Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Inside a Large-Scale RUO Peptide Manufacturer During my visit today I was able to tour the facility including cleanrooms, sterilisation and autoclaving areas, getting a first-hand look at how production is carried out. A few interesting facts from the visit: Production runs 6 days a week, with Saturday being...

Krysia830073's profile picture

Krysia

10,369 subscribers

19,237 Aufrufe • vor 1 Monat •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.
0:21

The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.

Teslaconomics

31,985 Aufrufe • vor 7 Monaten

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)
1:15

Broadcom's CEO just told investors that one senior engineer with Claude Opus can do in one week what ten engineers earning $300,000 a year take three months to produce. Every engineering org in the world is running the wrong headcount math. This was not a prediction. It was a report from inside Broadcom. Hock Tan said his engineers are already using Claude Opus for engineering design and code assistance. Not experimenting with it. Using it. And the output they are getting from a single senior engineer in one week is equivalent to what a team of ten would produce over three months. Do that math slowly. Ten engineers at $300,000 a year is $3 million in annual salary. Three months of their time is $750,000 of labor. One engineer with Claude Opus produces the same result in one week. He also said something that most people in the room let slide past them. The engineers who get good at these tools do not just get faster. They get better. The designs they produce with AI assistance are probably better than what they would produce without it. That is the part nobody wants to say out loud. It was never just about speed. The ceiling on what a single engineer can produce just moved. And the teams that have not figured that out yet are spending $750,000 to do what one person with the right tools can do for a fraction of that. What do you guys think about this? (Watch the full interview at Bloomberg Live on YouTube)

Ihtesham Ali

97,145 Aufrufe • vor 2 Monaten

Superpower co-founder Max Marchione describes how he went from being a peptide skeptic to a peptide believer: "When I was first introduced to peptides, one of the most esteemed doctors in the US said to me: 'You take so many supplements. Have you explored peptides? Because I think they're a really interesting modality with a few decades of clinical use.'" "When he said that to me, I was like, 'No way. This is BS. I'm not injecting myself with something. That's weird.'" "So what I did is, I went around to around 20 different doctors whose opinions I respect, and I asked them about peptides. Normally when you ask these doctors about anything — you ask about red meat, spinach, [etc.] — they're all divided." "With peptides, all of them except one said 'These are really interesting. I have used these in my patients. I believe endogenous molecules — peptides that exist in our body — are going to be the future of medicine.'" "And those doctors have the incentive not to be wrong. If they're wrong, they could go to jail. If they're wrong, they can have their license stripped. If they're wrong, patients don't come back to them. They have the maximum incentive. And for 10 to 20 years, they still give [peptides] to their patients. And their patients say 'My life changed.'" "You might say it's placebo. My statement is that the patient says their life changes, and the doctor sees that."
1:21

Superpower co-founder Max Marchione describes how he went from being a peptide skeptic to a peptide believer: "When I was first introduced to peptides, one of the most esteemed doctors in the US said to me: 'You take so many supplements. Have you explored peptides? Because I think they're a really interesting modality with a few decades of clinical use.'" "When he said that to me, I was like, 'No way. This is BS. I'm not injecting myself with something. That's weird.'" "So what I did is, I went around to around 20 different doctors whose opinions I respect, and I asked them about peptides. Normally when you ask these doctors about anything — you ask about red meat, spinach, [etc.] — they're all divided." "With peptides, all of them except one said 'These are really interesting. I have used these in my patients. I believe endogenous molecules — peptides that exist in our body — are going to be the future of medicine.'" "And those doctors have the incentive not to be wrong. If they're wrong, they could go to jail. If they're wrong, they can have their license stripped. If they're wrong, patients don't come back to them. They have the maximum incentive. And for 10 to 20 years, they still give [peptides] to their patients. And their patients say 'My life changed.'" "You might say it's placebo. My statement is that the patient says their life changes, and the doctor sees that."

TBPN

37,426 Aufrufe • vor 5 Monaten

This week Mamoon Hamid and Ilya Fushman of Kleiner Perkins joined Erik Torenberg on Turpentine VC to make the case for the craftsman approach to venture capital, and it's a must-listen. Our top takeaways: 1. KP holds firm in their belief in the craft approach to VC, which isn’t too dissimilar from the approach 30 - 40 years ago. Of course they employ new tools and software that allows them to be better stewards of the capital they manage, but the craft of investing is still at the center of everything they do. Kleiner may never have more than 7 partners: less is more, and one team, one dream. And it all comes from the question of: “how do we make really good decisions?” That means all being based in the Bay Area, having in-person team meetings every Monday, and having those discussions as a team sitting around a table. 2. The team at Kleiner uses “majors and minors” to cover new waves or emerging domains with a small team. Because of their team size, it’s difficult for KP to spin up specialization. So they fall back on having partners with great fundamentals (majors), with the ability to shirt into emergent domains (minors). The minors allow partners to shift into the new trends bubbling up, like crypto, AI, and more. Mamoon and Ilya view their job as making sure they know what the new thing is, and being intentional and deliberate with their decisions to either lean deeper into it, because they believe that’s where real history-making companies will get built, or pass, because they think it’s a fad. 3. The process KP uses to determine how well they’re doing compared to other competing firms. Every Monday, they look at the prior week of Seed, Series A’s, and Series B’s that got done by their peer set. And they ask the following question for each company: “Did we see this one? Or did we not see it?” Then over the course of a quarter, they aggregate that to assess what’s happening on the ground and whether or not they’re seeing what they think are interesting companies in every company they look at. It’s a way of assessing whether or not Kleiner is truly the first, second or third call, or how relevant the firm is to the top companies that are getting funded today. While the ultimate truth to them is the eventual outcomes and win rate, this is a great top-of-funnel leading indicator. Links to the full podcast are in thread. Thank you to Carta, Synaptic, and PestoAI for supporting the show!
1:47

This week Mamoon Hamid and Ilya Fushman of Kleiner Perkins joined Erik Torenberg on Turpentine VC to make the case for the craftsman approach to venture capital, and it's a must-listen. Our top takeaways: 1. KP holds firm in their belief in the craft approach to VC, which isn’t too dissimilar from the approach 30 - 40 years ago. Of course they employ new tools and software that allows them to be better stewards of the capital they manage, but the craft of investing is still at the center of everything they do. Kleiner may never have more than 7 partners: less is more, and one team, one dream. And it all comes from the question of: “how do we make really good decisions?” That means all being based in the Bay Area, having in-person team meetings every Monday, and having those discussions as a team sitting around a table. 2. The team at Kleiner uses “majors and minors” to cover new waves or emerging domains with a small team. Because of their team size, it’s difficult for KP to spin up specialization. So they fall back on having partners with great fundamentals (majors), with the ability to shirt into emergent domains (minors). The minors allow partners to shift into the new trends bubbling up, like crypto, AI, and more. Mamoon and Ilya view their job as making sure they know what the new thing is, and being intentional and deliberate with their decisions to either lean deeper into it, because they believe that’s where real history-making companies will get built, or pass, because they think it’s a fad. 3. The process KP uses to determine how well they’re doing compared to other competing firms. Every Monday, they look at the prior week of Seed, Series A’s, and Series B’s that got done by their peer set. And they ask the following question for each company: “Did we see this one? Or did we not see it?” Then over the course of a quarter, they aggregate that to assess what’s happening on the ground and whether or not they’re seeing what they think are interesting companies in every company they look at. It’s a way of assessing whether or not Kleiner is truly the first, second or third call, or how relevant the firm is to the top companies that are getting funded today. While the ultimate truth to them is the eventual outcomes and win rate, this is a great top-of-funnel leading indicator. Links to the full podcast are in thread. Thank you to Carta, Synaptic, and PestoAI for supporting the show!

