1/ The AI era is no longer about "apps"โitโs about the "Energy-to-Inference" pipeline. I just synthesized the key takeaways from the Cisco AI Summit ft. @jensenhuang, @sama, @pmarca, and more.
Here is the 5-Layer AI Stack for 2026. ๐งต๐
2/ โก ENERGY: The New Moat. Power is now the primary determinant of AI velocity. Weโre seeing "Compute-Utilities" emergeโhyperscalers moving into nuclear and natural gas to secure gigawatt-scale capacity. No power = no intelligence.
3/ ๐พ CHIPS: The Bespoke Era. The "Inference Flip" is here. 80% of workloads are now inference, not training. The result? A move toward custom silicon (OpenAI/Broadcom) and hardware-agnostic software like PyTorch 3.0 to break the software lock-in.
4/ ๐ INFRASTRUCTURE: The Private AI Factory. Networking is the new wall. East-West traffic is 10x global broadband. Enterprises are moving away from public clouds toward private, terabit-scale factories to protect sovereignty and IP.
5/ ๐ MODELS: Beyond Language. Language was just the interface. The new frontier is "World Models" (shoutout @drfeifei@worldlabs). Weโre moving from software that talks to software that simulates physical reality.
6/ ๐ค APPLICATIONS: The Agentic Workforce. The "App" layer is collapsing into the "Model" layer. Coding is the first breakout workflow, but the goal is "Intelligence Autonomy"โagents pushing beyond "human-in-the-loop" to rebuild entire industries.
7/ ๐ฎ 2026 Prediction: Software won't be a product you buy; it will be a capability you own. The era of "renting intelligence" is closing.
Check out the full breakdown and the "Gigawatt Moat" theory here: https://t.co/Ig9plN6qDG
#AI #TechStrategy #SiliconValley #Energy #Agents #FutureTech @elonmusk@davidsacks47@DavidSacks
@a16z โThe app layer is backโ is an interesting framing. Put simply, AI is now being leveraged for P&L transformation, with the underlying capital owners also investing directly to create enterprise value.
I wrote about this broader shift here: https://t.co/jSLmtFgust
Very interesting. Steve Jobs is said to have answered โeveryoneโ when asked which team at Apple was responsible for innovation.
Most staff functions โ HR, marketing, innovation, compliance, finance, and others โ were created to standardize, govern, and scale specialized capabilities across multiple business or product lines.
With the advent of AI and agents, that model may start to change. A line of business or product team could become more self-sufficient, with AI agents increasingly performing or augmenting staff-function capabilities in a consistent, scalable way. This could shift organizations from centralized support functions toward more embedded, agent-enabled operating models.
This is bigger than โAI adoption.โ
What weโre watching is a repricing of enterprise returns โ shifting from labor toward compute, energy, and AI-orchestrated execution. In many ways, this is an enterprise P&L transformation cycle disguised as a technology cycle.
Thatโs why frontier AI labs, PE firms, and alternative asset managers are suddenly aligning around workflow transformation and operating leverage.
But the larger long-term risk to incumbents may not be automation alone.
It is AI-native companies using these new execution layers to disintermediate entire enterprise business models โ much like internet-native companies did in the digital era.
I explore this in my latest piece on the AI Execution Economy:
https://t.co/2z0lDBvRTm
The first phase of AI was about models.
The second phase is about execution.
Programming was the proving ground.
Enterprise operations are the real market.
The winners wonโt simply deploy copilots on top of legacy software.
They will redesign workflows around agents.
The rise of the AI Execution Economy:
FINTECHTALK: The AI Execution Economy https://t.co/2z0lDBvRTm
@JTLonsdale@beyondthearc@rshevlin@charlesepotts@karlmehta@davidsacks47@chamath@sama@garrytan
Important signal.
The AI market is shifting from:โWho has the best model?โtoโWho can transform enterprise workflows fastest?โ
Programming was Wave 1.Enterprise execution is Wave 2.
