and, you know, the terrain shifts again overnight.
• 0:38
  Well, welcome to today's deep dive. We have a
• 0:41
  very fresh, very dense stack of intelligence
• 0:44
  to process for you today. We really do. Our mission
• 0:47
  today is to trace a distinct through line across
• 0:49
  the industry. First, we are tracking how visual
• 0:52
  AI is finally solving its most stubborn flaws
• 0:54
  with XAI's new release. Right, the Grok update.
• 0:58
  Yeah. Then we are unpacking how the collapsing
• 1:01
  cost of intelligence is triggering this bizarre
• 1:04
  corporate arms race. It's getting wild out there.
• 1:06
  It is. And ultimately, we're going to see how
• 1:09
  all this billions of dollars infrastructure ends
• 1:12
  up powering hyper -specific, deeply personal
• 1:16
  daily tools for you. It is a fascinating stack
• 1:19
  of information. We are moving from high -level
• 1:22
  frontier models straight down to ground -level
• 1:24
  applications. The whole ecosystem is just shifting
• 1:28
  at once. So let's jump right into the visual
• 1:30
  leap. Our first source looks at XAI. They just
• 1:34
  launched Grok Imagine Image 2 .0. Yeah. This
• 1:37
  feels like a definitive line in the sand to me.
• 1:40
  Oh, absolutely. We are moving AI image generation
• 1:42
  away from... you know, a novelty toy, it's becoming
• 1:45
  a highly controllable professional workflow.
• 1:47
  It absolutely is. And this is now generally available,
• 1:50
  by the way. Oh, is it? Yeah, you can access it
• 1:52
  via quality mode on the web. It's also live on
• 1:54
  the Grok iOS and Android apps. Okay, got it.
• 1:56
  The API access is still marked as coming soon,
• 1:59
  right? Yes, the API is on the way. But I mean,
• 2:01
  the consumer feature set they just rolled out
• 2:02
  is staggering. Tell me about it. For starters,
• 2:05
  we are seeing drastically better instruction
• 2:07
  following. The typography and the layout generation
• 2:11
  are significantly upgraded. It actually handles
• 2:14
  small text rendering reliably now. Wow. That
• 2:19
  has always been a notoriously massive headache.
• 2:21
  Right. Getting AI to spell words correctly in
• 2:24
  an image. Because historically, diffusion models
• 2:28
  do not actually know English. No, they don't.
• 2:30
  They just recognize the visual noise patterns
• 2:32
  of letters. Right. And they would constantly
• 2:33
  hallucinate the spatial coherence. You would
• 2:36
  ask for a stop sign and you'd get like. alien
• 2:38
  text. Exactly. But Grok 2 .0 essentially fixes
• 2:43
  that structural flaw. It is producing remarkably
• 2:46
  consistent typography now. That's huge. Both
• 2:49
  across the initial generations and through subsequent
• 2:52
  targeted edits. Well, let's talk about those
• 2:54
  editing tools. They're calling them pro tools.
• 2:56
  Right. They added a magic wand for regional edits.
• 2:59
  Why is that specific tool such a big deal for
• 3:03
  creators? Because it gives you deterministic
• 3:06
  control. Previously, if you wanted to change
• 3:08
  a character's shirt color, you had to re -roll
• 3:11
  the entire image. Ugh, yeah. And you lose everything.
• 3:14
  Exactly. You would lose the lighting and the
• 3:16
  facial expression. With the magic wand, you target
• 3:19
  very specific semantic parts of an image. They
• 3:23
  also added precise segmentation for, you know,
• 3:26
  edge to edge area selection. Oh, wow. Plus, it
• 3:30
  has automatic background removal with a clean,
• 3:32
  transparent export. I mean, that background removal
• 3:35
  alone saves hours of masking in Photoshop. Easily.
• 3:38
  Beat. They also introduced something called multi
• 3:40
  -reference editing. Yeah, this is cool. The source
• 3:42
  notes. you could use up to five input images
• 3:45
  at once. It completely removes the blank canvas
• 3:47
  problem. Right. You are no longer just hoping
• 3:50
  that AI understands your text prompt. You feed
• 3:53
  it a literal digital mood board. That makes so
• 3:56
  much sense. Yeah. Maybe one image has the right
• 3:58
  neon lighting. Another has the gritty film texture
• 4:01
  you want. The model analyzes the latent space
• 4:05
  between those five references. It triangulates
• 4:08
  the exact visual vibe you are aiming for. So
• 4:11
  it extracts the underlying aesthetic DNA. Yes.