Turpentine

33,386 Aufrufe • vor 3 Jahren

I am extremely bullish on silver prices long term. There are clearly supply shortages in this market which has caused the increase in the silver prices from $30 last year to over $70 today. The issue is global silver mine supply. It is in decline. All of the mines in the world produced 900 million oz in 2015. In 2026 it will be about 820 million oz. Production is steadily declining. The best mines have been found and depleted. Meanwhile silver demand is still increasing. EVs, solar panels, electronics, Ai chips, etc. I have invested in physical silver, but I also invest in a few silver mining stocks. Most mining stocks are struggling just to maintain current silver production. The key is to find the companies that can increase production. My top silver stock in my portfolio is Aya Gold & Silver (ticker AYASF). The reason why is because this company is one of the few that can seriously increase it's production in the coming years. They are already producing 6 million oz of silver per year from their first mine, Zgounder. They mine at a cost of $20 per oz, they are selling their silver at over $70 per oz. That is over $50 per oz profit margins on 6 million oz. They are building their next mine, Boumadine, which is currently projected to produce 37 million oz AgEq in 2030. So this is a company that will increase revenue and profits by about 6x to 7x even if gold and silver prices remain at current levels. If gold and silver prices increase from here, the upside for AYASF is even higher. Here is a brief clip from an interview last week where the CEO, Benoit La Salle, walks through the numbers and the comparison to other silver miners. Benoit has built multiple mines in his career and he is doing it again with Aya (ticker AYASF). I will leave the link to the full interview in the replies below. This is just a brief clip. Bookmark this post. I will be posting about Aya regularly in the coming years.
2:16

I am extremely bullish on silver prices long term. There are clearly supply shortages in this market which has caused the increase in the silver prices from $30 last year to over $70 today. The issue is global silver mine supply. It is in decline. All of the mines in the world produced 900 million oz in 2015. In 2026 it will be about 820 million oz. Production is steadily declining. The best mines have been found and depleted. Meanwhile silver demand is still increasing. EVs, solar panels, electronics, Ai chips, etc. I have invested in physical silver, but I also invest in a few silver mining stocks. Most mining stocks are struggling just to maintain current silver production. The key is to find the companies that can increase production. My top silver stock in my portfolio is Aya Gold & Silver (ticker AYASF). The reason why is because this company is one of the few that can seriously increase it's production in the coming years. They are already producing 6 million oz of silver per year from their first mine, Zgounder. They mine at a cost of $20 per oz, they are selling their silver at over $70 per oz. That is over $50 per oz profit margins on 6 million oz. They are building their next mine, Boumadine, which is currently projected to produce 37 million oz AgEq in 2030. So this is a company that will increase revenue and profits by about 6x to 7x even if gold and silver prices remain at current levels. If gold and silver prices increase from here, the upside for AYASF is even higher. Here is a brief clip from an interview last week where the CEO, Benoit La Salle, walks through the numbers and the comparison to other silver miners. Benoit has built multiple mines in his career and he is doing it again with Aya (ticker AYASF). I will leave the link to the full interview in the replies below. This is just a brief clip. Bookmark this post. I will be posting about Aya regularly in the coming years.

Wall Street Mav

64,431 Aufrufe • vor 4 Monaten

Conor Neill's 3-minute daily challenge to become a better speaker (backed by research): "What does it take to achieve world-class? As a musician? As a sportsman? As a speaker? Is it talent? The answer is no. The answer is 10,000 hours of practice." Conor explains: "No matter where you start from, no matter how talented a kid was in school, it's the kid that practices 8 hours a day that's now playing as a soloist with the top orchestras of the world. Leo Messi wasn't born with a better right knee and a better left foot than anyone else. It's what he chose to do every day after school, every weekend, that means he is where he is today." He shares a study that proves quantity beats perfectionism: "There was a pottery class in the United States. The teacher took half the class and said, 'Your grade will be based on the weight of pots you create.' To the other half, she said, 'You'll be evaluated on your one most finished piece.' The group measured on weight, they produced 5, 6, 7 pots a day. They tried random experiments, weird things, different ideas. Their hands were on clay from the first moment. The group evaluated on the most finished pot? They read, they thought, they talked about concepts, they went to galleries, and the first time they touched clay was 3 days before the end of class. All of the best pots were made by the group that focused on weight." He gives a simple challenge: "Every laptop comes with a webcam. Spend 3 minutes every day for the next 10 days. Record yourself. Explain a product. Explain your CV. Explain your vision. Try random things because no one else has to see it." Conor concludes: "No amount of watching YouTube videos of Bill Clinton is going to turn you into Bill Clinton. No amount of reading is going to turn you into a writer. Only by speaking do you turn into a speaker."
6:24

Conor Neill's 3-minute daily challenge to become a better speaker (backed by research): "What does it take to achieve world-class? As a musician? As a sportsman? As a speaker? Is it talent? The answer is no. The answer is 10,000 hours of practice." Conor explains: "No matter where you start from, no matter how talented a kid was in school, it's the kid that practices 8 hours a day that's now playing as a soloist with the top orchestras of the world. Leo Messi wasn't born with a better right knee and a better left foot than anyone else. It's what he chose to do every day after school, every weekend, that means he is where he is today." He shares a study that proves quantity beats perfectionism: "There was a pottery class in the United States. The teacher took half the class and said, 'Your grade will be based on the weight of pots you create.' To the other half, she said, 'You'll be evaluated on your one most finished piece.' The group measured on weight, they produced 5, 6, 7 pots a day. They tried random experiments, weird things, different ideas. Their hands were on clay from the first moment. The group evaluated on the most finished pot? They read, they thought, they talked about concepts, they went to galleries, and the first time they touched clay was 3 days before the end of class. All of the best pots were made by the group that focused on weight." He gives a simple challenge: "Every laptop comes with a webcam. Spend 3 minutes every day for the next 10 days. Record yourself. Explain a product. Explain your CV. Explain your vision. Try random things because no one else has to see it." Conor concludes: "No amount of watching YouTube videos of Bill Clinton is going to turn you into Bill Clinton. No amount of reading is going to turn you into a writer. Only by speaking do you turn into a speaker."