This is why deployment, orchestration, and workflow redesign are becoming strategic assets โ not just implementation services.
I recently wrote about this transition as the rise of the AI Execution Economy. https://t.co/2z0lDBvRTm
Great segment, Joe.
One way to frame this is through checks and balances.
The founders designed government with internal checks โ three branches โ and external checks, where citizens retain overarching rights. What is often missing in industry and regulation is an equivalent system of competition and choice.
The contrast is obvious when you look across sectors.
In less regulated or more competitively contested markets โ Uber, Airbnb, Big Tech, e-commerce, cloud, mobile apps โ value creation happened much faster. The rate of development was extraordinary because disruptors had room to challenge incumbents first, prove consumer demand, and then force the system to adapt.
Uber challenged taxi medallion monopolies. Airbnb challenged hotel and zoning incumbents. Amazon and Shopify changed commerce. Apple and Google opened mobile distribution. Cloud platforms let startups build without buying their own data centers. In each case, the market moved faster than the regulatory apparatus, and consumers benefited from speed, choice, lower friction, and new business models.
Now compare that with pharma and banking.
In pharma, regulation is obviously necessary because safety matters. But the problem is when incumbents use regulatory complexity as a moat. Smaller biotechs face approval timelines, compliance burdens, trial costs, and gatekeeping structures that large pharma can absorb far more easily. The result is slower innovation and fewer paths for challengers.
Banking has the same pattern. Regulation protects consumers and the system, but it also entrenches large institutions. New banks, fintechs, and payment innovators face licensing, capital, compliance, and supervisory burdens that incumbents helped shape and can better afford. This creates a system where โsafetyโ can become a euphemism for protecting the existing order.
The issue is not regulation versus no regulation. The issue is regulatory capture โ or more precisely, regulatory grab. Incumbents learn how to turn regulation into a barrier against competition.
That is the danger now with AI. Anthropic and its allies are trying their level best to define the rules of the game in a way that sounds like safety, but could easily become incumbent protection. Once the largest model companies help write the regulatory framework, the risk is that future startups are forced to ask permission from the very architecture their competitors helped design.
The deeper principle is this: society needs to balance the rights of incumbents with the rights of disruptors.
In politics, Trump was a force of nature and could break through the institutional barriers. But most disruptors are not like that. In business, especially in regulated markets, most challengers can be quietly suffocated long before consumers ever get a choice.
That is why competitive regulatory choice matters. A biotech should not be captive to one approval pathway forever. A fintech should not need to become a bank before it can challenge a bank. And an AI startup should not have to comply with a safety regime designed by Anthropic, OpenAI, Google, and Microsoft before it can even enter the market.
The free press, markets, courts, consumers, investors, and competing jurisdictions should all be part of the check-and-balance system.
Without that, regulation becomes less about protecting the public and more about protecting the powerful.
Could Sierra become the AI-native company that disrupts Salesforce โ the original SaaS trailblazer?
Salesforce defined the SaaS era. It took Marc Benioff @Benioff more than a decade after founding the company โ and years beyond its IPO โ to reach roughly $15 billion in market value.
Sierra got there in about three years as a private company with this raise announced today.
That contrast may tell the bigger story. Salesforce was built for the cloud-software age. Sierra is being built for the AI-agent age. And while Salesforce became a horizontal enterprise platform, Sierra is starting with a narrower wedge: customer service. But that wedge sits right in the heart of Salesforceโs empire.
Today, Sierra is still only a tenth of Salesforceโs valuation. But the speed of value creation is stunning. It raises an uncomfortable question: is Salesforce the incumbent platform that adds AI, or is Sierra the AI-native architecture that eventually absorbs the category?
There is also an irony in the leadership arc. Benioff brought Bret Taylor @btaylor into Salesforce as co-CEO in what looked, at the time, like both a business and political hedge. Bret Taylorโs appointment was not incidental but strategicโreflecting his deep entrenchment within Silicon Valleyโs power networks and positioning him, ultimately, to assume the role of Chairman at Twitter prior to 2020 election cycle.(I wrote about this https://t.co/JvYkbcwJw0).
