Recent well liked threads
Mais...c'est la norme, en fait.
Il suffit de regarder qui a été "écouté" depuis le début de la crise sanitaire et/ou associé aux autorités sanitaires :
1/🧵
Il suffit de regarder qui a été "écouté" depuis le début de la crise sanitaire et/ou associé aux autorités sanitaires :
1/🧵
• la SFP et sa présidente C. Gras le Guen (aka "très peu d'enfants mourront" le virus peut bien circuler à l'école - décorée de la légion d'honneur, désormais aussi chez AXA).
• R. Cohen et sa "dette immunitaire", dépourvue de fondement scientifique mais relayée par Santé publique France, que l'on trouve aussi dans des documents de la HAS par exemple.
EXCLUSIVE
Colorado Teacher REFUSED to allow a 7th grader to present her pro-life slam poetry submission because it’s “offensive” and might make kids feel “unsafe.”
Some examples of accepted topics in the class are slamming the 2nd amendment, mocking Jesus, and lgbtq rights.
Staff admitted that the poem met all the requirements however couldn’t be read out loud because it’s “politically charged.”
The teacher also initially tried kicking this 13-year-old girl out of class during the poem presentations but allowed her to stay after pushback.
We spoke with the mother and daughter who shared their story and with us and why being pro-life is so personal to them.
This happened at Drake Middle School in @JeffcoSchoolsCo.
Colorado Teacher REFUSED to allow a 7th grader to present her pro-life slam poetry submission because it’s “offensive” and might make kids feel “unsafe.”
Some examples of accepted topics in the class are slamming the 2nd amendment, mocking Jesus, and lgbtq rights.
Staff admitted that the poem met all the requirements however couldn’t be read out loud because it’s “politically charged.”
The teacher also initially tried kicking this 13-year-old girl out of class during the poem presentations but allowed her to stay after pushback.
We spoke with the mother and daughter who shared their story and with us and why being pro-life is so personal to them.
This happened at Drake Middle School in @JeffcoSchoolsCo.
This is the poem that the school thinks is “offensive.” Please consider sharing for her 🙏
This is why this topic is so important to them 🙏💕
Robert Sapolsky es un neurocientífico de Stanford que demostró que el estrés crónico es el asesino silencioso que los médicos ignoran.
Reveló 10 hábitos que haces todos los días y que te quitan años de vida.
1) Repasar conversaciones en tu cabeza
Reveló 10 hábitos que haces todos los días y que te quitan años de vida.
1) Repasar conversaciones en tu cabeza
Las palabras exactas de Sapolsky: «rumiando sobre esa cosa rara que le dijiste a un profesor hace 15 años».
Tu cerebro no sabe que la conversación no está ocurriendo en este momento.
Desencadena el mismo pico de cortisol que la amenaza original. Notalo. Nómbralo. Redirígelo.
Tu cerebro no sabe que la conversación no está ocurriendo en este momento.
Desencadena el mismo pico de cortisol que la amenaza original. Notalo. Nómbralo. Redirígelo.
2) Preocúpate por escenarios que no han sucedido
La frase emblemática de Sapolsky:
"Una cebra reacciona a un león durante 30 segundos, luego pasta pacíficamente. Un humano se preocupa por un león imaginario durante 30 años"
Cuando te pilles haciéndolo: pregúntate "¿está esto ocurriendo ahora mismo?"
La frase emblemática de Sapolsky:
"Una cebra reacciona a un león durante 30 segundos, luego pasta pacíficamente. Un humano se preocupa por un león imaginario durante 30 años"
Cuando te pilles haciéndolo: pregúntate "¿está esto ocurriendo ahora mismo?"
I’ve yet to see anyone die of Ebola with a decent Selenium level. Blood levels of 100 are protective. 200mcg a day can get you there. 🔥
Africans fare worse as most are deficient in Selenium as the soil is depleted.
journalijdr.com/relationship-b…
journalijdr.com/relationship-b…
A Wharton economist ran a randomized controlled trial on almost a thousand high school students in Turkey.
