Dwiki Pratama Putra @dwikiwiki

A self-taught programmer from Kazakhstan helped build a company that went from making $20 million a year to making $20 million a week. Jensen Huang made his first ever trip to Kazakhstan this week, and on the same day Nvidia announced it had invested in that company. It's called Higgsfield, and in this clip Jensen calls its founders some of the brightest people in the world. The programmer is Yerzat Dulat, and he runs the models and the training infrastructure. His co-founder Alex Mashrabov had already done this once before. He built AI Factory, the face animation tech behind Snapchat's Cameos, and sold it to Snap for $166 million. Then he ran generative AI at Snap and led the team behind My AI, which got to 150 million users. In late 2023 he texted a friend that he was leaving Snap to build something really big this time, and asked if he knew a great researcher to do it with. That's pretty much how it played out. Annualized revenue was about $20 million in August 2025, $200 million by New Year's, $700 million this August, and over $1 billion in September. Most of the engineering team is in Kazakhstan. Back in January they became the country's first ever unicorn at $1.3 billion, and by August DST Global was paying $5.4 billion. Now put that next to Cursor, which everybody calls the fastest growing software company ever. When Cursor crossed $1 billion in annualized revenue last November, investors valued it at $29.3 billion. Higgsfield crossed the same line with its last priced round at under a fifth of that. Most of that money now comes from companies, too. Businesses were less than a quarter of Higgsfield's revenue in January and they're the majority now. 390 of the Fortune 500 use it, and brands like Dollar Shave Club crank out batches of ad videos every single day, where they used to shoot one for a whole campaign. And Nvidia probably saw all of this coming before almost anyone. Every version of Higgsfield has run on Nvidia chips, from L40s through Hopper and Blackwell, with Vera Rubin up next, and the platform now pushes around 12 trillion tokens a month across 150 million generations. This wasn't a bet Jensen had to make blind. Kazakhstan spent the last month throwing a national AI Month to show the world it can build serious AI companies. Pretty good timing that the guy closing it out had just invested in one.

Higgsfield AI 🧩 @higgsfield

a day ago

Jensen Huang: “… the founders of Higgsfield are visionary in the work that they do … but these are some of the brightest people in the world.” We're grateful to Jensen and the teams at NVIDIA and TCV for believing in us. Both companies have joined Higgsfield as investors in

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One answer to what's driving GPU rental prices: Token demand on OpenRouter is up ~27x since last November, from ~6T/week to ~165T/week Open-weight models now handle ~72% of those tokens, up from ~28% last November Charts of the Week: a16z.news/p/charts-of-th…

a16z @a16z

a day ago

Zooming out, GPUs are still renting ahead of a standard 6-year depreciation schedule Rental price vs. schedule: - H200: +143% - B200: +111% - RTX PRO 6000: +73% - H100: +54% - A100: +9% Most of the gap opened up after April, when token demand took off Charts of the Week:

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看完山姆·奥特曼那篇《如何投资自己，并指数级成长》，我反复看了几遍。 不是被鸡汤打动，而是被他几句话扎到了。 他说复利是魔法，要让自己成为一条指数曲线。以前我总觉得自己很努力，每天都很忙。现在才明白，大部分努力其实是线性的——做一年和做三年差别不大。真正该投的是能产生杠杆的事：深度技能、个人品牌、人脉、能增值的资产。薪水是线性的，这些才是复利的。 他说最成功的人自信到近乎妄想。这点我听着刺耳，但结合自己想，确实是。不自信的人很难敢走别人都觉得离谱的路，而最大的价值往往就在那些逆势的选择里。自信要配自我觉察，否则容易变成自我欺骗。 他还说要让冒险变得容易，保持生活低成本和灵活。我以前总想先“稳定下来再冒险”，结果稳定成了惯性。他说得对，越晚越难离开舒适区，因为人会自动把生活方式匹配到更高的薪水上。 另外几点也让我记下来了： 所有伟大事业最终都变成“销售”工作，要能说服别人相信你相信的东西。 专注是倍增器，做对的事比做很多事更重要。 真正的财富来自拥有快速增值的东西，而不是高薪。 长期帮助他人建立的人脉，往往会在多年后带来意外回报。 看完之后我问自己： 我现在做的事，五年后会不会还只是线性增长？ 有没有哪一件事，值得我把其他的努力都当成注脚？ 这篇文章最大的价值不是给鸡汤，而是逼你重新审视自己的时间、技能和选择，到底有没有在复利。 你最近有在认真投资自己吗？有没有发现自己一直在做线性努力？ blog.samaltman.com/how-to-be-succ…