Later reporting around โTwitter Filesโ fueled claimsโdisputed by someโthat the company under Taylor's and the then CEO leadership worked closely with government actors during the 2020 election cycle, with critics alleging bias against Donald Trump and the MAGA movement. Since then, both Benioff and Taylor appear to have adapted to the new political climate, positioning themselves closer to the Trump/MAGA center of gravity rather than fighting it.
But strategic tradeoffs have consequences.
The deal that once looked like succession planning may now look like the seed of disruption. Taylor left Salesforce and built Sierra. And if Sierra continues on this trajectory, the ultimate twist may be that Benioffโs chosen heir doesnโt inherit Salesforce.
He disrupts it.
Or perhaps, one day, acquires it.
@elonmusk@davidsacks47@realDonaldTrump@scottmcnealy@rabois@shaunmmaguire@mtaibbi@shellenberger@bariweiss
Sierra is raising $950 million from new and existing investors, led by Tiger Global and GV, at a valuation of over $15 billion. We now have more than $1 billion to invest in becoming the global standard for companies wanting to transform their customer experiences with AI.ย
Weโve never had such conviction in the opportunity for Sierra and our customers. Just a couple of years ago, we had four design partners. Now, Sierra is serving over 40% of the Fortune 50, and agents built on our platform are powering billions of customer interactions โ everything from refinancing homes to processing insurance claims, returning orders, and helping people raise millions in fundraisers.
Weโre deeply grateful to our customers for helping show whatโs possible. If youโre not yet using Sierra, weโd love to partner with you. https://t.co/tT4tBSeSoR
I got a few minutes with the greatย Duลกan Stojanoviฤ, Founding Partner atย True Global Venturesย โ a one-of-a-kind investor with a rare ability to spot major shifts before the rest of the market catches on.
He gave a sneak preview of the upcomingย TGV Conference, where the conversation will center on the ๐ง๐๐ฐ ๐ฌ๐ญ๐๐๐ค: ๐๐ ๐๐ง๐ญ๐ฌ, ๐๐จ๐๐๐ฅ๐ฌ, ๐๐ง๐ ๐๐ญ๐๐๐ฅ๐๐๐จ๐ข๐ง ๐๐๐ข๐ฅ๐ฌ.
The timing could not be better. It comes right on the heels ofย Animoca Brands, a TGV portfolio company, announcing stablecoin approval in Hong Kong alongsideย Standard Charteredย andย Hong Kong Telecom - Anchorpoint, the new co, will issue the HKDAP
TL;DR from the conversation:
๐๐ก๐ ๐๐ฆ๐๐ซ๐ ๐๐ง๐๐ ๐จ๐ ๐๐ ๐๐ง๐ญ๐ข๐ ๐๐ง-๐๐ก๐๐ข๐ง ๐ ๐ข๐ง๐๐ง๐๐ (๐๐๐ ): ๐๐ก๐ ๐๐๐ฐ ๐ ๐ข๐ง๐๐ง๐๐ข๐๐ฅ ๐๐๐ฒ๐๐ซ
๐๐๐๐ ๐๐ฌ ๐ญ๐ก๐ ๐๐๐ฐ ๐๐๐ข๐ง๐๐ซ๐๐ฆ๐: ๐๐จ๐๐๐ฅ๐ฌ ๐๐ง๐ ๐๐ ๐๐ง๐ญ๐ฌ ๐๐๐ค๐ ๐๐๐ง๐ญ๐๐ซ ๐๐ญ๐๐ ๐
๐๐ก๐ ๐๐ ๐๐ฌ ๐๐๐๐: ๐๐ ๐๐ง๐ญ๐ฌ ๐๐๐๐จ๐ฆ๐ ๐ญ๐ก๐ ๐๐ง๐ญ๐๐ซ๐๐๐๐, ๐๐จ๐๐๐ฅ๐ฌ ๐๐จ๐ฅ๐ ๐ญ๐ก๐ ๐๐จ๐ ๐ข๐ ๐๐ง๐ ๐๐๐ญ๐
๐๐ก๐ ๐๐ข๐ฌ๐ ๐จ๐ ๐๐ ๐๐ง๐ญ๐ข๐ ๐๐๐ง๐ญ๐ฎ๐ซ๐ ๐๐๐ฉ๐ข๐ญ๐๐ฅ
More here ๐ https://t.co/4yBvCJ7izj
@mazemoore@NateSilver538 isnโt acting as a data scientist here (nor he is one in the true sense)โheโs shaping narratives to fit a predetermined agenda, with the analysis bending to match the story.
