Futurist. AI Guy. Web3 Guy. Digital Transformation Guy. Political Ranter. Sports fan. Public Speaker. Degen 🐧 🧙♂️. #godons ❤️🖤 📱💻🥩🍷 🏋️🇦🇺
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Joined October 2007
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2025 was a big year, 2026 is going to be bigger.
2025 was busy. It was full of learning, and full of building better systems and networks.
2026 is the year I start speaking more publicly.
• 20 years in tech & telecom
• Deep in AI + Web3 (without the hype)
• Elected local councillor
• Focused on SMEs, governance & community
This thread (re)introduces me and what I have to say. 🧵
Hamish Jones.btc 🐧 🧙♂️ 🤖 🇦🇺🥜 retweeted
当全世界的聪明人都在为近 4 万亿美元市值灰飞烟灭捶胸顿足时，硅谷风投大佬 Chamath 却掏出放大镜，找到了一个真正决定 AI 巨头生死的命门。
这位硅谷老狐狸抛出了一个暴论：眼下整场 AI 博弈的胜负，全看一单买卖。他指的，正是马斯克的 SpaceX 和 Anthropic 签下的那份算力合同，里面藏着一个“双向 90 天随时解约”的条款。
这事还得从这周的政治海啸说起。美国中期选举共和党遭遇了水门事件以来最惨的溃败，连红了几十年的德州都翻蓝了。州长们听见“数据中心”四个字就像见了瘟神，因为选民们早就受够了：这些庞大的机房除了替科技巨头赚钱，留给当地人的只有昂贵的水电费和噪音。
各地要是真给新数据中心按下暂停键，算力供给立马就得卡脖子。
就在这个节骨眼，硅谷的阶级成分瞬间清晰了。手里有现成地盘和电力的微软、谷歌、Meta、亚马逊，外加马斯克的 SpaceX，成了紧俏的“房东”；而像 Anthropic 和 OpenAI 这种得急需算力来迭代大模型的实验室，说白了，只是群没有房产证的“租客”。
在 Chamath 看来，这直接把马斯克送上了牌桌的主位。
手握着庞大算力，一旦数据中心审批放缓导致算力紧缺，现货价格可能会翻倍甚至涨到三倍。如果现货暴涨让原有长约变成了亏本买卖，SpaceX 随时可能直接动用“提前 90 天解约”的条款撕毁老合同，把算力按现货价重新计价。
到那时候，急需算力续命的 Anthropic 除了当场滑跪、认下翻倍甚至三倍的高价，根本没有第二条路可走。
就在这两天的富达路演上，面对机构把 IPO 估值从 2 万亿美元狠砍到 1.2 万亿的“骨折价”，CEO 达里奥为了保住上市，硬是诚恳地向买方投资者连说了三次“谢谢”。现在就开始赔笑脸了，真等到马斯克拔电源那天，他们更是连谈判的筹码都没有。
更损的是，这一刀要是真捅下去，等于当场给其他“房东”上了一堂生动的现实主义扫盲课，启发它们重新盘算手里的算力到底是留给自己用，还是拿去放高价租。而那些“二房东”们，为了不被踢出局，只能咬碎后槽牙，不计代价地去抢购市面上残存的算力。
听到这儿，旁边的 Jason 赶紧踩了脚刹车。他反驳说，马斯克跟 OpenAI 的奥特曼确实仇深似海，但跟 Anthropic 团队的关系好得像蜜月期，而且 Anthropic 还是他未来搞太空数据中心的好搭档。就算现货涨上天，马斯克也未必会干出直接掀桌子按现货价宰客的事，大家坐下来谈个“中间价”才更像人话。
但 Chamath 显然不相信硅谷的塑料兄弟情：“这跟关系好不好无关，跟谁是好公司坏公司也无关。这就是一张算账的表格，表格会直接给出答案。”
在这场近 4 万亿美元灰飞烟灭的魔幻风暴里，所谓的科技宏大叙事，剥开来看依然是最朴素的包租公哲学：只要电表转得够快、现货涨得够狠，再美好的革命友谊，可能也熬不过一张 Excel 表格，和一纸短短 90 天的逐客令。
Mate it’s not too late to delete this.