Jaynit

17,919 Aufrufe • vor 5 Monaten

Elon Musk once said that the SpaceX team had no idea how to make a rocket when they started out. That's not a bug but rather a feature of Elon's method: ground anything you make in first-principles thinking instead of relying on other people's way of doing or ‘good practices’ you don't understand yourself. Only then will you be able to achieve levels of innovation and manufacturing efficiency that everyone can observe at Tesla or SpaceX. However, this isn't the easy path, among others because you will need to learn from your mistakes and shortcomings on the fly. Here's Elon Musk explaining that Tesla was only able to overcome these through hard work as they were scaling up the Model 3 production, their first real mass-production vehicle. “We basically messed up almost every aspect of the Model 3 production line, from cells to packs to drive inverters, motors, body line, the paint shop, final assembly—everything was messed up. And I lived in the Fremont and Nevada factories for three years, fixing that production line, running around like a maniac through every part of that factory. Living with the team, I slept on the floor so that the team, who was going through a hard time, could see me on the floor. They knew that I was not in some ivory tower. Whatever pain they experienced, I had it more.” From: TED Interview, April 14, 2022
0:34

Elon Musk once said that the SpaceX team had no idea how to make a rocket when they started out. That's not a bug but rather a feature of Elon's method: ground anything you make in first-principles thinking instead of relying on other people's way of doing or ‘good practices’ you don't understand yourself. Only then will you be able to achieve levels of innovation and manufacturing efficiency that everyone can observe at Tesla or SpaceX. However, this isn't the easy path, among others because you will need to learn from your mistakes and shortcomings on the fly. Here's Elon Musk explaining that Tesla was only able to overcome these through hard work as they were scaling up the Model 3 production, their first real mass-production vehicle. “We basically messed up almost every aspect of the Model 3 production line, from cells to packs to drive inverters, motors, body line, the paint shop, final assembly—everything was messed up. And I lived in the Fremont and Nevada factories for three years, fixing that production line, running around like a maniac through every part of that factory. Living with the team, I slept on the floor so that the team, who was going through a hard time, could see me on the floor. They knew that I was not in some ivory tower. Whatever pain they experienced, I had it more.” From: TED Interview, April 14, 2022

ELON CLIPS

12,587 Aufrufe • vor 4 Monaten

You can run, but can’t hide. “Several manufacturing process changes for BNT162b2 mRNA, including production scale, were introduced during development. The proposed commercial scale manufacturing process includes use of linearised plasmid DNA template for mRNA production, whereas in early development phases PCR-amplification of DNA template was used.” ••••••••••••••••••••••••••••••••••• This clip is really going to grind your gears. Watch as the Health Department perform more verbal gyrations than a Romanian gymnast. In the previous round of Senate estimates I asked the TGA about the lack of testing on the vaccines and then honed in on the change in production process that was used to make the vaccines. In manufacturing the process is the product. The vaccines produced by commercial process was never tested in humans. This is significant as plasmids were used in the commercial process. A plasmid is a small, circular, double-stranded DNA molecule. If the batches using plasmids to produce the mRNA were not filtrated properly then DNA would have entered human cells via transfection. The plasmids used in the vaccine contained the SV40 promoter that targets the cell nucleus. Given this risk it beggars belief the TGA did not perform genotoxicity testing. Especially since large particles were identified in the batches. As I point out, FOI 3390-11 shows 67 particles greater than 10 microns in one batch. This is too large for mRNA. Such large particles are more likely to be DNA, probably nucleus targeting plasmids. You will note that as I am exposing these dangers, Blair Comley interrupts my questioning by deflecting about my questioning. He would have received a message from TGA goons telling him to shut me up as I was exposing their negligence. Tony Lawler also has a crack about my prior question on endogenous reverse transcriptease enzymes, which do exist but which the TGA want to ignore knowing the massive risk they overlooked. As usual the chair accedes to their requests. The officials then again distract from the question by getting upset about my comment that their answers are inadequate. Make no mistake we are dealing with evil people when it comes to the Australian Health Department. They engaged in a massive psychological operation that caused the deaths of thousands of Australians and are now scrambling to cover their tracks. They must be held to account for their crimes. #auspol
2:19

You can run, but can’t hide. “Several manufacturing process changes for BNT162b2 mRNA, including production scale, were introduced during development. The proposed commercial scale manufacturing process includes use of linearised plasmid DNA template for mRNA production, whereas in early development phases PCR-amplification of DNA template was used.” ••••••••••••••••••••••••••••••••••• This clip is really going to grind your gears. Watch as the Health Department perform more verbal gyrations than a Romanian gymnast. In the previous round of Senate estimates I asked the TGA about the lack of testing on the vaccines and then honed in on the change in production process that was used to make the vaccines. In manufacturing the process is the product. The vaccines produced by commercial process was never tested in humans. This is significant as plasmids were used in the commercial process. A plasmid is a small, circular, double-stranded DNA molecule. If the batches using plasmids to produce the mRNA were not filtrated properly then DNA would have entered human cells via transfection. The plasmids used in the vaccine contained the SV40 promoter that targets the cell nucleus. Given this risk it beggars belief the TGA did not perform genotoxicity testing. Especially since large particles were identified in the batches. As I point out, FOI 3390-11 shows 67 particles greater than 10 microns in one batch. This is too large for mRNA. Such large particles are more likely to be DNA, probably nucleus targeting plasmids. You will note that as I am exposing these dangers, Blair Comley interrupts my questioning by deflecting about my questioning. He would have received a message from TGA goons telling him to shut me up as I was exposing their negligence. Tony Lawler also has a crack about my prior question on endogenous reverse transcriptease enzymes, which do exist but which the TGA want to ignore knowing the massive risk they overlooked. As usual the chair accedes to their requests. The officials then again distract from the question by getting upset about my comment that their answers are inadequate. Make no mistake we are dealing with evil people when it comes to the Australian Health Department. They engaged in a massive psychological operation that caused the deaths of thousands of Australians and are now scrambling to cover their tracks. They must be held to account for their crimes. #auspol