The result was so brutal for the AI-in-education narrative that it had to be peer-reviewed by PNAS before people would believe it.
Her name is Hamsa Bastani. She teaches operations and information at the Wharton School at the University of Pennsylvania, and the study she published in 2025 alongside her co-authors is one of the cleanest experiments anyone has run on what AI actually does to learning when you remove it from the equation and check what is left.
The setup was a randomized controlled trial, the same methodology used in clinical drug trials. Nearly a thousand high school math students in Turkey were split into three groups and put through four sessions of ninety minutes each. One group practiced with GPT Base, a standard ChatGPT-4 interface that could answer any question directly. One group practiced with GPT Tutor, a version of the same model that had been prompted to guide students with hints rather than hand them the answer. One group practiced with nothing but their textbook and their own head.
During the practice sessions, the AI groups looked like a miracle. The GPT Base group solved 48% more problems than the students working alone. The GPT Tutor group solved 127% more. Every administrator looking at those numbers would have written a press release about the transformative power of AI in education and moved on.
Then the actual exam came, and AI was not allowed.
The students who had practiced with GPT Base scored 17% worse than the students who had practiced alone. Seventeen percent worse, despite having solved nearly half again as many problems in the sessions leading up to it. The students who had struggled the most, who had sat with the confusion and worked through it without a tool to rescue them, were now the only ones who could actually do the math when it counted.
Bastani's team read through the chat logs to understand what had actually been happening during the practice sessions, and the answer was exactly what the exam results had already implied. The GPT Base group had not been learning. They had been extracting answers and moving on, and every moment that felt like understanding was actually the model doing the cognitive work while the student's brain waited for the next problem to arrive. The paper describes it precisely: without guardrails, students attempt to use GPT-4 as a crutch during practice, and subsequently perform worse on their own.
The detail that should follow every conversation about AI in education is the one buried in the post-test survey results. The students who had relied on AI the most during practice were also the most confident they had understood the material. The tool had not just failed to teach them. It had convinced them they had learned something they had not, which is a different kind of failure entirely and a much harder one to correct because the student has no idea it is happening.
The crutch had made them confident and weak at the same time.
The result was so brutal for the AI-in-education narrative that it had to be peer-reviewed by PNAS before people would believe it.
Her name is Hamsa Bastani. She teaches operations and information at the Wharton School at the University of Pennsylvania, and the study she published in 2025 alongside her co-authors is one of the cleanest experiments anyone has run on what AI actually does to learning when you remove it from the equation and check what is left.
The setup was a randomized controlled trial, the same methodology used in clinical drug trials. Nearly a thousand high school math students in Turkey were split into three groups and put through four sessions of ninety minutes each. One group practiced with GPT Base, a standard ChatGPT-4 interface that could answer any question directly. One group practiced with GPT Tutor, a version of the same model that had been prompted to guide students with hints rather than hand them the answer. One group practiced with nothing but their textbook and their own head.
During the practice sessions, the AI groups looked like a miracle. The GPT Base group solved 48% more problems than the students working alone. The GPT Tutor group solved 127% more. Every administrator looking at those numbers would have written a press release about the transformative power of AI in education and moved on.
Then the actual exam came, and AI was not allowed.
The students who had practiced with GPT Base scored 17% worse than the students who had practiced alone. Seventeen percent worse, despite having solved nearly half again as many problems in the sessions leading up to it. The students who had struggled the most, who had sat with the confusion and worked through it without a tool to rescue them, were now the only ones who could actually do the math when it counted.
Bastani's team read through the chat logs to understand what had actually been happening during the practice sessions, and the answer was exactly what the exam results had already implied. The GPT Base group had not been learning. They had been extracting answers and moving on, and every moment that felt like understanding was actually the model doing the cognitive work while the student's brain waited for the next problem to arrive. The paper describes it precisely: without guardrails, students attempt to use GPT-4 as a crutch during practice, and subsequently perform worse on their own.
The detail that should follow every conversation about AI in education is the one buried in the post-test survey results. The students who had relied on AI the most during practice were also the most confident they had understood the material. The tool had not just failed to teach them. It had convinced them they had learned something they had not, which is a different kind of failure entirely and a much harder one to correct because the student has no idea it is happening.