In a randomised trial, software developers using AI took 19% longer to complete tasks. Yet they believed AI had made them 20% faster.
At the ‘Evidence in Action’ event, I argued for a government that tests what works, and saves money by learning from the results. ministers.treasury.gov.au/mi… #randomistas
Readers added context they thought people might want to know
The 19%/20% figures are from a July 2025 METR study of experienced open-source developers using early-2025 AI tools, which the authors describe as an outdated snapshot; a 2026 follow-up found some evidence for speedup with newer tools.
metr.org/blog/2025-07-1…
metr.org/blog/2026-02-2…
arxiv.org/abs/2507.09089
Hamish Jones.btc 🐧 🧙♂️ 🤖 🇦🇺🥜 retweeted
True
After DOGE, Elon came to a conclusion most people missed.
He tried to reform the state from the inside. He failed. And he said it plainly: the government can't fix its own deficit. Nobody in power actually wants to cut. The machine protects itself.
So he drew the only logical conclusion. If the state can't be reformed, there are only two ways this ends.
Either global bankruptcy. Debt keeps compounding, interest payments eat the budget, and the system collapses under its own weight.
Or robots. AI and robotics grow the economy so fast that the debt becomes manageable, and abundance makes the old fights over scarcity irrelevant.
Here's the part almost nobody sees.
For two centuries, capitalism has had a marketing problem. Its gains are real but slow and invisible. 2% growth a year doesn't feel like anything. Your life gets better over 30 years and you never connect it to the system that did it. Meanwhile, the left's promises feel immediate and concrete: a check, a subsidy, a benefit. That's why the left wins on promises even when it can't deliver them. Capitalism produces the wealth, socialism takes the credit.
Robots break that asymmetry.
When productivity doubles in a few years instead of a few decades, people will see it. Things get cheaper, services get better, opportunities appear, in real time. For the first time in history, people will connect capitalism and their rising standard of living directly, almost instantly. The causal chain becomes visible.
And that changes everything politically.
So there are two scenarios.
Scenario one: we stay in the current inertia. No breakthrough, or a breakthrough we regulate to death. The debt wins. Living standards stagnate, then fall. A frustrated electorate turns to whoever promises the most, and that pushes toward a hard collectivist left. But collectivism fails, as it always does. And when it fails, the most organized, most disciplined collectivist force left standing isn't the old left. It's radical Islamism. Bankruptcy, then communism, then something worse.
Scenario two: robots win. Abundance arrives, and it arrives visibly. The link between free enterprise and prosperity becomes obvious to everyone. And this is the key point: it becomes obvious to everyone, including the minorities the left was counting on. Because their long-term incentive is the same as everyone else's. A kid in the suburbs gains infinitely more from a booming, abundant West than from a bankrupt state handing out promises it can't keep. Abundance is the most powerful integration machine ever built.
That's the Western renaissance.
The West doesn't need to win the culture war by arguing. It needs to win it by delivering. And for the first time, it has the technology to deliver fast enough for people to notice.
The race isn't left vs right anymore. It's robots vs bankruptcy.
Hamish Jones.btc 🐧 🧙♂️ 🤖 🇦🇺🥜 retweeted
We've launched these ads across Ben Carroll's electorate. Maybe next time he won't forget to pay, because we're sick of paying for Labor.
I'm opening a few Tech Check slots before Christmas for small businesses that want a quick, practical once-over on their tech setup.
Flat fee, clear outputs, zero fluff.
If you want your systems cleaner before January, message me.
I'm increasingly convinced we need to treat AI memory as ours, not an app feature.
My ideal setup?
Markdown files containing my context, preferences, skills and knowledge, stored somewhere I control and accessible across different AI models.
Am I doing this perfectly?
Not yet. But I'm working towards it.
Own your context. Rent the intelligence.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Great post.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
I've been thinking about AI agents and personality. (I am always nice to the robots)
If my AI agent is going to negotiate, collaborate and interact with other people's AI agents, should it behave like me?