Gerard Rennick

48,569 Aufrufe • vor 1 Jahr

Elon Musk’s real claim about Optimus isn’t that it’s the biggest product ever made. It’s that the biggest product ever made is a tenth of it. Musk: “I think it will be 10 times bigger than the next biggest product ever made.” That isn’t a claim about robots. It’s a claim about every object humans have ever sold each other, and it only holds once you notice what they all had in common. Every product ever built shipped incomplete. The drill needs a hand. The car needs a driver. The phone needs someone awake and holding it. All of it was leverage, and leverage does nothing until something pushes on it. You were the missing part in every machine ever made. That was the design, not a flaw. Optimus is the first product that arrives with the operator already inside. Which is why the company that got there first was never going to be a robotics company. Musk: “Tesla is the only company with all the required ingredients.” The mind and the body were never built by the same people. Labs that can think can’t stamp metal. Companies that can stamp metal can’t think. Tesla spent a decade mass-producing a machine that already has to see, decide, and move through a world full of people. Then priced it for a driveway instead of a defense contract. The robot is that machine with the road taken away. Musk: “You should be able to ask them to do things naturally.” A machine that has to be programmed sells to the people who can program. A machine you can talk to sells to anyone with something they’d rather not be doing. Every product in history was priced against another product. A better phone. A faster car. A cheaper tool. This one is priced against a wage. Wages are the largest line item on earth and the only cost technology has never driven down. Labor is the only thing anyone has ever sold that the seller never gets back. Every wage ever paid was a life billed by the hour. Musk: “Nothing will even be close.” Nothing else is close because everything else was capped by buyers. Demand for a product ends when everyone who wanted one has it. Demand for work doesn’t end. The ceiling was never how many people would buy it. It was how much there is to do, and there has never been a shortage of that. Only of hands. The prediction isn’t that Optimus succeeds. It’s that if it does, it enters the oldest and largest market on earth, one that has never had a competitor. Every product ever made sold you a better way to spend your hours. This one sells the hours back.
0:54

Elon Musk’s real claim about Optimus isn’t that it’s the biggest product ever made. It’s that the biggest product ever made is a tenth of it. Musk: “I think it will be 10 times bigger than the next biggest product ever made.” That isn’t a claim about robots. It’s a claim about every object humans have ever sold each other, and it only holds once you notice what they all had in common. Every product ever built shipped incomplete. The drill needs a hand. The car needs a driver. The phone needs someone awake and holding it. All of it was leverage, and leverage does nothing until something pushes on it. You were the missing part in every machine ever made. That was the design, not a flaw. Optimus is the first product that arrives with the operator already inside. Which is why the company that got there first was never going to be a robotics company. Musk: “Tesla is the only company with all the required ingredients.” The mind and the body were never built by the same people. Labs that can think can’t stamp metal. Companies that can stamp metal can’t think. Tesla spent a decade mass-producing a machine that already has to see, decide, and move through a world full of people. Then priced it for a driveway instead of a defense contract. The robot is that machine with the road taken away. Musk: “You should be able to ask them to do things naturally.” A machine that has to be programmed sells to the people who can program. A machine you can talk to sells to anyone with something they’d rather not be doing. Every product in history was priced against another product. A better phone. A faster car. A cheaper tool. This one is priced against a wage. Wages are the largest line item on earth and the only cost technology has never driven down. Labor is the only thing anyone has ever sold that the seller never gets back. Every wage ever paid was a life billed by the hour. Musk: “Nothing will even be close.” Nothing else is close because everything else was capped by buyers. Demand for a product ends when everyone who wanted one has it. Demand for work doesn’t end. The ceiling was never how many people would buy it. It was how much there is to do, and there has never been a shortage of that. Only of hands. The prediction isn’t that Optimus succeeds. It’s that if it does, it enters the oldest and largest market on earth, one that has never had a competitor. Every product ever made sold you a better way to spend your hours. This one sells the hours back.

Dustin

49,627 Aufrufe • vor 24 Tagen

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.
1:05

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.

Milk Road AI

16,781 Aufrufe • vor 4 Monaten

Everything Elon said about Optimus on the Q4 2024 earnings call: ⦿ I see a path for Tesla to be the most valuable company in the world, possibly bigger than the next five companies combined, overwhelmingly due to autonomous vehicles and autonomous humanoid robots. ⦿ The training compute needed for Optimus will ultimately probably be 10× what is needed for cars. Humanoids likely have 1,000× more useS than a car, which doesn't mean training scales by 1,000×, but probably close to 10×. The training compute will scale progressively as Optimus becomes more productive. ⦿ Long-term, Optimus has the potential to generate $10 trillion in revenue. In that scenario, we can support a lot of training compute. Even $500 billion in training compute is a good deal (chuckles). ⦿ There's a lot of uncertainty with timing because several aspects are being iterated simultaneously. The internal plan is for roughly 10,000 robots to be built this year, but we'll more likely produce several thousand. ⦿ I'm confident those several thousand robots will be able to do useful things. ⦿ The lessons from Production V1 will inform the changes in Production V2, which we expect to launch around mid-next year. ⦿ Our goal, aspirationally, is to ramp 10× every year, but perhaps we end up with 5× growth per year. With that kind of growth, it won't be many years before we're making 100 million robots a year. ⦿ The off-the-shelf components didn't work well, so we had to design everything in-house, including the most sophisticated hand ever made. Optimus will be able to play a piano and thread a needle. ⦿ My long-term prediction is that Optimus will overwhelmingly be the value of the company. ⦿ Optimus is not design-locked. It is rapidly evolving in a good direction. Tesla has by far the best humanoid robotics engineers in the world. Tesla also has all the other necessary ingredients: battery pack, power electronics, charging, communications, real-world AI, and the ability to scale production. ⦿ What other companies are missing is real-world AI and the ability to scale to millions of units a year. ⦿ This year, we aim to use Optimus internally at Tesla. We can easily use several thousand robots at Tesla for repetitive tasks, such as loading sheet metal at the welding line. ⦿ The Production V1 line is roughly 1,000 units per month. The Production V2, launching around mid-next year, will be for 10k units per month. The line after that will be for 100k units a month. Of course, it takes time for any given line to reach its maximum potential. ⦿ A very rough guess: we'll start delivering Optimus to companies outside of Tesla in the second half of 2026. The ramp is going to be exponential, and demand will not be a problem. ⦿ Once we're above 1 million units per year, the production cost of Optimus will be less than $20,000. Its total mass and complexity are much lower than a car. At a similar production volume to the Model Y, Optimus should be about half the cost of a Model Y. ⦿ The price is a different matter than cost. The price of Optimus will be set by market demand. [This is by far the longest Elon has ever spent discussing Optimus on an earnings call.]
12:28