🚨 اختتام أسطوري لمؤتمر Nvidia GTC Berlin 2026: جينسن هوانغ يستعرض Hermes Agent في عرض خيالي يصعق الجميع! ​بكلمات قليلة وبـ "برومبت واحد فقط"، قام جينسن بتثبيت وإعداد نظام Hermes مخصص، ليختتم به مؤتمره بضربة مدوية توضح كيف يُبنى مستقبل الروبوتات الذكية: ​📌 تفاصيل العرض التقني المجنون: ​وكيل يبني وكيلاً: الطريقة الأفضل لبناء Agent هي أن تطلب من Agent آخر أن يبنيه لك! ​محاكاة روبوتية خارقة: باستخدام جهاز Nvidia RTX Spark، قام Hermes بالتخطيط لعائق تدريبي لبطة Hugging Face Microduck، ثم استعان بـ Nemotron لتخطيط المسار وبـ GPT-6 Astra لتنفيذه عبر FreeCAD، Omniverse، Blender، وIsaac Lab! ​الواقع والمحاكاة: تدريب آلاف البطات الافتراضية على المسار حتى النضج التام، لينتقل الذكاء بسلاسة للروبوت الحقيقي في العالم المادي. ​📌 الثورة الحقيقية للمصدر المفتوح: المصدر المفتوح ليس مجرد فكرة فلسفية، بل هو أداة عملية تمنحك القدرة على ربط السحابة، النماذج المحلية، والأجهزة لتصنع سير عمل خارق بنفسك، دون انتظار شركة كبرى لتسمح لك بذلك! ​احفظ التغريدة وشاركها مع أصدقائك المطورين.. المستقبل يُصنع الآن! 🧵👇 x.com/NousResearch/s…

Ken Griffin breaks down the biggest hedge fund trade of 2026: Citadel's takeover of Situational Awareness's public-equity portfolio.

This is what it looks like to be a robot joining Nvidia $NVDA CEO Jensen Huang on stage

Jensen Huang says a 1 GW AI factory costs ~$50B but could generate ~$40B in annual revenue meaning every month of deployment delays ~$3.3B in potential revenue. That puts $NVDA new DSX POD into perspective because cutting deployment from 4 months to roughly one month could bring forward over $10B in revenue per GW without needing additional GPUs.

Elon Musk says there's no AI demand issue and explains why SpaceX rents its compute on short-term contracts: AI compute is worth more per watt every year, renewals are coming in at higher prices, and the market is a “cheese shop” with no chips left “Our belief is that the value of compute per watt will increase over time, not decrease. And the reason for that, I think it should be obvious to people really, is that as you improve the AI software, as the models get better and better, they’re able to, you get more useful intelligence per watt. And the value of that compute is proportionate to the useful intelligence per watt. And that’s why you see things like even old computers like H100s renting for more than they rented when they were new.” “And I think that’s likely to continue. I think we’re going to face a shortage of superintelligence compute. And that shortage is going to remain for a long time. So, I think the smart move is to do short-term contracts and to essentially place the bet on useful intelligence per watt increasing rapidly, which obviously is done, so that’s what’s happened so far.” “And also if you look at, say, how are our short-term contracts renewing? Are they renewing at higher dollars per watt or lower? And we see them renewing at higher dollars per watt, not lower dollars per watt. We are also frankly hounded relentlessly by every hyperscaler and by dozens of smaller companies to rent more of our compute. So we could rent out our compute several times over if we said yes to everyone. That’s just the situation here.” “That’s why I posted that sort of video meme of the Monty Python cheese shop sketch where the guy goes into a cheese shop to get some cheese and they actually have no cheese. And so he goes like, what about this kind of cheese? What about that kind of cheese? And they really just don’t have any cheese.” “So we’ve actually tried to offload some of our inference compute to other cloud providers so that we can use more compute for training. And we get the cheese shop sketch. You know, they’ll say, like, well, we’ll ask them about what about some, you know, GB300s? Nope, don’t have any of those. What about, you know, some GB200s? Nope, don’t have any of those. What about some TPUs? Nope. What about some Trainiums? Nope. What about some, okay, how about some H100s? And like, well, we might have some overseas somewhere and then they’ll check and come back and say, nope, we don’t have that either. So it’s like ridiculous.” “Yeah. So I don’t think we’re going to have a demand issue here. And like, it’s not impossible. I’m not saying this will happen. It’s not impossible that we will have a six-month payback period for compute next year.”