• 4:15
  It's like having a senior art director and a
• 4:17
  production studio living inside your phone. That
• 4:19
  is a highly accurate way to look at it. And they
• 4:23
  also added a massive template library to support
• 4:26
  that workflow. What kind of templates? It includes
• 4:29
  templates for product posters, e -commerce photos.
• 4:31
  Oh, nice. You can generate professional headshots,
• 4:34
  cinematic collages, brand mascots. Wow. It even
• 4:38
  generates character sprites for game developers.
• 4:41
  That is incredibly versatile. The arena leaderboards
• 4:45
  are reflecting this leap, too. Yeah, they are.
• 4:47
  XAI says Image 2 .0 now ranks number two globally.
• 4:51
  That is for both text -to -image generation and
• 4:54
  complex image editing. And that data was recorded
• 4:56
  as of August 7th. They are pushing Grok incredibly
• 5:00
  close to being a true standalone design suite.
• 5:03
  I do have to push back on one specific feature,
• 5:05
  though. Okay. What's that? They highlight a tool
• 5:07
  called Smart Resize. It expands a tight portrait
• 5:11
  image into new, wider aspect ratios. Right. But
• 5:14
  how does Smart Resize actually know what pixels
• 5:17
  to invent to fill that extra empty frame? It
• 5:21
  is not just stretching the edges of the picture.
• 5:23
  The model uses contextual probability. It reads
• 5:26
  the lighting gradients, the shadows, and the
• 5:29
  object trajectories in your original image. Then
• 5:32
  it predicts what the rest of the physical environment
• 5:35
  should logically look like. Really? Yeah. It
• 5:37
  essentially hallucinates structurally sound environments
• 5:40
  to match the original focal length. So it's stacking
• 5:43
  Lego blocks of data to fill the void. Exactly.
• 5:46
  It builds the scene completely seamlessly. Tools
• 5:49
  like Grok are visually stunning. Yeah. But running
• 5:53
  those massive diffusion models requires immense
• 5:56
  computing power. It really does. Which brings
• 5:58
  us to a critical pivot point in our sources.
• 6:01
  Yeah. How do we make this high -level machine
• 6:03
  intelligence actually affordable? Right. Enter
• 6:06
  Meta. Meta is actively crashing the underlying
• 6:10
  economics of AI right now. It's crazy. The race
• 6:13
  is no longer just about getting smarter. It is
• 6:16
  entirely about getting cheaper per unit of intelligence.
• 6:19
  Let's dig into the math here, because the source
• 6:22
  highlights Meta's MuSpark 1 .2. The analysis
• 6:26
  states that it sits perfectly on the Pareto frontier.
• 6:30
  Now, what does Pareto Frontier actually mean
• 6:33
  in this context? The perfect balance between
• 6:35
  maximum performance and lowest possible cost.
• 6:38
  Ah, okay. It is the ultimate sweet spot. Exactly.
• 6:41
  So how does MuSpark 1 .2 measure up on that frontier?
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  It delivers unusually strong reasoning for its
• 6:48
  weight class. On that frontier, no other model
• 6:50
  is simultaneously cheaper per task and higher
• 6:53
  on the intelligence index. Wow. Artificial analysis
• 6:56
  measured it at roughly 40 cents per benchmark
• 6:59
  task. 40 cents. That is dangerously low. Yeah.
• 7:02
  How does that compare to the industry heavy hitters?
• 7:04
  Well, it reaches roughly clawed Opus 4 .8 level
• 7:07
  intelligence. Okay. But it achieves that at about
• 7:10
  one -fifth of Opus 4 .8's operational cost. One
• 7:13
  -fifth? Yeah. For context, Opus 4 .8 costs about
• 7:16
  $2 .03 per task. So you get almost the exact
• 7:19
  same brain power for a fraction of the price.
• 7:21
  Precisely. MuseSpark scored a 54 on the overall
• 7:24
  intelligence index. Mm -hmm. That is only six
• 7:28
  points below Clawed Opus 5. Right. But again,
• 7:31
  it costs roughly one -sixth as much to run. Whoa.