Everything Elon said about Optimus on the Q4 2024 earnings call: ⦿ I see a path for Tesla to be the most valuable company in the world, possibly bigger than the next five companies combined, overwhelmingly due to autonomous vehicles and autonomous humanoid robots. ⦿ The training compute needed for Optimus will ultimately probably be 10× what is needed for cars. Humanoids likely have 1,000× more useS than a car, which doesn't mean training scales by 1,000×, but probably close to 10×. The training compute will scale progressively as Optimus becomes more productive. ⦿ Long-term, Optimus has the potential to generate $10 trillion in revenue. In that scenario, we can support a lot of training compute. Even $500 billion in training compute is a good deal (chuckles). ⦿ There's a lot of uncertainty with timing because several aspects are being iterated simultaneously. The internal plan is for roughly 10,000 robots to be built this year, but we'll more likely produce several thousand. ⦿ I'm confident those several thousand robots will be able to do useful things. ⦿ The lessons from Production V1 will inform the changes in Production V2, which we expect to launch around mid-next year. ⦿ Our goal, aspirationally, is to ramp 10× every year, but perhaps we end up with 5× growth per year. With that kind of growth, it won't be many years before we're making 100 million robots a year. ⦿ The off-the-shelf components didn't work well, so we had to design everything in-house, including the most sophisticated hand ever made. Optimus will be able to play a piano and thread a needle. ⦿ My long-term prediction is that Optimus will overwhelmingly be the value of the company. ⦿ Optimus is not design-locked. It is rapidly evolving in a good direction. Tesla has by far the best humanoid robotics engineers in the world. Tesla also has all the other necessary ingredients: battery pack, power electronics, charging, communications, real-world AI, and the ability to scale production. ⦿ What other companies are missing is real-world AI and the ability to scale to millions of units a year. ⦿ This year, we aim to use Optimus internally at Tesla. We can easily use several thousand robots at Tesla for repetitive tasks, such as loading sheet metal at the welding line. ⦿ The Production V1 line is roughly 1,000 units per month. The Production V2, launching around mid-next year, will be for 10k units per month. The line after that will be for 100k units a month. Of course, it takes time for any given line to reach its maximum potential. ⦿ A very rough guess: we'll start delivering Optimus to companies outside of Tesla in the second half of 2026. The ramp is going to be exponential, and demand will not be a problem. ⦿ Once we're above 1 million units per year, the production cost of Optimus will be less than $20,000. Its total mass and complexity are much lower than a car. At a similar production volume to the Model Y, Optimus should be about half the cost of a Model Y. ⦿ The price is a different matter than cost. The price of Optimus will be set by market demand. [This is by far the longest Elon has ever spent discussing Optimus on an earnings call.]

The Humanoid Hub

96,475 Aufrufe • vor 1 Jahr

"If you are not working 7 days per week, you are going to lose". Corgi Insurance is the most intense workplace culture in startups. - The company works 7 days per week. - Founder (nico laqua) lives and sleeps in the office. - He built a cafe in the office because there was no local cafe that was open 24/7. - 2/3 of the first 30 team members have the Corgi logo as a tattoo. Today I went behind the scenes with Nico, who has used this culture to scale the company to a $2.6BN valuation in just two years. My condensed notes below: 1. If You Are Not Working 7 Days Per Week, You Are Going to Lose: Whatever you can get done in 5 days, you'll get more done in 6 and 7. If you are trying to solve the world’s hardest problems, a standard 5-day workweek will not cut it. 2. Work Trials Repel the Mediocre: Corgi forces candidates into mock work trials over the weekend. If seeing a full office on a Saturday scares them, they don't belong. True intensity acts as a natural filter to attract killers and repel clock-watchers. 3. Lead from the Front Lines You can’t demand 7-day weeks while sitting on a yacht. Nico sleeps 3–4 hours a night on a mattress inside the office. If you want your troops to bleed, you have to be in the trenches with them. 4. Culture Only Means One Thing: Winning Forget superficial jargon like "hackers" or "ex-founders." Strip away the corporate fluff. A great startup culture is aggressively optimized around one single word: Winning. 5. Lifespan vs. Victories Building something world-historic requires radical sacrifice. When asked if he'd rather build a trillion-dollar company and die at 50, or fail and live to 80, the answer was easy. "I would rather measure my lifespan in victories." 6. Reject the Comfort of "Quiet Quitting." If you are operating in a hyper-growth environment and your days off happen to be Saturday and Sunday every single week, you are quiet quitting. To win, you must deliberately bypass the off-ramps of personal comfort and low volatility. Corgi isn't for everyone—and that’s exactly the point.
0:31

"If you are not working 7 days per week, you are going to lose". Corgi Insurance is the most intense workplace culture in startups. - The company works 7 days per week. - Founder (nico laqua) lives and sleeps in the office. - He built a cafe in the office because there was no local cafe that was open 24/7. - 2/3 of the first 30 team members have the Corgi logo as a tattoo. Today I went behind the scenes with Nico, who has used this culture to scale the company to a $2.6BN valuation in just two years. My condensed notes below: 1. If You Are Not Working 7 Days Per Week, You Are Going to Lose: Whatever you can get done in 5 days, you'll get more done in 6 and 7. If you are trying to solve the world’s hardest problems, a standard 5-day workweek will not cut it. 2. Work Trials Repel the Mediocre: Corgi forces candidates into mock work trials over the weekend. If seeing a full office on a Saturday scares them, they don't belong. True intensity acts as a natural filter to attract killers and repel clock-watchers. 3. Lead from the Front Lines You can’t demand 7-day weeks while sitting on a yacht. Nico sleeps 3–4 hours a night on a mattress inside the office. If you want your troops to bleed, you have to be in the trenches with them. 4. Culture Only Means One Thing: Winning Forget superficial jargon like "hackers" or "ex-founders." Strip away the corporate fluff. A great startup culture is aggressively optimized around one single word: Winning. 5. Lifespan vs. Victories Building something world-historic requires radical sacrifice. When asked if he'd rather build a trillion-dollar company and die at 50, or fail and live to 80, the answer was easy. "I would rather measure my lifespan in victories." 6. Reject the Comfort of "Quiet Quitting." If you are operating in a hyper-growth environment and your days off happen to be Saturday and Sunday every single week, you are quiet quitting. To win, you must deliberately bypass the off-ramps of personal comfort and low volatility. Corgi isn't for everyone—and that’s exactly the point.

Harry Stebbings

3,120,458 Aufrufe • vor 2 Monaten

Mo Gawdat spent years inside the machine at Google X. Now he is saying out loud what the economists will not. Gawdat: “The very base of capitalism, which is labor arbitrage, to hire you for a dollar and then sell what you make for two, is going to disappear.” That is not a prediction. That is a coroner’s report on a system that has not stopped breathing yet. Capitalism was never about innovation. It was about one equation. Buy human time cheap. Sell the output high. Pocket the spread. Every empire. Every fortune. Every supply chain on Earth was built on that margin. AI just closed it to zero. A humanoid robot now costs $9,000. It does not sleep. It does not negotiate. It does not quit. It runs every hour of every day at a quality ceiling no biological worker will ever touch. When production costs fall to nearly nothing, the entire pricing structure of the global economy falls with it. But here is what every CEO celebrating margin expansion has not thought through for five minutes. Gawdat: “Even if you can have all of the productivity gains in the world, by firing people consistently, nobody’s able to buy what you’re making.” That single sentence should end every strategy meeting on the planet. Capitalism is a closed loop. You pay workers. Workers become consumers. Consumers buy products. Revenue funds the next payroll. Cut the worker and you do not just eliminate a cost. You eliminate the customer. Every company racing to automate headcount out of existence is quietly engineering the death of its own demand. They are building the most efficient production systems in human history to sell to a population that no longer has income. 50% unemployment is not a recession. It is the demand side of the economy going permanently dark. You cannot push infinite supply into zero purchasing power. The math does not care about your earnings call. Gawdat: “Wealth is going to have very little meaning for most of us in a few years’ time.” This is where it turns on the people who think they are winning. If production approaches zero cost, scarcity begins to dissolve. And scarcity is the only reason money holds value in the first place. The billionaire class is stockpiling a currency that is quietly losing its reason to exist. Gawdat: “So the entire capitalist model has to be rethought.” He is right. And nobody in power is doing the rethinking. Every board meeting about efficiency is a conversation about dismantling the very economic engine that made the board meeting possible. The question was never whether AI could produce enough. It was whether capitalism could survive its own success. The machine does not just replace the worker. It erases the consumer. And a system that can produce everything but sell nothing is not an economy. It is a machine that perfected itself into extinction.
1:59