Fireside Alpha @firesidealpha

5 days ago

Elon Musk says Starlink connectivity revenue has “a clear path to several hundred billion dollars,” and promises a separate presentation on the plan “Yeah, I think we probably should do a longer presentation on the game plan for Starlink connectivity and why we think it will

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The foreshadowing is insane

Banger insights from the makers of this chart, who put IBM above Anthropic

Carl Quintanilla @carlquintanilla

6 days ago

“.. At the moment, we have more compute capacity than we have demand, and we have a lot of the tokens and processing power being given away in freemium solutions,” he told @CNBC. “Everyone in the AI race is running for a last-man standing position.” cnbc.com/2026/10/19/gar…

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Fatimah Azzahra : "Kedepannya tolong jangan diklaim lagi MBG itu tujuan utamanya untuk stunting. Jujur saja sebut untuk ekonomi!" Kritik lugas dari Fatimah Azzahra ini menohok langsung ke jantung narasi pemerintah.

Anthropic just made a MASSIVE announcement and I think people are overlooking how significant this could become, especially with the company reportedly looking to IPO at a $2 TRILLION valuation. Anthropic announced that it plans to commit 5% of its shares to a new Public Benefit Fund, subject to shareholder approval, with the possibility of contributing an additional 2% for every 50% increase in its valuation. Think about how insane that is. At Anthropic’s most recent $965B private valuation, that initial 5% stake would already be worth approximately $48 billion. But at the $2T valuation being discussed for its IPO, we’re talking about $100B in equity potentially being allocated toward public benefit initiatives. Unlike a traditional charitable donation, Anthropic is committing ownership in the company itself, meaning the value of those shares could continue growing as Anthropic becomes more valuable. The idea is that a portion of the enormous wealth being created by AI could eventually be used to benefit society, including potentially supporting people and communities affected by AI driven economic disruption. We’re entering a period where AI agents are becoming increasingly capable of performing tasks that previously required human labor. Everything from customer service and software development to research, financial analysis, and administrative work is beginning to change. Now, I don’t believe this means everyone is suddenly going to lose their jobs. In fact, I think AI is going to create entirely new industries and opportunities that we haven’t even imagined yet. But we also have to recognize that this transition could be extremely disruptive, particularly for workers whose responsibilities can increasingly be automated. And this is WHY I think Anthropic is making this move. The company recognizes that AI has the potential to generate trillions of dollars in economic value, but that doesn’t necessarily mean everyone is going to benefit equally. By setting aside equity today, Anthropic is creating a mechanism that could potentially help address those challenges as AI adoption accelerates. My biggest takeaway from all of this is that we’re reaching a point where AI companies are not only competing to build the most powerful models in the world but are also beginning to think about how the enormous wealth created by those models should be distributed. And I think we’re only scratching the surface of how much AI is going to change our economy over the next decade.

Here’s why agent swarms are going to be a very big deal. We can finally make compute a critical variable in a large number of important tasks in the economy which wasn’t possible before. There’s a large amount of work in the world that is essentially only constrained by the amount of time that we put into research, data processing, discovery work, running tests, and more. All of this work is bottlenecked by our ability to plan, execute, review, and coordinate everything happening in these workflows. Which means we basically do very little of them today relative to their economic value. Agent swarms are the first real ability to parallelize this work in a way where - in theory - the only parameter that matters is how much compute you throw at the problem. Cyber security, life sciences or material science research, trading, QA testing, data processing, and many other fields are essentially compute constrained problems in the limit once models are good enough at these tasks. The breakthrough is that in a world where agents can use tools, write code, process data, have strong domain depth, and can coordinate with each other, you can begin to solve arbitrarily complex problems by setting the right goals and applying enough tokens to the problem. This changes a lot of fields as well as even the competitive dynamics in these fields.