Balaji, I think your assessment misses the central point: this conflict is fundamentally being used as leverage in a broader U.S.โChina trade negotiation. My view is that when Trump visits China in May, the message will be something akin to โtear down this wall, Mr. Gorbachevโ โ a symbolic demand for a rebalancing of the economic relationship. China, in turn, may agree to a more balanced trade deal, giving the U.S. room to accelerate reindustrialization. If that happens, the MAGA movement will likely celebrate it on July 4th as a defining victory, casting Trump as the figure who helped save America โ and, in their eyes, the West โ from a new form of communism cloaked in globalism.
This is great. Godspeed, David Sacks.
My only caveat is that AI is bigger than science and technology alone. It is closer to a new industrial revolution. Yes, the opportunities across the AI stack are enormous โ from energy, chips, and infrastructure to models and applications. But AI will also reshape education, the workforce, and the overall pace of human development in profound ways.
That is why PCAST would be better served by broader representation, not just from technology companies, but also from leaders in education, labor, industry, and public policy. I write/podcast about these things at https://t.co/FRf5PxlKez
In my latest FINTECHTALK with Bill Capuzzi, we unpack why the bigger story is not just brokerage infrastructure, but the new financial stack forming around:
Alts
tokenization
stablecoins
TradFi/DeFi convergence
prediction markets
AI as a force multiplier
Read/Listen - https://t.co/ISSlUnw6YR
In December, President Trump signed an Executive Order tasking us with the development of a national framework for AI, what he called โOne Rulebook.โ This was in response to a growing patchwork of 50 different state regulatory regimes that threaten to stifle innovation and jeopardize Americaโs lead in the AI race.
Today we are releasing that framework. It will help parents safeguard their children from online harm, shield communities from higher electric bills, protect our First Amendment rights from AI censorship, and ensure that all Americans benefit from this transformative technology.
We look forward to working with our colleagues in Congress to turn the principles we are announcing today into legislation.
https://t.co/9fwatPYP5M
If you Missed Jensen Huangโs GTC Keynote, Here Are the Big Takeaways
It was not just another product launch. The deeper story is that NVIDIA is no longer simply selling chips into an AI boom. It is helping define the operating logic of the next industrial era.
1.ย Physical AI is moving from sci-fi to platform shift
Jensen closed with an Olaf-like Disney robot โ a signal that humanoids are moving out of the lab and into the commercial imagination. Today that may show up in theme parks. Tomorrow it could extend much further: humanoids in businesses, and eventually in many homes.
The right mental model is not a niche robotics market. It is something closer to aย Cambrian explosion, or the early smartphone era, when a new device class rapidly became a new platform. The physical world is becoming the next compute canvas.
That is why the next chapter of AI is not just digital AI โ it isย Physical AI. World models, robotics, autonomy, and embodied systems are no longer side stories. They are one of the biggest vectors pushing AI from software assistance into real-world economic transformation. AI is moving from generating answers to generating action.
2.ย The inference inflection is here โ and the stack is being rebuilt around production
One of the most important themes Jensen reinforced is theย inference inflection. The center of gravity in AI is shifting from training to inference.
That matters because the market is moving from โbuild the modelโ to โrun the model continuously, efficiently, and at scale.โ This is where silicon design, orchestration, latency, deployment economics, and real-time responsiveness start to matter as much as raw model capability.