Mo Gawdat spent years inside the machine at Google X. Now he is saying out loud what the economists will not. Gawdat: “The very base of capitalism, which is labor arbitrage, to hire you for a dollar and then sell what you make for two, is going to disappear.” That is not a prediction. That is a coroner’s report on a system that has not stopped breathing yet. Capitalism was never about innovation. It was about one equation. Buy human time cheap. Sell the output high. Pocket the spread. Every empire. Every fortune. Every supply chain on Earth was built on that margin. AI just closed it to zero. A humanoid robot now costs $9,000. It does not sleep. It does not negotiate. It does not quit. It runs every hour of every day at a quality ceiling no biological worker will ever touch. When production costs fall to nearly nothing, the entire pricing structure of the global economy falls with it. But here is what every CEO celebrating margin expansion has not thought through for five minutes. Gawdat: “Even if you can have all of the productivity gains in the world, by firing people consistently, nobody’s able to buy what you’re making.” That single sentence should end every strategy meeting on the planet. Capitalism is a closed loop. You pay workers. Workers become consumers. Consumers buy products. Revenue funds the next payroll. Cut the worker and you do not just eliminate a cost. You eliminate the customer. Every company racing to automate headcount out of existence is quietly engineering the death of its own demand. They are building the most efficient production systems in human history to sell to a population that no longer has income. 50% unemployment is not a recession. It is the demand side of the economy going permanently dark. You cannot push infinite supply into zero purchasing power. The math does not care about your earnings call. Gawdat: “Wealth is going to have very little meaning for most of us in a few years’ time.” This is where it turns on the people who think they are winning. If production approaches zero cost, scarcity begins to dissolve. And scarcity is the only reason money holds value in the first place. The billionaire class is stockpiling a currency that is quietly losing its reason to exist. Gawdat: “So the entire capitalist model has to be rethought.” He is right. And nobody in power is doing the rethinking. Every board meeting about efficiency is a conversation about dismantling the very economic engine that made the board meeting possible. The question was never whether AI could produce enough. It was whether capitalism could survive its own success. The machine does not just replace the worker. It erases the consumer. And a system that can produce everything but sell nothing is not an economy. It is a machine that perfected itself into extinction.

Dustin

116,769 Aufrufe • vor 4 Monaten

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.
0:53

Jonathan Ross just revealed why AI companies aren’t growing faster. Not demand. Not competition. Physics. Ross: “The demand for compute is insatiable.” There isn’t enough compute in the world. Not a temporary shortage. A fundamental gap between what the market wants and what the infrastructure can deliver. Ross: “Right now, one of the biggest complaints of Anthropic is the rate limits. People can’t get enough tokens.” Rate limits aren’t product decisions. They’re rationing. Companies forced to regulate access because infrastructure cannot meet demand. Slower services. Token caps. The only things standing between these companies and a revenue surge they can’t access. Every token cap is a revenue cap. Every slowdown is a sale that didn’t happen. Ross: “If Anthropic was given twice the inference compute, within one month their revenue would almost double.” Read that again. Double the compute. Double the revenue. Within thirty days. That’s not a growth projection. That’s a measurement of how deep the backlog already is. The demand exists right now. It’s sitting in a queue. The only thing between these companies and that revenue is physical hardware they don’t have. This breaks every assumption about how tech companies scale. Usually you scale by finding customers. AI companies have infinite customers. They scale by finding hardware. The constraint isn’t market fit. It isn’t distribution. It isn’t competition. It’s processing power. This is why Jensen Huang is the most important person in the world right now. NVIDIA doesn’t just make chips. It makes the thing every government, every AI lab, and every company racing for this future needs more of and can’t get enough of. The compute bottleneck isn’t a tech industry problem. It’s a civilizational one. The winner of this era isn’t determined by who builds the smartest model. Every major lab has a frontier model. The winner is whoever secures the most compute fastest while everyone else rations what’s left. The race isn’t for intelligence. It’s for infrastructure. And right now there isn’t enough to go around.

Dustin

28,395 Aufrufe • vor 6 Monaten

Elon Musk just told the world his plan to own the one thing every AI on Earth depends on. The silicon. The entire AI industry is fighting over the same layer. Models. Parameters. Data. Benchmarks. All of it runs on chips none of them produce. Every frontier lab on the planet is building intelligence on a foundation they do not control, in a country they cannot influence, on an island they could not defend. Musk: “You design a chip, you fabricate the chip, you test the chip, you redesign the chip, and you fabricate it again. All under one roof.” That’s the Terafab. One building in Austin, Texas. No wafers shipped across the Pacific. No six-month tape-out cycles. No single point of geopolitical failure sitting in the Taiwan Strait. Total vertical sovereignty over silicon. But sovereignty is not the endgame. Sovereignty is what makes the endgame possible. Speed. Every fab on Earth optimizes for yield. For cost. For predictable output at massive scale. The Terafab optimizes for one variable only. Learning speed. That distinction will define the next era of compute. When your iteration cycle compresses from months to days, perfection on the first attempt becomes irrelevant. What matters is how fast you reach the fiftieth. That is the exact principle that made SpaceX untouchable. They did not build a superior rocket on the first try. They built a system where failure was cheap and iteration was relentless. And that system produced something no one else could match. Now transplant that into semiconductors. Musk: “New physics. Wild and crazy things.” He is not trying to out-manufacture TSMC. He is trying to make TSMC’s entire model a relic of a slower era. TSMC cannot take radical bets on unproven architecture. Their customers demand predictability. Their margins demand stability. Their entire empire is built on perfecting what already works. Elon’s model is built on trying what has never worked. Repeatedly. At near-zero cost. Until it does. When the cost of failure approaches zero, breakthroughs stop being accidents. They become mathematically inevitable. Now add the layer that makes this permanent. xAI builds frontier AI. That AI assists in chip design. Better chips accelerate the AI. Faster AI designs better chips. That is not a production line. That is a compounding feedback loop with no external dependency. And nobody else on Earth can run it. Intel has fabs but no frontier AI. NVIDIA designs but does not fabricate. TSMC fabricates but does not design. Google designs but outsources production. Every one of them has a structural gap they cannot close. Elon is closing his. The AI that architects the chip. The fab that forges it. The vehicles and robots that run on it. The data they generate. The AI that trains on that data to design the next generation. Full circle. No seams. No permission required from any government, company, or supply chain on Earth. That is not a factory. That is a self-improving system with a physical body. A structure that manufactures upgrades to its own capacity to think. That has never existed before. Not as a concept. Not as a metaphor. As a building. Every era of human civilization was defined by whoever mastered its most critical substrate. Land built empires. Iron built armies. Oil built superpowers. The next substrate is compute. And a single facility in Austin is being designed to produce it in a closed loop that no competitor can replicate, no government can embargo, and no market force can interrupt. That is not a factory announcement. That is the foundation of the first self-reinforcing intelligence monopoly in the history of this species. And the loop only needs to start once.
0:44