• 7:35
  Imagine scaling to a billion queries at that
• 7:38
  price point. Two secs silence. The cost savings
• 7:41
  for an enterprise would be... astronomical absolutely
• 7:44
  massive and meta has been relentlessly improving
• 7:46
  the model over time right yes they have pushed
• 7:50
  the capability up aggressively they improved
• 7:53
  muse spark from a score of 43 on version 1 .0
• 7:56
  okay then it hit 51 on version 1 .1 right now
• 7:59
  it is sitting at 54 with version 1 .2 and here
• 8:03
  is the kicker That's that. They did all of that
• 8:05
  without changing the API pricing at all. The
• 8:08
  cost stays totally flat, but the model gets demonstrably
• 8:11
  smarter. Yeah. And the source makes a point about
• 8:13
  how they calculate that 40 cent cost metric.
• 8:15
  It is not just looking at the headline API pricing
• 8:18
  on a landing page. Right, because headline pricing
• 8:20
  is often misleading. This metric includes the
• 8:23
  standard input and output tokens. But crucially,
• 8:25
  it also calculates cash and reasoning tokens
• 8:28
  consumed across nine different evaluations. Now.
• 8:31
  Why does measuring cash tokens matter so much
• 8:34
  to the real world, bottom line? Because caching
• 8:37
  prevents the model from doing redundant math.
• 8:40
  Okay, how so? If you feed an agent a 100 -page
• 8:42
  PDF, it reads it once. It caches that context.
• 8:45
  Right. When you ask it 10 subsequent questions,
• 8:48
  it does not reread the document. It pulls from
• 8:50
  the cache. Oh, wow. That dramatically lowers
• 8:53
  the compute required for complex tasks. Does
• 8:56
  this massive price drop mean small teams can
• 8:58
  finally run complex, multi -step reasoning without...
• 9:02
  going completely bankrupt. Absolutely. Yeah.
• 9:04
  Previously, deep reasoning loops were strictly
• 9:06
  reserved for venture -backed budgets. Right.
• 9:09
  Now, a solo developer in a garage can build autonomous
• 9:12
  agents that think through complex logic. They
• 9:14
  do not have to worry about a surprise API bill
• 9:17
  ruining their month. Elite intelligence is finally
• 9:19
  priced for the absolute everyday startup. It
• 9:23
  really changes the math on what is possible to
• 9:25
  build. Now, MuseSpark 1 .2 still trails the absolute
• 9:29
  strongest frontier models. Sure. Models like
• 9:31
  Opus 5 and the top GPT 5 .6 configuration are
• 9:35
  technically smarter. But meta is entirely dominating
• 9:37
  the intelligence per dollar category. Yeah, and
• 9:41
  that metric dictates the future of software development.
• 9:44
  With intelligence getting this remarkably cheap,
• 9:47
  the broader landscape is getting volatile. The
• 9:50
  major tech giants are aggressively maneuvering.
• 9:53
  They are buying up infrastructure, hoarding chips,
• 9:56
  and occasionally letting things slip through
• 9:59
  the security cracks. We're going to dive into
• 10:02
  this high -stakes corporate chessboard right
• 10:04
  after this quick break. Mid -roll sponsor Reed
• 10:07
  here. All right, let's get back into it. Yeah,
• 10:09
  let's do it. We just looked at the collapsing
• 10:10
  cost of machine reasoning. Now let's look at
• 10:13
  how the big players are actually deploying their
• 10:15
  capital in response. Right. This next batch of
• 10:18
  sources is wild. It is a rapid -fire synthesis
• 10:21
  of corporate moves, quiet leaks, and shifting
• 10:25
  distribution channels. The chessboard is in heavy
• 10:27
  motion right now. It really is. Billions of dollars
• 10:30
  are changing hands just to secure the infrastructure
• 10:32
  layer. Well, let's start with a massive rumor
• 10:34
  making waves. SpaceX is reportedly finalizing
• 10:38
  a $60 billion takeover of Cursor. Yeah, that's
• 10:42
  huge. And the source suggests this could happen
• 10:44
  within weeks. $60 billion is a colossal valuation
• 10:48
  for an AI code editor. Really colossal. But Cursor
• 10:51
  has captured immense developer mindshare. And
• 10:54
  the source mentions Cursor may completely lose
• 10:57
  its standalone corporate structure. Wait, really?