Pantesan gak ada yang berani ngabarin berita buruk ke dia Lah orangnya ngamukan 😂

Honestly, I can’t help but laugh whenever I see this kind of modeling. How many people can actually predict what will happen beyond 2027 with any accuracy? Is there even any point in building these models in the first place? Especially the idea of “forecasting” demand… I think it’s utterly absurd.

He cant come back from this 😭

Now that I have successfully rage baited what feels like every AI bear on X, let's systematically break down the most common counterarguments because they are very off the mark. #1. The AI trade is the same as the dotcom bubble or the 2008 crisis This is the most frequent rebuttal, and it is a lazy comparison. When you look under the hood, the numbers do not support it. Looking at the S&P 500 top 10 at each peak: March 2000: 53.6x trailing earnings, 9.1% net margin October 2007: 16.4x trailing earnings, 13.3% net margin Today: 26.5x trailing, 20.9x forward, 33% net margin Unlike the dotcom bubble, today's leaders have the earnings to support their build out. In the three years before the 2000 peak, the tech leaders' stocks rose 7.4x on 2.1x earnings growth, and the rest of the market was priced even higher: the Nasdaq traded at about 175x earnings. Today's AI leaders rose 2.9x on 3.1x earnings growth, and on average they pay about a fifth of the 2000 price per unit of growth. PEG on trailing three year growth: 2000: 1.6 to 5.7 Now: 0.2 to 1.6 2007 was never a valuation problem. It was a leverage problem, which I cover in #4. Yes, today's concentration at the top of the index is real, and it is higher than in 2000, but this time it is backed by profits. Today the top 10 are 40.6% of the S&P 500's value and 36.1% of its earnings. In March 2000 they were 27% of the value and only about 15% of the earnings. Put today's earnings at 2000's prices and the gap is massive: Nvidia at Cisco's peak multiple (178x) would be about $1,210 a share, a $29T company, 5.7 times today's price. Today's top 10 at the 2000 top 10's 53.6x would be worth $43T even on earnings excluding gains, against $28T today. The S&P 500 at 2000's forward P/E of 25.2x would be about 10,160, against 7,659. (See Chart 1) #2. Nvidia is the next Cisco, or the next Enron. From January 1998 to the March 2000 peak, Cisco's stock rose 7.6x while its trailing earnings rose 1.7x, ending at 178x pro forma earnings. Over the last 26 months, the same length of time, Nvidia's stock rose 1.8x and its trailing earnings 3.3x. It trades at 31x. Enron is an even worse comparison. In 2000 it reported $100.8B of revenue, much of it gross trading volume, and kept $979M of it, a 1% margin, while its stock traded at about 74x earnings. Its profits leaned on mark to market accounting and debt kept off its balance sheet, and it filed for bankruptcy in December 2001, then the largest in US history. Over the last 12 months Nvidia turned $303B of revenue into $193B of net income and $134B of operating cash flow, paid in cash by the largest companies in the world. #3. Today's companies aren't profitable These are some of the most profitable companies in history. Over the last 12 months Nvidia earned $166B excluding investment gains, more than Saudi Aramco's $161B in 2022, the largest annual profit any company had ever reported. Until 2024 only Aramco had earned more than $100B in a year. Nvidia, Apple, Microsoft and Alphabet now each do, even excluding gains. The suppliers are the same. Micron earned $85B at a 64% margin and TSMC $78B at 50%. SK hynix made $85B of operating profit, and Samsung made $80B of operating profit in the third quarter alone, a quarter only Aramco has ever topped. The ten largest tech companies of March 2000 earned $37B combined, at a 15% margin. Cisco kept 17 cents of each revenue dollar and Lucent 9. Nvidia keeps 55 cents and Micron 64. To be fair, memory is cyclical. These are shortage profits, and Micron's previous record year was $14B in fiscal 2018. (See Chart 2) #4. The AI trade is built on debt and is not sustainable The dotcom buildout ran on borrowed money. Debt owed by US telecom carriers rose from $158B in 1996 to $380B in 2000, and the junk rated carriers had negative operating cash flow as a group. The seven pure network builders (Global Crossing, Level 3, Williams, XO, 360networks, McLeodUSA, Winstar) owed $31.8B plus $6.5B of preferred stock, and six of the seven did not cover their interest bill even on their own EBITDA numbers. Telecom made up 55% of US junk bond defaults in 2001, and 23 telecoms went bankrupt by mid 2002. In 2000 WorldCom needed 3.2 years of cash flow to repay its debt, Global Crossing 8, and XO's cash flow was negative. Today Alphabet, Microsoft, Amazon and Meta owe $351B, hold $533B of cash, and could repay it all with about six months of cash flow. In the last year the 10 largest US tech companies generated $1.10T of operating cash flow and had $523B left after $574B of capex. At the 2000 peak, the top 10 tech