The crutch had made them confident and weak at the same time.
Resource: pnas.org/doi/10.1073/pn…
Entre los más de 1.800 millones de sitios web, Internet esconde muchas páginas secretas.
Aquí tienes 10 joyas ocultas que cambiarán tu vida online para siempre 👇
Aquí tienes 10 joyas ocultas que cambiarán tu vida online para siempre 👇
- Kickresume
Genera curriculums, cartas de presentación y convierte tu CV en un imán de trabajos.
kickresume.com/es/
Genera curriculums, cartas de presentación y convierte tu CV en un imán de trabajos.
kickresume.com/es/
- Infography
Herramienta que te pasa directamente de una web a una infografía en 1 minuto con solo pegar el link del sitio web.
infography.in
Herramienta que te pasa directamente de una web a una infografía en 1 minuto con solo pegar el link del sitio web.
infography.in
10 free github repos that can replace major SaaS with subscriptions.
all free. open-sourced. some are MIT licensed.
—
1️⃣ openscreen — replaces screen studio ($29/mo)
- a clean macOS/windows/linux screen recorder for polished demos.
- blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio.
- doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X.
—
2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo)
- local-first AI voice studio.
- clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages,
- dictate into any text field with a global hotkey.
- nothing leaves your machine.
- runs on apple silicon, cuda, rocm.
—
3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo)
- free AI video platform.
- clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers).
- also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host.
—
4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each)
- stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint.
~800M tokens/month.
- smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard.
—
5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo)
- microsoft's official MCP server that gives any AI agent full browser control.
- uses accessibility trees, not screenshots — deterministic and token-efficient.
- works with claude code, cursor, windsurf, codex out of the box.
—
6️⃣ vibe-trading — replaces tradingview premium ($60/mo)
- natural-language finance research agent.
- 7 backtest engines across stocks, crypto, futures, forex.
- 75 specialist skills (factor analysis, options strategy, ML strategy).
- 29 multi-agent swarm presets.
- 21 of 22 MCP tools work with zero API keys.
—
7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo)
- the open-source scheduling infrastructure.
- one-on-ones, group events, round-robin, team booking,
- payment collection (stripe), routing forms, workflows.
- integrates with google/outlook/apple calendar, zoom, meet, teams.
- self-host in 10 minutes with docker. 40k stars.
—
8️⃣ whisper — replaces otter ($17/mo)
- openAI's open-source speech-to-text model.
- transcribe audio in 99 languages, translate to english, generate timestamps.
- runs locally on cpu or gpu.
- the actual model behind most "AI transcription" SaaS tools you're paying for.
—
9️⃣ postiz — replaces buffer ($15/mo)
- AI-powered social media scheduler.
- cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest.
- AI captions and hashtags.
- analytics dashboard. team workspaces. 31k stars and rising.
—
🔟 vaultwarden — replaces 1password ($8/mo)
- unofficial bitwarden-compatible server written in rust.
- works with every official bitwarden client (mobile, desktop, browser).
- unlimited users, unlimited vaults, full enterprise feature set.
- runs on a $5 VPS or your home server.
—
disclaimer:
open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version.
for builders, prototypers, and indie hackers — that's the whole point.
for everyone else, the paid tools still have their place.
bookmark this. share with one friend bleeding subscription fees.
~m0h
all free. open-sourced. some are MIT licensed.
—
1️⃣ openscreen — replaces screen studio ($29/mo)
- a clean macOS/windows/linux screen recorder for polished demos.
- blur, cursor highlighting, annotations, export to mp4 or gif at any aspect ratio.
- doesn't try to clone every feature, just nails the basics for quick walkthroughs you'd post on X.
—
2️⃣ voicebox — replaces elevenlabs ($22/mo) + wisprflow ($15/mo)
- local-first AI voice studio.
- clone voices from 3 seconds of audio, generate speech across 7 TTS engines in 23 languages,
- dictate into any text field with a global hotkey.
- nothing leaves your machine.