Elon Musk just told the world his plan to own the one thing every AI on Earth depends on. The silicon. The entire AI industry is fighting over the same layer. Models. Parameters. Data. Benchmarks. All of it runs on chips none of them produce. Every frontier lab on the planet is building intelligence on a foundation they do not control, in a country they cannot influence, on an island they could not defend. Musk: “You design a chip, you fabricate the chip, you test the chip, you redesign the chip, and you fabricate it again. All under one roof.” That’s the Terafab. One building in Austin, Texas. No wafers shipped across the Pacific. No six-month tape-out cycles. No single point of geopolitical failure sitting in the Taiwan Strait. Total vertical sovereignty over silicon. But sovereignty is not the endgame. Sovereignty is what makes the endgame possible. Speed. Every fab on Earth optimizes for yield. For cost. For predictable output at massive scale. The Terafab optimizes for one variable only. Learning speed. That distinction will define the next era of compute. When your iteration cycle compresses from months to days, perfection on the first attempt becomes irrelevant. What matters is how fast you reach the fiftieth. That is the exact principle that made SpaceX untouchable. They did not build a superior rocket on the first try. They built a system where failure was cheap and iteration was relentless. And that system produced something no one else could match. Now transplant that into semiconductors. Musk: “New physics. Wild and crazy things.” He is not trying to out-manufacture TSMC. He is trying to make TSMC’s entire model a relic of a slower era. TSMC cannot take radical bets on unproven architecture. Their customers demand predictability. Their margins demand stability. Their entire empire is built on perfecting what already works. Elon’s model is built on trying what has never worked. Repeatedly. At near-zero cost. Until it does. When the cost of failure approaches zero, breakthroughs stop being accidents. They become mathematically inevitable. Now add the layer that makes this permanent. xAI builds frontier AI. That AI assists in chip design. Better chips accelerate the AI. Faster AI designs better chips. That is not a production line. That is a compounding feedback loop with no external dependency. And nobody else on Earth can run it. Intel has fabs but no frontier AI. NVIDIA designs but does not fabricate. TSMC fabricates but does not design. Google designs but outsources production. Every one of them has a structural gap they cannot close. Elon is closing his. The AI that architects the chip. The fab that forges it. The vehicles and robots that run on it. The data they generate. The AI that trains on that data to design the next generation. Full circle. No seams. No permission required from any government, company, or supply chain on Earth. That is not a factory. That is a self-improving system with a physical body. A structure that manufactures upgrades to its own capacity to think. That has never existed before. Not as a concept. Not as a metaphor. As a building. Every era of human civilization was defined by whoever mastered its most critical substrate. Land built empires. Iron built armies. Oil built superpowers. The next substrate is compute. And a single facility in Austin is being designed to produce it in a closed loop that no competitor can replicate, no government can embargo, and no market force can interrupt. That is not a factory announcement. That is the foundation of the first self-reinforcing intelligence monopoly in the history of this species. And the loop only needs to start once.

Dustin

47,490 Aufrufe • vor 1 Monat

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."
1:27

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

TBPN

135,588 Aufrufe • vor 6 Monaten

This is one of the best comebacks you’ll ever see. Proof that you should never give up on life… Little Koda who shouldn’t be here today but who now has made one of the greatest recoveries ever and even has a forever family thanks to the wonderful people in Little Steps Matter. The transformation is really like something out of a movie. I was lucky enough to visit them recently and see first hand the incredible work they do in Bali. What I loved about their operation was they focus a lot of the activity on the streets helping the dogs with food and sterilization. One of my big goals this year is to help use my reach to raise up other people around the world and I could 100% see the compassion and kindness the team there were giving out to the dogs. If you want uplifting content and people who are changing the world one dog at a time they are ones to follow. Watch this space for more exciting things in the future with them. Only through working together with other people around the world can this street dog problem truly be solved at scale. You’ll find them here
1:29

This is one of the best comebacks you’ll ever see. Proof that you should never give up on life… Little Koda who shouldn’t be here today but who now has made one of the greatest recoveries ever and even has a forever family thanks to the wonderful people in Little Steps Matter. The transformation is really like something out of a movie. I was lucky enough to visit them recently and see first hand the incredible work they do in Bali. What I loved about their operation was they focus a lot of the activity on the streets helping the dogs with food and sterilization. One of my big goals this year is to help use my reach to raise up other people around the world and I could 100% see the compassion and kindness the team there were giving out to the dogs. If you want uplifting content and people who are changing the world one dog at a time they are ones to follow. Watch this space for more exciting things in the future with them. Only through working together with other people around the world can this street dog problem truly be solved at scale. You’ll find them here