names had about $34B of free cash flow, $66B in today's dollars, and traded at about 57x cash flow. Today it is 24x. 2008 was leverage inside the financial system. In 2007 financial sector debt was 144% of GDP, broker dealers ran 46x assets to equity, and Citigroup ran 18.6x and Bank of America 11.4x, both top 10 companies at the time. Today financial sector debt is 85% of GDP, broker dealers run 17x, and the S&P 500 top 10 run 1.8x. The debt that does exist sits at the edges, with names like Oracle. The four largest spenders are paying for this out of cash flow. (See Chart 3) #5. Demand is inflated, and they are overbuilding capacity like the dotcom bubble. AI provides no long term value and adoption is overstated. In 2000, WorldCom said internet traffic doubled every 100 days. In reality it doubled about once a year, and US backbone networks ran at about 10 to 15% of capacity. The carriers built for demand that did not exist yet, in the hope that it would show up. Yes, AI spend is larger now, but it sits against $2.3T of signed backlog at AWS, Google Cloud, Microsoft and Oracle. Demand is running ahead of supply: TSMC says demand is still far above its ability to supply, Micron says it has no line of sight to when supply and demand balance, and Nvidia's CFO expects supply to remain a bottleneck at least through fiscal 2028. Rental prices for the H100, a chip that shipped in 2022, are up 17% over the past year after halving from their 2024 peak. Hardware normally gets cheaper as it ages; over the past year we have seen the opposite, because demand is outrunning supply. The suppliers see this demand first. They would be the canary in the coal mine here, and they just reported record quarters: TSMC, ASML and Micron each posted the highest quarterly revenue in their history. TSMC raised its 2026 growth guidance at every update, to about 44%. ASML lifted its 2026 sales guide from €34 to 39B to €44 to 45B, says 2027 is close to fully covered with orders for its standard EUV machines, and has customers asking to order for 2029 and 2030. By press estimates a High NA machine costs about $380M and a standard EUV machine about $180M. Nobody books those years ahead on a whim. Memory makers are seeing the same and are signing take or pay agreements years out. Micron has 26 of them, some running to 2031, covering over 35% of its revenue through 2030, and over 75% of its 2027 output is already committed. Samsung's agreements run at least five years, and SanDisk has $93.9B of minimum contracted revenue. For comparison, in 2000 Lucent had committed $8.1B of financing to its own customers. Now the money runs the other way: Micron holds $12.7B of customer cash deposits, and ASML's long term customer down payments rose from €3.6B to €9.3B last quarter. These commitments are funded by the cash flow of the largest companies in the world. Generative AI reached 55% of US adults aged 18 to 64 within three years of launch. The internet was at 30% at the same point, and the PC at 20%. About 2B people use AI worldwide, 55.9% of US businesses on Ramp pay for AI tools, and Census data shows 23.8% of US businesses used AI in the past two weeks. Consumers spend about $40B a year on it, roughly $20 per user, so monetization has barely started. Physical AI is earlier still. Waymo runs about 500,000 paid rides a week, up from 50,000 in May 2024, and Nvidia's physical AI revenue passed $9B over the last 12 months. Each robot or autonomous vehicle needs far more compute and memory than a phone: Micron says an L4 vehicle typically carries over 200 GB of DRAM, about 24 times a phone, and expects humanoids to need as much. Counterpoint expects humanoid memory demand to grow 20x from 2026 to 2030. Volumes are still small; this layer of demand has barely started. (See Chart 4) #6. Today's tech leaders aren't smart This is the most insane one for me. The idea that random people on X can question the intelligence of the people running these companies is nuts. ASML's EUV machines fire a laser at a drop of molten tin twice, 50,000 times a second, creating a plasma hotter than the surface of the sun to print chips. TSMC is mass producing chips at 2nm. These companies turn science fiction into reality on a regular basis. The leadership is also nothing like 2000. Executives at Enron and WorldCom were convicted of fraud, and Congress passed Sarbanes Oxley in 2002 because of them. Since then every public company CEO and CFO has had to personally certify their company's financial statements. For people to throw stones at the intelligence of these leaders is just loony. In summary, I am very optimistic about the transformative nature of AI for our lives and our economy. So as I originally stated, I will continue to be long my picks and shovels favorites like TSMC, ASML, GEV and others. We are still in the early innings; don't let lazy fear mongering tell you otherwise. I am not saying "it is not the same" out of hope or cope. The math and data support me.