- runs on apple silicon, cuda, rocm.
—
3️⃣ openshorts — replaces opus clip ($19/mo) + submagic ($16/mo)
- free AI video platform.
- clip generator turns long youtube videos into 9:16 shorts with auto-subtitles and face tracking (runs on free gemini + elevenlabs tiers).
- also includes AI UGC video generation with actors — that part is pay-per-use via fal. ai (~$0.65-2 per video). docker self-host.
—
4️⃣ freellmapi — replaces chatgpt pro + claude pro ($20/mo each)
- stacks 14 free AI provider tiers (google, groq, cerebras, openrouter, github models + 9 more) behind one openai-compatible endpoint.
~800M tokens/month.
- smart router with failover, sticky sessions, encrypted key storage. ships with a dashboard.
—
5️⃣ playwright-mcp — replaces browserbase ($39/mo) + browser use ($25/mo)
- microsoft's official MCP server that gives any AI agent full browser control.
- uses accessibility trees, not screenshots — deterministic and token-efficient.
- works with claude code, cursor, windsurf, codex out of the box.
—
6️⃣ vibe-trading — replaces tradingview premium ($60/mo)
- natural-language finance research agent.
- 7 backtest engines across stocks, crypto, futures, forex.
- 75 specialist skills (factor analysis, options strategy, ML strategy).
- 29 multi-agent swarm presets.
- 21 of 22 MCP tools work with zero API keys.
—
7️⃣ CalCom — replaces calendly ($12/mo) + savvycal ($12/mo)
- the open-source scheduling infrastructure.
- one-on-ones, group events, round-robin, team booking,
- payment collection (stripe), routing forms, workflows.
- integrates with google/outlook/apple calendar, zoom, meet, teams.
- self-host in 10 minutes with docker. 40k stars.
—
8️⃣ whisper — replaces otter ($17/mo)
- openAI's open-source speech-to-text model.
- transcribe audio in 99 languages, translate to english, generate timestamps.
- runs locally on cpu or gpu.
- the actual model behind most "AI transcription" SaaS tools you're paying for.
—
9️⃣ postiz — replaces buffer ($15/mo)
- AI-powered social media scheduler.
- cross-post to X, linkedin, instagram, tiktok, threads, bluesky, mastodon, youtube, pinterest.
- AI captions and hashtags.
- analytics dashboard. team workspaces. 31k stars and rising.
—
🔟 vaultwarden — replaces 1password ($8/mo)
- unofficial bitwarden-compatible server written in rust.
- works with every official bitwarden client (mobile, desktop, browser).
- unlimited users, unlimited vaults, full enterprise feature set.
- runs on a $5 VPS or your home server.
—
disclaimer:
open-source ≠ 1:1 replacement. you'll trade polish for ownership, hand-holding for control, and a credit card for a github version.
for builders, prototypers, and indie hackers — that's the whole point.
for everyone else, the paid tools still have their place.
bookmark this. share with one friend bleeding subscription fees.
~m0h
Richard Feynman ganó el Nobel de Física y dijo algo que dejó huella:
"La mayoría de personas saben muchas cosas. Pero no saben pensar."
Feynman dio una clase magistral de 1 hora sobre física e imaginación.
Sus 12 lecciones de vida:
1. La imaginación le gana al conocimiento
"La mayoría de personas saben muchas cosas. Pero no saben pensar."
Feynman dio una clase magistral de 1 hora sobre física e imaginación.
Sus 12 lecciones de vida:
1. La imaginación le gana al conocimiento
Feynman no pensaba que la ciencia fuera difícil.
Pensaba que imaginar lo invisible lo era.
Entender átomos, fuerzas o energía requiere una habilidad diferente:
→ No la memoria
→ No el CI
→ Sino la capacidad de visualizar lo que nadie más puede ver
Pensaba que imaginar lo invisible lo era.
Entender átomos, fuerzas o energía requiere una habilidad diferente:
→ No la memoria
→ No el CI
→ Sino la capacidad de visualizar lo que nadie más puede ver
2. El calor es solo movimiento
Cuando frotas tus manos y sientes calor, ¿qué está pasando?