As inference becomes the dominant workload, the hardware and software stack must increasingly be optimized forย high-volume, low-latency production use casesย โ especially agents, real-time decisioning, and world models operating in dynamic environments. The implication is bigger than faster chips. The entire ecosystem is being redesigned for production AI. I had covered this an earlier piece here
3.ย The AI factory is the new industrial model
Jensenโs framing of theย AI factoryย may be the most important idea from GTC.
The old factory of the Industrial Revolution took in raw materials, labor, and energy, and turned them into finished goods. The AI factory takes inย data,ย compute, andย energy, and turns them into tokens, predictions, software, decisions, automation, and increasingly physical outcomes.
This is not just a metaphor. It is the correct mental model for where AI is going. See AI Factory image below.
4.ย Compute becomes the new labor โ and token-per-watt becomes the new productivity metric
In the AI factory model, compute begins to function like a new form of labor. Data is the raw material. Energy is the critical input. And output is no longer just content โ it is intelligence operationalized into products, workflows, services, and machines.
Software was about digitizing workflows. AI is aboutย industrializing cognition.
That is why Jensenโs emphasis onย token per wattย matters so much. It is an AI-era productivity metric โ the equivalent of energy efficiency and output optimization in the traditional factory model. The long-term bottleneck in AI may not be capability. It may be the ability to deliver intelligence efficiently. The constraint is shifting from โcan the model do it?โ to โcan the system do it economically at scale?โ
That means energy, compute efficiency, and deployment economics will become strategic differentiators.
5.ย This is bigger than a software cycle
I increasingly think AI should be understood not as just another software wave, but as anย Industrial Revolution-scale shift.
Software transformed workflows. AI transforms the production function itself.
This is the move from chatbot to factory, from software layer to production layer, from digital intelligence to physical intelligence.
6.ย We are entering the era of the AI-native company
Just as the internet era produced internet-native companies like Amazon, Google, Uber, Airbnb, and Facebook, this era will produceย AI-native companiesย designed from the ground up around AI factories, inference economics, autonomous systems, and machine-mediated decision loops.
The winners will not be the firms that merely add AI features. They will be the firms that redesign their business models and operating systems around AI as a production architecture.
The sectors where this transformation is already becoming visible includeย autonomous vehicles, customer support, software development, engineering, healthcare, robotics, and search. These are early indicators of a broader shift in which intelligence becomes scalable, programmable, and embedded into core operations. See screen shot from Jensenโs presentation showing the players in each of the category.
7.ย The AI application stack is getting clearer โ and OpenClaw could be a platform-shift moment
Another useful lens from GTC is the emerging application stack. The market is taking shape across several important layers: Also in screenshot below
Frontier model builders
Model-to-production platforms
Agent frameworks and protocols
Inference frameworks
This matters because the AI economy will not be won by models alone. It will be won by the systems that connect models to production, production to workflows, and workflows to outcomes.
That is also whyย OpenClawย could become one of those moments people later compare to the PC, Linux, the mobile phone, or ChatGPT. Agentic frameworks are not just developer tools. They shape how personal agents and enterprise agents are built, coordinated, and deployed. That has implications not only for productivity, but for the future human interface to computing itself.
8.ย Conclusion: GTC was a blueprint for the new industrial age
My core takeaway from GTC is this:
NVIDIA is not just supplying picks and shovels to an AI boom. It is helping define the blueprint for a new industrial age โ one whereย data, compute, energy, inference economics, and embodied intelligenceย become foundational inputs of economic power.
That is why this moment matters far beyond tech.
It is not just a software innovation cycle.
It is the early architecture of a new Industrial Revolution. https://t.co/Ig9plN6qDG @elonmusk@davidsacks47@rabois
My information consumption is now 1/4 X, 1/4 podcast interviews of the smartest practitioners, 1/4 talking to the leading AI models, and 1/4 reading old books. The opportunity cost of anything else is far too high, and rising daily.