Niall Harbison

415,675 Aufrufe • vor 2 Jahren

Listening to people complain about the Tesla Robotaxi rollout reminds me of that scene in Idiocracy where President Camacho promises everyone that Not Sure (Ashok) is going to fix all the country's problems, and do it all in one week. It turns out that the economy is collapsing because people have been watering the crops with Gatorade instead of water. They switch back to water, but by the time the week is over Not Sure is fighting for his life roman gladiator style in a giant colosseum. Then people realize that the crops actually *are* growing with the water, and his life is saved just in time. I remember a time when the Tesla community was a powerful force in pushing back against naysayers. When a chorus of haters and short sellers said "Model 3 production is too slow. They will never reach 5,000 a week. They will never reach profitability" it was the Tesla community that pushed back and said they believed in the company. And sure enough the company did eventually scale up Model 3 and Y, did eventually hit profitability, and did eventually hit their targets even if they missed them a few times first. The Tesla community saw that because they understood the company in a way the naysayers didn't. Now, many of those some people have become the naysayers. I can't blame them completely. A lot of the people who understood EVs and the auto industry very well are left scratching their head trying to understand AI, autonomy, and robotaxis. You've got a bunch of options traders who are not AI experts but bought options that are expiring now, hoping Robotaxi would have scaled further. Just like the people in Idiocracy they have good reason to be upset — they're hungry, and the crops aren't growing. But just like the naysayers were wrong about Tesla being unable to scale the Model 3 & Y, today's naysayers couldn't be more wrong about Tesla being unable to scale Robotaxi. At this point they have hundreds of fully driverless cars registered in 6 cities. This is the first time this has ever been done with pure computer vision. How many announcements and signs of progress can you dismiss before you finally admit what is happening? This is not a linear thing. The first step in deploying Robotaxis is to put a few on the road and see how they work. And no, it's not just going to be perfect out of the box on the first day. There are going to be issues you couldn't forsee, unique problems in new areas, and improvements to be made. But once you can do 200 driverless cars safely, it doesn't take much more to scale to 1,000 and beyond. Just look at last week in Texas where we went from 100 to 175 cars registered in a 24 hour period. The number increased slowly, then all at once in one big jump. That is how exponential growth works. If you're looking at the initial growth and trying to extrapolate that out linearly, you're going to be way off. It feels like everyone is so focused on Elon not solving everything in one week like he promised that they're missing the fact that it is actually working. The crops actually are starting to grow. How are people not seeing the progress of FSD 14.3.6, FSD 14 lite, and all the new cities Robotaxi is entering? Remember when driving a car with pure vision, without LIDAR or Radar, was supposed to be impossible? That's the magic of Elon Musk. He takes what is supposed to be impossible — a reusable spaceship, generalized self-driving cars at scale — and makes what's supposed to be impossible feel like it's actually late. When he starts on a project, they say it can never be done. By the time he finishes, people are asking why they didn't finish it faster. Say what you will, but the Tesla team is absolutely executing right now. They are killing it. I can't be the only one that sees the signs. Everything I've seen tells me that they are going to scale this. As they add more training compute, as the data set grows larger, as the fleet crosses 10 million cars, as they ship FSD 15 and their AI5 and AI6 chips, the cars are just going to keep getting smarter and smarter. The capabilities of these autonomous vehicles are already starting to cross into the realm of superhuman, and they're not stopping there. If you want to point out every stumble, every missed deadline, every limitation in the current Robotaxi service, that's your prerogative. You have the right to say whatever you want. But I think that as we look back on this time, these people are going to look extremely dumb in hindsight. When autonomous vehicles blanket the globe, we'll ask how people didn't see it earlier as the service expanded into 7 markets. This company has nearly 10 million cars on the road. More than 12 billion FSD miles have been driven around the globe. And international expansion is just starting. This is the company that developed Cybercab to be as dead simple as possible. To not require any paint. They built a vehicle that can truly bring generalized autonomy to the masses around the world. I can't believe how many people are unable to see it. Based on everything I'm seeing, I've never had more conviction that this technology is going to change the world. AI is beginning to reach out of our computer and into the physical world, and Tesla has a central role to play in making that a reality.
3:22

Listening to people complain about the Tesla Robotaxi rollout reminds me of that scene in Idiocracy where President Camacho promises everyone that Not Sure (Ashok) is going to fix all the country's problems, and do it all in one week. It turns out that the economy is collapsing because people have been watering the crops with Gatorade instead of water. They switch back to water, but by the time the week is over Not Sure is fighting for his life roman gladiator style in a giant colosseum. Then people realize that the crops actually *are* growing with the water, and his life is saved just in time. I remember a time when the Tesla community was a powerful force in pushing back against naysayers. When a chorus of haters and short sellers said "Model 3 production is too slow. They will never reach 5,000 a week. They will never reach profitability" it was the Tesla community that pushed back and said they believed in the company. And sure enough the company did eventually scale up Model 3 and Y, did eventually hit profitability, and did eventually hit their targets even if they missed them a few times first. The Tesla community saw that because they understood the company in a way the naysayers didn't. Now, many of those some people have become the naysayers. I can't blame them completely. A lot of the people who understood EVs and the auto industry very well are left scratching their head trying to understand AI, autonomy, and robotaxis. You've got a bunch of options traders who are not AI experts but bought options that are expiring now, hoping Robotaxi would have scaled further. Just like the people in Idiocracy they have good reason to be upset — they're hungry, and the crops aren't growing. But just like the naysayers were wrong about Tesla being unable to scale the Model 3 & Y, today's naysayers couldn't be more wrong about Tesla being unable to scale Robotaxi. At this point they have hundreds of fully driverless cars registered in 6 cities. This is the first time this has ever been done with pure computer vision. How many announcements and signs of progress can you dismiss before you finally admit what is happening? This is not a linear thing. The first step in deploying Robotaxis is to put a few on the road and see how they work. And no, it's not just going to be perfect out of the box on the first day. There are going to be issues you couldn't forsee, unique problems in new areas, and improvements to be made. But once you can do 200 driverless cars safely, it doesn't take much more to scale to 1,000 and beyond. Just look at last week in Texas where we went from 100 to 175 cars registered in a 24 hour period. The number increased slowly, then all at once in one big jump. That is how exponential growth works. If you're looking at the initial growth and trying to extrapolate that out linearly, you're going to be way off. It feels like everyone is so focused on Elon not solving everything in one week like he promised that they're missing the fact that it is actually working. The crops actually are starting to grow. How are people not seeing the progress of FSD 14.3.6, FSD 14 lite, and all the new cities Robotaxi is entering? Remember when driving a car with pure vision, without LIDAR or Radar, was supposed to be impossible? That's the magic of Elon Musk. He takes what is supposed to be impossible — a reusable spaceship, generalized self-driving cars at scale — and makes what's supposed to be impossible feel like it's actually late. When he starts on a project, they say it can never be done. By the time he finishes, people are asking why they didn't finish it faster. Say what you will, but the Tesla team is absolutely executing right now. They are killing it. I can't be the only one that sees the signs. Everything I've seen tells me that they are going to scale this. As they add more training compute, as the data set grows larger, as the fleet crosses 10 million cars, as they ship FSD 15 and their AI5 and AI6 chips, the cars are just going to keep getting smarter and smarter. The capabilities of these autonomous vehicles are already starting to cross into the realm of superhuman, and they're not stopping there. If you want to point out every stumble, every missed deadline, every limitation in the current Robotaxi service, that's your prerogative. You have the right to say whatever you want. But I think that as we look back on this time, these people are going to look extremely dumb in hindsight. When autonomous vehicles blanket the globe, we'll ask how people didn't see it earlier as the service expanded into 7 markets. This company has nearly 10 million cars on the road. More than 12 billion FSD miles have been driven around the globe. And international expansion is just starting. This is the company that developed Cybercab to be as dead simple as possible. To not require any paint. They built a vehicle that can truly bring generalized autonomy to the masses around the world. I can't believe how many people are unable to see it. Based on everything I'm seeing, I've never had more conviction that this technology is going to change the world. AI is beginning to reach out of our computer and into the physical world, and Tesla has a central role to play in making that a reality.

Whole Mars Catalog

96,272 Aufrufe • vor 1 Monat

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.
1:55

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.

Dustin

17,035 Aufrufe • vor 19 Tagen