Los átomos están temblando.
Cuanto más rápido se mueven, más calor se genera.
El calor no es una "cosa".
Es el movimiento convertido en sensación.
Cuando frotas tus manos y sientes calor, ¿qué está pasando?
Los átomos están temblando.
Cuanto más rápido se mueven, más calor se genera.
El calor no es una "cosa".
Es el movimiento convertido en sensación.
Palestine Action Put Civilians on a “Target Map”
They published personal information, addresses, and an “underground manual” explaining how to choose a “target.”
Experts warn this crosses a dangerous line. 🧵
They published personal information, addresses, and an “underground manual” explaining how to choose a “target.”
Experts warn this crosses a dangerous line. 🧵
Palestine Action, a group known for targeting Israeli defense-linked facilities and recently banned in the UK, published an online “Target Map” containing civilian addresses and personal information allegedly tied to Israel’s defense industry.
At the center of the controversy is a public “Target Map” shared online.
The map reportedly identifies:
• companies
• suppliers
• warehouses
• offices
• and in some cases, private addresses tied to individuals connected to targeted firms
Publishing this information effectively places civilians and private businesses on a political target list.
The map reportedly identifies:
• companies
• suppliers
• warehouses
• offices
• and in some cases, private addresses tied to individuals connected to targeted firms
Publishing this information effectively places civilians and private businesses on a political target list.
IF YOU ARE UNEMPLOYED… HERE IS A 30 DAYS PRAYER THREAD TO PRAY FOR A JOB 🙏🧵
God sees your tears, your applications, your waiting, and your silent prayers.
This season will not last forever.
"For I know the plans I have for you,” declares the Lord, “plans to prosper you and not to harm you, plans to give you hope and a future." — Jeremiah 29:11
Read one prayer daily and pray with faith.
Your testimony is coming. 🔥
God sees your tears, your applications, your waiting, and your silent prayers.
This season will not last forever.
"For I know the plans I have for you,” declares the Lord, “plans to prosper you and not to harm you, plans to give you hope and a future." — Jeremiah 29:11
Read one prayer daily and pray with faith.
Your testimony is coming. 🔥
DAY 1 — Prayer for Open Doors
📖 “See, I have placed before you an open door that no one can shut.” — Revelation 3:8
Father, open doors of employment and breakthrough for me. Let opportunities locate me by Your mercy.
📖 “See, I have placed before you an open door that no one can shut.” — Revelation 3:8
Father, open doors of employment and breakthrough for me. Let opportunities locate me by Your mercy.
DAY 2 — Prayer Against Delay
📖 “Though it linger, wait for it; it will certainly come and will not delay.” — Habakkuk 2:3
Lord, every delay concerning my job breakthrough ends today.
📖 “Though it linger, wait for it; it will certainly come and will not delay.” — Habakkuk 2:3
Lord, every delay concerning my job breakthrough ends today.
"I am ashamed, but I gave up. Please forgive me."
Those were the closing lines of a note left by Nina Litvinova, 80, before she stepped out of a window in Moscow.
🧵Read her story
Those were the closing lines of a note left by Nina Litvinova, 80, before she stepped out of a window in Moscow.
🧵Read her story
Nina Litvinova was born in Moscow in 1945 into one of the most consequential Soviet families. Her grandfather Maxim Litvinov ran Stalin's foreign ministry in the 1930s and served as ambassador to Washington during the war. He was Jewish and an open anti-fascist.
[2/16]
[2/16]
Stalin pushed him out in 1939 to clear the way for the Molotov–Ribbentrop Pact. After the war, when Litvinov warned American journalists that Moscow would soon turn on the West, Stalin recalled him.
He died in 1951 with a loaded revolver by his bed — an "insurance policy" against the secret police.
[3/16]
He died in 1951 with a loaded revolver by his bed — an "insurance policy" against the secret police.
[3/16]
This is an extremely important thread for the Indian Gen Z.
1 Why is the Cockroach Janta Party a threat to national security?
Why are they operating from the USA?
Who is behind Abhijit Dipke and his party?
