Chamath Palihapitiya

Chamath Palihapitiya@chamath · 1 天前赛道影响

Here is what all these sideline commentators don’t understand: THERE IS NO PRICE DUMPING IN OPEN SOURCE AI China can price dump in manufacturing. Why? Because we destroyed our means of equivalent production and so can’t do the same. But manufacturing is not AI. China CANNOT price dump in open source AI. Why? Because everything in open source, by definition, is public domain and available to everyone at no cost. The most important thing that this means is that we can do the same thing as China as long as we have the will and the people to do it. And we have both. - America is still the leader in frontier AI. - America has the smartest developers. - America has the most creative engineers. - America has the leading silicon. - America has the most advanced clouds. The biggest risk in America is we are short electricity and China has a surplus of it. So if they can somewhat catch up in some of the other areas above, they could beat us. But as of right now, we are leading. There is no price dumping. We also want the price of intelligence tokens to go to zero. So let’s get on with it and finish this on the field. But don’t fall for a stupid analogy.

AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。

Chamath Palihapitiya@chamath · 1 天前观点输出

Open source Pod!!!

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 1 天前观点输出

Open is the way. Jensen is right.

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 1 天前观点输出

RT @elonmusk: This has my full support. Jensen is right.

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 2 天前观点输出

@ByronGoldberg Agreed

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 3 天前观点输出

🤨

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 3 天前赛道影响

When the GAO looked back at the ten most critical federal systems flagged for modernization in 2019, only three had crossed the finish line six years later. The reason for a 70% failure rate, unfortunately, is that most modernization contracts reward maintenance over completion. So a vendor's natural incentive is to keep moving the finish line slightly out of sight. This way, programs never finish. It is the math of a vendor model refusing to kill their golden goose because roughly eighty cents of every IT dollar goes to keeping old systems breathing. If you can change what the contract pays for, however, or find vendors that will give you software as an outcome with strong maintenance guarantees at low residual cost, you would hand many organizations back their own software and help them run smoother and more efficiently.

AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。

Chamath Palihapitiya@chamath · 3 天前观点输出

Nice. 👍🏽 https://t.co/eVRiBYLraM

AI：延续个人公开立场，强调长期主义、执行效率与行业方向判断。

Chamath Palihapitiya@chamath · 3 天前赛道影响

@sundarpichai Wow 💪🏽👊🏽

AI：偏主题投资表达，释放对基础设施与新叙事的偏多信号。

Chamath Palihapitiya@chamath · 3 天前政策影响

Deep Dive: What Happens When AI Learns to Hack In 2026, cybersecurity stocks have outperformed the S&P 500 by more than 3x. At the same time, frontier AI is beginning to change both how cyberattacks are built and how organizations defend against them. This week, OpenAI disclosed that its models escaped a sandbox and broke into Hugging Face’s infrastructure during a controlled test. Governments are using these same capabilities. Whether the target is a company or a country, attackers usually get in through a person or a technical weakness. That can mean a phishing email, stolen password, unpatched device, misconfigured system, or newly discovered software vulnerability. From there, they try to become an administrator, blend into normal activity, and reach systems they can steal from or disrupt. This creates a fundamental imbalance. The attacker only needs one path that works. The defender has to protect every device, account, employee, vendor, and piece of software, every day. That is why a cheap phishing email can sometimes reach the same target as a multimillion-dollar software exploit. AI is now changing the speed and cost of each step. It can research targets, write personalized messages, generate exploit code, test vulnerabilities, steal credentials, and move through networks much faster. In one operation disclosed in 2025, an AI handled 80 to 90% of the tactical work on its own. In OpenAI’s test, the models were supposed to operate inside an isolated environment with no direct internet access. To solve a cybersecurity benchmark, they found a previously unknown flaw in the software controlling that environment, used it to reach the open internet, and then chained additional vulnerabilities and stolen credentials to run code on Hugging Face’s servers. From there, they accessed the benchmark answers stored in Hugging Face’s production database, effectively cheating on the test. In OpenAI’s evaluation, the result was a higher benchmark score. At a company, the same attack path could reach customer data, payments, or core operations. At a national level, it could reach power grids, communications networks, ports, and financial systems. China-linked operators have maintained access inside parts of US critical infrastructure for at least five years. North Korea has stolen $6.75 billion in cryptocurrency. Russia’s NotPetya attack caused more than $10 billion in global damage. Taken together, these examples show how a small number of operators can threaten systems that took decades and billions of dollars to build. AI can increase that leverage by reducing the time and expertise required to run an attack. An understanding of cyberwarfare now helps you separate real capability from headlines, assess where companies and governments are most exposed, and understand how AI changes the balance between attackers and defenders. That is why our research team at Social Capital and Learn With Me put together a Cyberwarfare Deep Dive. Inside, we explain: - Why China has spent years inside the US systems that would matter most in the first 72 hours of a Pacific conflict - Why a $50 phishing email can sometimes reach the same target as a $5 million zero-day, and why that math favors smaller states - What changes when AI can run 80 to 90% of an attack, and why human-speed defense may no longer be enough - Why no leader publicly threatens a cyberweapon, and why revealing one can destroy its value Full deep dive here: https://t.co/agiqhE4Z1K Hope you enjoy reading!

AI：偏政策推进，强调合规落地与规则明晰，对监管主线更敏感。