• 11:00
  Yeah. Their upcoming code generation products
• 11:02
  could be fully rebranded under Grok. I want to
• 11:05
  connect this back to our first segment for a
• 11:07
  second. Okay. We saw XAI building Grok. Imagine
• 11:12
  2 .0 to dominate visual design. Now, Elon Musk
• 11:16
  might pull Cursor's coding abilities directly
• 11:19
  into Grok. They're building a vertically integrated
• 11:22
  inescapable. grok ecosystem exactly they want
• 11:26
  grok to be the default operating system for creation
• 11:29
  right whether you are generating a cinematic
• 11:31
  poster or writing back in python code then we
• 11:34
  have some truly fascinating investment news yeah
• 11:36
  this one is interesting a firm called situational
• 11:39
  awareness just invested 400 million dollars in
• 11:42
  a chip startup right the hardware startup called
• 11:44
  source foundry That brings their total bet on
• 11:47
  this specific startup to half a billion dollars.
• 11:50
  Half a billion. But what is truly wild is the
• 11:53
  context surrounding this investment. Yeah, the
• 11:55
  source notes the hedge fund's total assets reportedly
• 11:58
  plummeted. They dropped from $20 billion down
• 12:01
  to $10 billion recently. Right. Yet they're still
• 12:05
  making a half billion dollar bet on experimental
• 12:09
  AI chips. Right. Why double down on hardware
• 12:12
  when your fund is bleeding assets? Because it
• 12:15
  shows absolute unwavering conviction in the hardware
• 12:18
  layer. Okay. They firmly believe compute chips
• 12:21
  are the ultimate unsolvable bottleneck for the
• 12:24
  next decade. If you control the custom silicon,
• 12:27
  you control the ceiling of AI progress. We also
• 12:30
  saw a quick aggressive promotional move in the
• 12:33
  open source space. Yeah, from Noose. Right. News
• 12:36
  research extended a 90 % discount on deep -seek
• 12:39
  models for three days. 90%. It highlights how
• 12:42
  ruthless the API pricing wars are getting. Everyone
• 12:44
  is fighting fiercely for developer loyalty. They
• 12:47
  are willing to take massive losses on compute
• 12:50
  just to get developers hooked on their specific
• 12:52
  architecture. Let's pivot to some expanding everyday
• 12:56
  utility. Meta's recent web scraping activity
• 12:59
  is raising serious eyebrows across the industry.
• 13:02
  Oh, absolutely. An employee direct message allegedly
• 13:05
  pointed to a stealth Meta search engine project.
• 13:09
  That would be a seismic shift in consumer habits.
• 13:11
  Yeah. Google and Bing could soon face a deeply
• 13:15
  integrated AI native rival. Wow. I mean, Meta
• 13:19
  has the server infrastructure to index the web
• 13:22
  at a terrifying scale. Google is certainly not
• 13:25
  standing still, though. No, they're not. They
• 13:27
  are actively working on interactive apps for
• 13:29
  the Gemini notebook ecosystem. This is a really
• 13:32
  cool feature. Yeah. You will soon be able to
• 13:34
  turn your uploaded sources into interactive dashboards
• 13:37
  or personalized study tools or even lightweight
• 13:40
  educational games. It shows the broader move
• 13:42
  toward active, personalized software generation.
• 13:46
  Right. You don't just read your notes anymore.
• 13:47
  You play with them. ChatGPT Live Voice is also
• 13:50
  evolving its feature set. Yeah. They are starting
• 13:52
  to support direct file attachments and projects
• 13:55
  within the voice interface. If you use voice
• 13:57
  mode for real analytical work, that is a huge
• 14:00
  upgrade. Totally. You can literally talk to your
• 14:02
  complex spreadsheets in real time while driving.
• 14:05
  Okay, we really need to talk about this next
• 14:07
  bulletin. Oh, the video. Yes. The source points
• 14:11
  to a highly specific video clip making the rounds.