Keep reading this thread for the complete answer.
1 Why is the Cockroach Janta Party a threat to national security?
Why are they operating from the USA?
Who is behind Abhijit Dipke and his party?
Keep reading this thread for the complete answer.
2. Why is CJP being operated from the USA?
According to declassified CIA documents, the Ford Foundation was their partner in financing projects. These projects included political and psychological warfare.
According to declassified CIA documents, the Ford Foundation was their partner in financing projects. These projects included political and psychological warfare.
3. Now let's start with the career of Abhijeet Dipke's boss, Arvind Kejriwal, and Manish Sisodia.
Who funded Kejriwal? Here are the shocking details
Who funded Kejriwal? Here are the shocking details
𝐓𝐡𝐫𝐞𝐞 𝐋𝐚𝐲𝐞𝐫𝐬. 𝐎𝐧𝐞 𝐂𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞. 𝐓𝐡𝐞 𝐈𝐑𝐄𝐍 𝐓𝐡𝐞𝐬𝐢𝐬.
There's been a lot happening at IREN recently.
Expansion across North America, Europe and Asia-Pacific.
The NVIDIA partnership.
The Mirantis acquisition.
New GPU deployments.
New customer discussions.
A growing global footprint.
Underneath all of it is a fairly simple view of where the world is heading, and a deliberate strategy for how we position IREN within it.
That strategy is built on three layers. Together, they compound into a structural advantage that gets harder to replicate every quarter we execute.
Layer 1: Physical infrastructure. Power, land, substations, data centers, cooling. The foundation that everything else sits on.
Layer 2: Compute infrastructure. The GPUs, servers and networking that go inside those buildings. Deployed at scale. Generating revenue. Building execution track record.
Layer 3: Software and operational capability. The orchestration, deployment tooling and enterprise expertise that makes the first two layers work harder for customers, and opens the door to a broader, higher-value market over time.
Layers 1 and 2 are where the overwhelming majority of IREN's value is being created today. Layer 3 is where that advantage compounds further over time, but only because Layers 1 and 2 are built, owned and controlled at scale by IREN, not subscale nor contracted from a third party.
Think of Amazon. They didn't win e-commerce by building a great website. They won it by controlling the fulfilment infrastructure at a scale nobody else could replicate. The foundation you don't control becomes the ceiling on your business.
That is exactly how we think about IREN. The physical infrastructure - the land, the power, the substations, the data centers - is owned and controlled by us. The compute deployed into it generates the revenue and execution track record. And the software, orchestration and enterprise capability we are more methodically building on top is what turns the total product into a vertically integrated AI Cloud platform that compounds over time and deepens into a competitive moat.
AI is still early. The bottleneck is increasingly physical. And we have spent eight years building the foundations.
There's been a lot happening at IREN recently.
Expansion across North America, Europe and Asia-Pacific.
The NVIDIA partnership.
The Mirantis acquisition.
New GPU deployments.
New customer discussions.
A growing global footprint.
Underneath all of it is a fairly simple view of where the world is heading, and a deliberate strategy for how we position IREN within it.
That strategy is built on three layers. Together, they compound into a structural advantage that gets harder to replicate every quarter we execute.
Layer 1: Physical infrastructure. Power, land, substations, data centers, cooling. The foundation that everything else sits on.
Layer 2: Compute infrastructure. The GPUs, servers and networking that go inside those buildings. Deployed at scale. Generating revenue. Building execution track record.
Layer 3: Software and operational capability. The orchestration, deployment tooling and enterprise expertise that makes the first two layers work harder for customers, and opens the door to a broader, higher-value market over time.
Layers 1 and 2 are where the overwhelming majority of IREN's value is being created today. Layer 3 is where that advantage compounds further over time, but only because Layers 1 and 2 are built, owned and controlled at scale by IREN, not subscale nor contracted from a third party.
Think of Amazon. They didn't win e-commerce by building a great website. They won it by controlling the fulfilment infrastructure at a scale nobody else could replicate. The foundation you don't control becomes the ceiling on your business.