Or should it compensate for my weaknesses?
Personally, I want mine to be more patient and organised than I am.
But if our agents are all better-behaved versions of ourselves, are they really representing us?
And what happens when two AI agents with completely incompatible personalities have to negotiate?
Are we heading towards AI personality clashes? Are the robots going to take over from humanity? Or are they going to fight each other?
Quick AI dependency audit:
1. Which AI platforms do you rely on?
2. Can you export your data and prompts?
3. What happens if your preferred platform disappears?
4. Could you switch providers without starting again?
The AI tools will change. Make sure you can change with them.
Steal my process for evaluating AI and Web3 projects.
Three questions:
1. What problem does it actually solve?
2. Does it genuinely need AI/blockchain?
3. Who controls the data, infrastructure and value?
(it's not 2017 anymore.)
If I can't get clear answers, I'm not interested.
Never would I ever build my entire business around an AI platform without considering what happens if it disappears.
Models change. Platforms change. Pricing changes.
Your data, knowledge and relationships need to remain yours.
Never would I ever automate a relationship.
Research? Absolutely.
Scheduling? Sure.
Remembering to follow up? Definitely.
But pretending an AI-generated message is a genuine personal interaction?
No thanks.
I want technology to help me build relationships, not impersonate me.
I came in today. I worked hard. Everyone else in the room worked hard. No time wasted on chores at home or just faffing around. Please keep doing it.
I haven’t been as active on X as I should be recently and I need to fix that.
I consume a lot of content - and learn a lot. But it’s been a while since I’ve proactively created content on X and I do have a bit to say.
So I’ve set myself a goal of posting every day for the next 30 days - not just reading and replying. I’ve been doing the same thing on other platforms and (duh) the engagement is going up.
So I am taking a deep dive back into the communities I love and sharing a bit more - rather than just cheering others along from the sidelines.
Let’s see how this goes.
Almost 20 years ago I was selling Motorola RAZRs. I'm still in telecommunications, but things have changed a bit.
We've gone from people talking to people to computers talking to computers.
And strangely, that might make human connection more valuable.
Hamish Jones.btc 🐧 🧙♂️ 🤖 🇦🇺🥜 retweeted
I'm in San Francisco this week. So is the Deputy Prime Minister, but we’re here for very different reasons.
He's meeting with Anthropic and OpenAI, working on Australia’s data centre pipeline.
I'm here as the co-founder of @_Firmable and a Partner at @GlitchCapital, meeting with Australian founders who moved here this year. The CGT changes came up in almost every conversation as part of the reason they left Australia to build their business in the US.
What a mess.
The AFR published two opinion pieces todaythat demonstrate this shambles.
In the first, Treasurer Jim Chalmers set out how Australia can benefit from AI if we get it right. And that’s a big if. The headline number he was spruiking was $150 billion of data centre investment by 2030.
Jim, data centres are the bottom of the stack. Concrete, power and imported GPUs. Enormous capex, very few jobs, and negligible compounding growth for our country. If that's only our AI strategy, we’re in trouble, as we're importing expensive chips, hosting someone else's product, and then buying the intelligence built on top of it at full retail.
The bigger opportunity is in using and complementing the frontier models. Products built on top of frontier models that solve real business problems. That's where jobs are created and where the productivity Chalmers is chasing actually comes from. I’m seeing it at what we’re building at Firmable, the companies we invest in at Glitch and the founders I’m meeting in the Bay Area.
In the second op-ed, @Shaun_Cartoon laid out what Chalmers is actually doing to the people who build that layer. Effective rates for founder and employee equity are doubling to 47%, which is why founders are leaving.
Australia is courting the frontier labs on one hand, and taxing the hell out of the Australian founders and builders.
You can't be a value-add exporter of AI when the value-adders are in San Francisco.
If Australia is serious about backing innovation, restore the 50% CGT discount for all businesses. This alone will generate far greater productivity gains for our country than data centres will.