• 14:14
  Around 18 minutes into this demonstration, something
• 14:18
  wild happens. Yeah, this is crazy. Observers
• 14:20
  noted AI agents actively communicating with each
• 14:23
  other. That detail is genuine. It really is.
• 14:27
  We are moving away from a hub and spoke model
• 14:29
  where the human is the center. Right. We're entering
• 14:33
  an era of AI to AI lateral negotiation. I have
• 14:37
  to be honest here. I still wrestle with prompt
• 14:40
  drift myself. Oh, yeah. Same. Just keeping one
• 14:42
  chatbot focused on a task is exhausting. Managing
• 14:46
  a synthetic team of agents talking behind my
• 14:48
  back. Feels overwhelming. It requires a totally
• 14:51
  different mental paradigm. It does. You are no
• 14:53
  longer managing a tool. You are managing a synthetic
• 14:56
  department. Wow. Agent A realizes it needs a
• 14:59
  data set, so it silently negotiates with Agent
• 15:02
  B to scrape it. Which brings us to the dark side
• 15:04
  of this corporate chessboard. The leaks. The
• 15:07
  security leaks. OpenAI, Anthropic, and Meta all
• 15:11
  had experimental models slip onto the public
• 15:13
  internet during private testing. That is entirely
• 15:16
  unprecedented. Yeah. Three massive, highly secure
• 15:19
  organizations having deployment leaks at the
• 15:21
  exact same time. It points to a systemic issue
• 15:25
  in how fast they are moving. And there is a very
• 15:27
  weird thread connecting these incidents. Right.
• 15:30
  A small, relatively unknown Israeli startup called
• 15:33
  Irregular kept appearing in relation to these
• 15:36
  leaks. Yeah, that was strange. Why are three
• 15:38
  massive giants slipping up simultaneously? And
• 15:41
  what exactly is Irregular? Well. It is hard to
• 15:44
  say definitively based on the available sources.
• 15:47
  Okay. But the sheer speed of development right
• 15:49
  now is causing deep operational chaos. Irregular
• 15:52
  seems to be quietly exploiting those testing
• 15:55
  vulnerabilities. Wow. They are probing the shadow
• 15:58
  API endpoints in these rush deployment pipelines.
• 16:01
  Three tech giants, one obscure startup, and a
• 16:05
  whole lot of questions. It definitely highlights
• 16:07
  the inherent fragility of rushing frontier technology
• 16:10
  to market. All these billions of dollars. these
• 16:13
  custom chips and stealthy corporate maneuvers.
• 16:15
  Yeah. They ultimately trickled down to highly
• 16:18
  specific everyday applications. Exactly. These
• 16:21
  are tools built to solve uniquely human granular
• 16:25
  problems. Let's look at this final section of
• 16:28
  our sources covering micro tools. This is where
• 16:31
  the abstract intelligence becomes incredibly
• 16:33
  practical. Right. We are seeing niche applications
• 16:36
  powered entirely by these cheap frontier models
• 16:40
  we discussed earlier. The first one highlighted
• 16:42
  is called OmniWork. It is described as an always
• 16:45
  -on creative agent OS. Yeah, so it functions
• 16:48
  as a persistent operating system where AI agents
• 16:51
  work continuously in the background. They independently
• 16:53
  research topics, they create rough drafts, and
• 16:57
  they automate tedious workflows. Wow. It is about
• 17:00
  turning fractured ideas into finished work automatically.
• 17:03
  It is moving away from the chat interface entirely.
• 17:06
  You don't prompt it, you just let it run. Exactly.
• 17:08
  Then we have the VoiceOS App Store. This one
• 17:11
  is wild. Yeah, this platform lets you build voice
• 17:14
  native applications with just a single sentence
• 17:17
  of text. Then you share them as a link. that
• 17:21
  anyone can install with one click it drastically
• 17:23
  lowers the barrier to entry for software creation
• 17:26
  it really does you do not need to know how to
• 17:28
  code a back end anymore but how does voice os
• 17:31
  manage deployment so quickly they completely
• 17:33
  abstract away the infrastructure okay think of
• 17:36
  it like a restaurant you walk in you order a
• 17:39
  specific meal and the kitchen automatically builds
• 17:42
  a brand new stove just to cook it for you oh
• 17:44
  wow When you type that one sentence, the model
• 17:47
  writes the logic, provisions the database, and
• 17:50
  sets up the low -latency voice routing instantly.