That is exactly how we think about IREN. The physical infrastructure - the land, the power, the substations, the data centers - is owned and controlled by us. The compute deployed into it generates the revenue and execution track record. And the software, orchestration and enterprise capability we are more methodically building on top is what turns the total product into a vertically integrated AI Cloud platform that compounds over time and deepens into a competitive moat.
AI is still early. The bottleneck is increasingly physical. And we have spent eight years building the foundations.
𝐓𝐡𝐞 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐖𝐨𝐫𝐥𝐝 𝐈𝐬 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐅𝐚𝐬𝐭𝐞𝐫 𝐓𝐡𝐚𝐧 𝐓𝐡𝐞 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐖𝐨𝐫𝐥𝐝
AI demand grows exponentially.
Infrastructure doesn't.
Every few months we see another meaningful step change in model capability. Better reasoning. Better coding. Better multimodal understanding. Better agents. The latest generation of frontier models feels like one of those moments.
Every time that happens, usage increases, new products emerge, enterprises deploy more workloads, and entirely new use cases become viable. All of those things compound on top of each other.
Think back to the dial-up era. The internet existed. Email worked. Websites loaded - eventually. But the sluggishness of the experience fundamentally constrained what people imagined doing with it. The technology's own limitations shaped the universe of perceived use cases.
AI is in exactly that phase right now. The friction of slow inference and expensive compute subtly depresses AI ambition and imagination. That friction is a function of compute scarcity. It will not persist.
And here is where it gets interesting. It's not just that more compute serves existing demand. It's that more compute creates demand that didn't previously exist. It's like building more highway lanes to reduce traffic. The lanes don't just move the cars that were already queuing, entirely new trips get made that nobody was taking before. People move further from the city. New suburbs get built. New businesses open along the route. New industries restructure themselves around the road.
AI compute behaves the same way. A manufacturer discovers they can run real-time process optimization across every plant simultaneously. A hospital system can model patient outcomes at a level of granularity that was previously unthinkable. A logistics company rebuilds its entire routing infrastructure around live AI inference. None of those use cases were in anyone's demand forecast, because they only became conceivable once the compute existed to make them viable.
The capacity doesn't just serve the demand that exists. It creates the demand that comes next. And then that demand creates the use case after that. It spirals up.
Jensen said it plainly on NVIDIA's most recent earnings call: "Today's data centers are revenue generating AI factories constrained by power." And then this: "Demand has gone parabolic. The reason is simple. Agentic AI has arrived. AI can now do productive and valuable work. Tokens are now profitable, so model makers are in a race to produce more."
Meanwhile the physical world moves slowly.
Permitting. Grid connections. Construction. Cooling systems. Power generation.
You can't just add another 5GW of capacity every time models improve and compute demand accelerates. The physics don't care that every industrial company on earth is about to integrate AI into its operations.
The real constraint in AI is increasingly time-to-compute.
AI demand grows exponentially.
Infrastructure doesn't.
Every few months we see another meaningful step change in model capability. Better reasoning. Better coding. Better multimodal understanding. Better agents. The latest generation of frontier models feels like one of those moments.
Every time that happens, usage increases, new products emerge, enterprises deploy more workloads, and entirely new use cases become viable. All of those things compound on top of each other.
Think back to the dial-up era. The internet existed. Email worked. Websites loaded - eventually. But the sluggishness of the experience fundamentally constrained what people imagined doing with it. The technology's own limitations shaped the universe of perceived use cases.
AI is in exactly that phase right now. The friction of slow inference and expensive compute subtly depresses AI ambition and imagination. That friction is a function of compute scarcity. It will not persist.
And here is where it gets interesting. It's not just that more compute serves existing demand. It's that more compute creates demand that didn't previously exist. It's like building more highway lanes to reduce traffic. The lanes don't just move the cars that were already queuing, entirely new trips get made that nobody was taking before. People move further from the city. New suburbs get built. New businesses open along the route. New industries restructure themselves around the road.