I rebutted Anthropicโs recent AI jobs doomsday report in my X post below. But the bigger story is that they seem to be promoting a dystopian vision of AI in order to shape regulation in a way that favors Big AI incumbentsโpositioning themselves to control the agenda and reap the lionโs share of the benefits. @DavidSacks https://t.co/4NVCVru0iu
Anthropicโs new labor-impact chart (link in comments) is being read as a map of job loss. I think that overstates the case.
Their Figure 2 compares AIโs โ๐ญ๐ก๐๐จ๐ซ๐๐ญ๐ข๐๐๐ฅ ๐๐๐ฉ๐๐๐ข๐ฅ๐ข๐ญ๐ฒโ with current observed usage across todayโs occupational categories and implies that over time usage may expand toward capability. That is a useful near-term adoption lens, but a weak long-term labor-market model, because it assumes todayโs job categories will remain the right containers for tomorrowโs work.
๐๐ข๐ฌ๐ญ๐จ๐ซ๐ฒ ๐ฌ๐ฎ๐ ๐ ๐๐ฌ๐ญ๐ฌ ๐จ๐ญ๐ก๐๐ซ๐ฐ๐ข๐ฌ๐.
The printing press did not simply automate โscribesโ inside a fixed medieval job taxonomy. It reorganized knowledge production, created new firms and specialties, and helped cities with presses grow about 60% faster between 1500 and 1600. Technology changed the categories of work themselves.
That is the real weakness in doomsday AI charts: they measure exposure inside inherited buckets, while the buckets themselves are moving. BLS explicitly notes that occupation definitions change over time and that classification changes can create breaks in time series. ๐๐ก๐๐ญ ๐ข๐ฌ ๐ง๐จ๐ญ ๐ ๐๐จ๐จ๐ญ๐ง๐จ๐ญ๐ โ ๐ข๐ญ ๐ข๐ฌ ๐ญ๐ก๐ ๐ฉ๐จ๐ข๐ง๐ญ.
๐๐ก๐๐ซ๐ ๐ข๐ฌ ๐๐ฅ๐ฌ๐จ ๐ ๐ฉ๐จ๐ฅ๐ข๐ญ๐ข๐๐๐ฅ ๐๐๐จ๐ง๐จ๐ฆ๐ฒ ๐๐ง๐ ๐ฅ๐ ๐ก๐๐ซ๐. Anthropic is not just publishing research; it is also advocating targeted regulation, transparency rules for the largest AI developers, export controls, and stronger governance frameworks. It has funded AI-policy advocacy and holds a DoD agreement with a $200 million ceiling. None of this proves bad faith. But it does mean the company benefits from a policy climate in which AI is framed as powerful, destabilizing, and in need of compliance-heavy oversight that large incumbents can absorb more easily than smaller firms and open-source communities.
๐๐ฒ ๐ฏ๐ข๐๐ฐ: AI will disrupt work, but the bigger effect is not that todayโs occupations simply get hollowed out in place. The bigger effect is occupational recomposition โ new roles, merged roles, split roles, and new layers of supervision, verification, integration, and domain judgment. More importantly, this shift will dramatically increase the velocity of human progress. As AI augments discovery, design, and problem-solving, breakthroughs across medicine, science, engineering, and industry could accelerate exponentially. That means faster progress toward curing diseases, extending healthy lifespan, transforming energy systems, expanding into space, and unlocking breakthroughs we cannot even imagine today.
The printing press did not end knowledge work. It changed who did it, how it was organized, and which skills became valuable. AI will do the same.