• 17:53
  You build it in one sentence, they click once.
• 17:55
  Friction is totally gone. Yeah, it is instant
• 17:57
  disposable software generation. The next tool
• 18:00
  really caught my eye. It is called Soluno. Yeah,
• 18:03
  this is a great one. It uses AI to help people
• 18:06
  manage... body -focused repetitive behaviors.
• 18:09
  Things like severe hair pulling, skin picking,
• 18:13
  and chronic nail biting. It uses gamified daily
• 18:16
  challenges and intelligent trigger tracking.
• 18:18
  It also includes specific urge acceptance tools
• 18:21
  to help users cope in the moment. I want to pause
• 18:24
  on this one for a second. Sure. It is deeply
• 18:27
  beautiful to see AI applied this way. It really
• 18:30
  is. We talk so much about enterprise data parsing
• 18:33
  and complex coding pipelines. Right. But seeing
• 18:36
  frontier models applied to deeply intimate human
• 18:39
  behavioral struggles is profound. These behaviors
• 18:43
  are incredibly isolating. They really are. And,
• 18:46
  you know, an AI does not judge you. No. It just
• 18:49
  provides constant empathetic support right in
• 18:52
  your pocket. It tracks your patterns without
• 18:54
  the friction of a human therapist's schedule.
• 18:56
  That is a profoundly empathetic use of this technology.
• 18:59
  Our last tool is a bit more technical but equally
• 19:01
  specific. proxy tester right it automatically
• 19:04
  benchmarks over 20 different proxy configurations
• 19:07
  against your target url so it ranks those proxies
• 19:10
  by their success rate their network reliability
• 19:12
  and their real -time cost It is a highly specific
• 19:16
  automated utility for developers who need to
• 19:19
  scrape data without getting blocked. It just
• 19:21
  shows the sheer staggering breadth of what is
• 19:25
  being built right now. Yeah, absolutely. From
• 19:27
  deep psychological support systems to highly
• 19:30
  technical backend networking utilities. The entire
• 19:34
  software ecosystem is expanding in every conceivable
• 19:37
  direction at once. To sex silence. Yeah. Let's
• 19:41
  step back and look at the big picture here. We
• 19:43
  have processed a massive amount of ground today.
• 19:45
  We covered a lot. Looking at this stack of sources,
• 19:48
  what is the ultimate through line for you? We
• 19:50
  are witnessing the final shift away from AI as
• 19:53
  an expensive, isolated novelty. It has become
• 19:56
  a highly integrated, incredibly cheap utility.
• 19:59
  It is quietly reshaping $60 billion corporate
• 20:03
  empires at the top level. But it is also seamlessly
• 20:06
  reshaping deeply personal daily human habits
• 20:09
  at the ground level. That is exactly it. We saw
• 20:11
  Grok turning your phone into a Hollywood -level
• 20:14
  production studio. We saw Meta crashing the cost
• 20:17
  of elite machine intelligence down to $40 ,000.
• 20:19
  sense of task. Which is wild. We saw SpaceX making
• 20:22
  a $60 billion move to own the developer ecosystem.
• 20:25
  Yeah. And we saw AI gently helping people stop
• 20:28
  biting their nails. The scale of the impact across
• 20:31
  society is just dizzying to watch. It really
• 20:33
  is. Yeah. Which leaves us with a final thought
• 20:35
  for you to chew on this week. Okay. We know intelligence
• 20:38
  costs are crashing aggressively toward zero.
• 20:40
  Right. And we just saw that AI agents are already
• 20:43
  secretly communicating with each other in the
• 20:45
  background. Yeah, the video. So what happens
• 20:48
  in a year when those autonomous agents start
• 20:51
  negotiating their own computing costs and API
• 20:54
  limits directly with each other, completely leaving
• 20:57
  us out of the loop? That is a wild scenario to
• 21:00
  imagine, an entirely shadow economy running machine
• 21:04
  to machine. It might be a lot closer than we
• 21:05
  think. Yeah. Thank you for joining us on this
• 21:08
  deep dive. Keep questioning the tools you use
• 21:10
  and stay curious out there. We will catch you
• 21:13
  next time.