AI compute behaves the same way. A manufacturer discovers they can run real-time process optimization across every plant simultaneously. A hospital system can model patient outcomes at a level of granularity that was previously unthinkable. A logistics company rebuilds its entire routing infrastructure around live AI inference. None of those use cases were in anyone's demand forecast, because they only became conceivable once the compute existed to make them viable.
The capacity doesn't just serve the demand that exists. It creates the demand that comes next. And then that demand creates the use case after that. It spirals up.
Jensen said it plainly on NVIDIA's most recent earnings call: "Today's data centers are revenue generating AI factories constrained by power." And then this: "Demand has gone parabolic. The reason is simple. Agentic AI has arrived. AI can now do productive and valuable work. Tokens are now profitable, so model makers are in a race to produce more."
Meanwhile the physical world moves slowly.
Permitting. Grid connections. Construction. Cooling systems. Power generation.
You can't just add another 5GW of capacity every time models improve and compute demand accelerates. The physics don't care that every industrial company on earth is about to integrate AI into its operations.
The real constraint in AI is increasingly time-to-compute.
𝐓𝐰𝐨 𝐊𝐢𝐧𝐝𝐬 𝐨𝐟 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. 𝐎𝐧𝐞 𝐂𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐬 𝐭𝐡𝐞 𝐎𝐭𝐡𝐞𝐫.
When people talk about AI infrastructure, they tend to conflate two very different things. The distinction matters.
Layer 1 is the physical foundation - power, land, substations, data centers. Layer 2 is the compute that sits inside them - GPUs, servers, networking.
IREN has spent eight years securing that Layer 1 advantage. The land. The power. The substations. Data center sites being developed and built across multiple continents. We were building before it was obvious.
And we are deploying Layer 2 into it right now, at scale. GPUs online, generating revenue, building the operational track record that makes every subsequent deployment faster and more credible.
The barriers to entry are already forming. Developing a large-scale new data center today won’t be online until the end of the decade. And the asset-light neocloud trying to compete by renting capacity is discovering that sites were locked up years ago, and the operators utilizing them aren’t subletting. By the time new entrants solve for land, power and permitting, IREN will have gigawatts online, execution track record, and customer relationships that took years to build. That gap doesn’t close. It compounds.
Vertical integration across both layers is not just a structural advantage, it is a commercial one. Controlling the physical infrastructure and the compute sitting on top of it means faster deployment, greater certainty for customers, tighter operational optimization, and lower dependency risk at every stage. There are no landlords to negotiate with, no capacity constraints imposed by third parties, no contractual barriers between an operator and their own infrastructure, no misaligned incentives between the infrastructure owner and the operator. The economics improve as the platform scales, and the customer experience improves with it. That is what owning the full stack actually means in practice.
When people talk about AI infrastructure, they tend to conflate two very different things. The distinction matters.
Layer 1 is the physical foundation - power, land, substations, data centers. Layer 2 is the compute that sits inside them - GPUs, servers, networking.
IREN has spent eight years securing that Layer 1 advantage. The land. The power. The substations. Data center sites being developed and built across multiple continents. We were building before it was obvious.
And we are deploying Layer 2 into it right now, at scale. GPUs online, generating revenue, building the operational track record that makes every subsequent deployment faster and more credible.
The barriers to entry are already forming. Developing a large-scale new data center today won’t be online until the end of the decade. And the asset-light neocloud trying to compete by renting capacity is discovering that sites were locked up years ago, and the operators utilizing them aren’t subletting. By the time new entrants solve for land, power and permitting, IREN will have gigawatts online, execution track record, and customer relationships that took years to build. That gap doesn’t close. It compounds.
Vertical integration across both layers is not just a structural advantage, it is a commercial one. Controlling the physical infrastructure and the compute sitting on top of it means faster deployment, greater certainty for customers, tighter operational optimization, and lower dependency risk at every stage. There are no landlords to negotiate with, no capacity constraints imposed by third parties, no contractual barriers between an operator and their own infrastructure, no misaligned incentives between the infrastructure owner and the operator. The economics improve as the platform scales, and the customer experience improves with it. That is what owning the full stack actually means in practice.
lets all just get along...
yeah, trade!
10 premises in exchange for '#StarWars' IP??