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Paradise Capital @Para_Capital

a week ago

If you’re short or anti the AI trade, your thesis is literally to bet against some of the smartest people ever who are running the largest and most profitable companies to ever exist. Yeah… I think I’ll stay long.

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jack’s pretty spot on here, you don’t need to be an ai researcher to see it either: - 3 years ago ai models couldn’t do simple math. couldn’t count the letters in “strawberry” - today the most powerful models have solved coding and are solving the most challenging problems in maths, physics and genetics. soon (and i mean very soon) it’ll expand for medicine, cancer, knowledge work etc - model’s are improving at a blistering pace, new releases every 13 days. this eventually snowballs into rsi. - all while the cost of intelligence is plummeting. the frontier models of last quarter is now being served to you at 1/8th the cost. and the craziest part is barely anyone is even using AI. there’s a whole wide world of jobs, tasks, problems that can (and will) be eaten up by the singularity we just don’t notice it because it hasn’t affected “us individually” but doesn’t mean it’s not coming. anyways, think people will be very surprised next year when what ai did to software engineering comes barrelling into modern day humanity.

Jack Clark @jackclarkSF

a week ago

I’m speaking at the IMF in an hour about the singularity and why I believe it has begun and the choices we'll need to make. Watch live here: youtube.com/live/Rqt9kWoTJ…

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There’s a fundamental reason why TSMC has always been notoriously conservative in its forecasts and investment planning It’s not because they are the only adults in the room. It’s because they are the ones writing the checks to build those incredibly expensive fabs, packed with ASML lithography equipment If AI demand turns out to be overestimated, Nvidia would certainly take a significant valuation hit. And with $279bn in disclosed supply and capacity commitments, its potential financial exposure is hardly trivial either But TSMC faces a different problem entirely: tens of billions sunk into fabs that could sit underutilized for years, with depreciation and fixed costs crushing returns on capital You can adjust a product roadmap, renegotiate some supply commitments or write down inventory. You can’t simply return a $20bn semiconductor fab because demand didn’t materialize That’s why TSMC’s forecasts deserve particular attention These guys have enormous visibility across the semiconductor supply chain, strong incentives to avoid overbuilding and decades of experience navigating the industry’s brutal cycles And yet, they are planning to spend $60–64bn this year alone, with even heavier aggregate investment over the next three years, because AI demand keeps exceeding their expectations For all these reasons, as far as AI demand visibility goes, $TSMC inputs carry much more informational content than W St vibes…

Freda Duan @FredaDuan

a week ago

AI Infra: Are we confusing a U.S. power shortage with a global supply glut? One of the biggest debates around the AI buildout is whether we have enough power and data center capacity to absorb all the compute coming online in 2027. I think we're missing an important

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deddy sitorus anggota komisi ii dpr fraksi pdip bilang kopdes merah putih berpotensi gagal lebih dari 90 persen ia bilang program ini bisa jadi beban negara bertahun-tahun ia khawatir nanti banyak orang berurusan dengan hukum karena pelaksanaannya “sembrono” alasannya konsepnya salah kaprah dari awal koperasi tidak lahir dari keinginan warga desa, tapi dipaksa dari atas (top-down) dibangun pakai template yang sama di seluruh indonesia, tanpa model bisnis yang cocok dengan kebutuhan tiap desa modalnya, kata dia, diambil dengan cara “merampok” dana desa yang seharusnya untuk infrastruktur desa proyek juga menyedot apbn dari kementerian dan bank milik negara lahannya, kata dia, “merampas” tanah milik desa, dibangun tanpa lihat kelayakan lokasi dan desain yang cocok dengan calon pembeli