๐๐จ๐ญ๐ญ๐จ๐ฆ ๐ฅ๐ข๐ง๐:ย Figure 2 mistakes a transitional occupational taxonomy for a permanent labor market โ and that framing conveniently supports a politics of AI regulation that favors incumbents over open innovation. Recent blog that talks about some of these things https://t.co/Ig9plN6qDG @elonmusk@sama@DarioAmodei@DavidSacks@davidsacks47@chamath@JTLonsdale@rabois@shaunmmaguire@balajis@realDonaldTrump
Anthropicโs recently published paper, which I rebut above, makes what I believe is a flawed argument about labor-market impact. https://t.co/r0GRwIWfPa
Anthropicโs new labor-impact chart (link in comments) is being read as a map of job loss. I think that overstates the case.
Their Figure 2 compares AIโs โ๐ญ๐ก๐๐จ๐ซ๐๐ญ๐ข๐๐๐ฅ ๐๐๐ฉ๐๐๐ข๐ฅ๐ข๐ญ๐ฒโ with current observed usage across todayโs occupational categories and implies that over time usage may expand toward capability. That is a useful near-term adoption lens, but a weak long-term labor-market model, because it assumes todayโs job categories will remain the right containers for tomorrowโs work.
๐๐ข๐ฌ๐ญ๐จ๐ซ๐ฒ ๐ฌ๐ฎ๐ ๐ ๐๐ฌ๐ญ๐ฌ ๐จ๐ญ๐ก๐๐ซ๐ฐ๐ข๐ฌ๐.
The printing press did not simply automate โscribesโ inside a fixed medieval job taxonomy. It reorganized knowledge production, created new firms and specialties, and helped cities with presses grow about 60% faster between 1500 and 1600. Technology changed the categories of work themselves.
That is the real weakness in doomsday AI charts: they measure exposure inside inherited buckets, while the buckets themselves are moving. BLS explicitly notes that occupation definitions change over time and that classification changes can create breaks in time series. ๐๐ก๐๐ญ ๐ข๐ฌ ๐ง๐จ๐ญ ๐ ๐๐จ๐จ๐ญ๐ง๐จ๐ญ๐ โ ๐ข๐ญ ๐ข๐ฌ ๐ญ๐ก๐ ๐ฉ๐จ๐ข๐ง๐ญ.
๐๐ก๐๐ซ๐ ๐ข๐ฌ ๐๐ฅ๐ฌ๐จ ๐ ๐ฉ๐จ๐ฅ๐ข๐ญ๐ข๐๐๐ฅ ๐๐๐จ๐ง๐จ๐ฆ๐ฒ ๐๐ง๐ ๐ฅ๐ ๐ก๐๐ซ๐. Anthropic is not just publishing research; it is also advocating targeted regulation, transparency rules for the largest AI developers, export controls, and stronger governance frameworks. It has funded AI-policy advocacy and holds a DoD agreement with a $200 million ceiling. None of this proves bad faith. But it does mean the company benefits from a policy climate in which AI is framed as powerful, destabilizing, and in need of compliance-heavy oversight that large incumbents can absorb more easily than smaller firms and open-source communities.
๐๐ฒ ๐ฏ๐ข๐๐ฐ: AI will disrupt work, but the bigger effect is not that todayโs occupations simply get hollowed out in place. The bigger effect is occupational recomposition โ new roles, merged roles, split roles, and new layers of supervision, verification, integration, and domain judgment. More importantly, this shift will dramatically increase the velocity of human progress. As AI augments discovery, design, and problem-solving, breakthroughs across medicine, science, engineering, and industry could accelerate exponentially. That means faster progress toward curing diseases, extending healthy lifespan, transforming energy systems, expanding into space, and unlocking breakthroughs we cannot even imagine today.
The printing press did not end knowledge work. It changed who did it, how it was organized, and which skills became valuable. AI will do the same.
๐๐จ๐ญ๐ญ๐จ๐ฆ ๐ฅ๐ข๐ง๐:ย Figure 2 mistakes a transitional occupational taxonomy for a permanent labor market โ and that framing conveniently supports a politics of AI regulation that favors incumbents over open innovation. Recent blog that talks about some of these things https://t.co/Ig9plN6qDG @elonmusk@sama@DarioAmodei@DavidSacks@davidsacks47@chamath@JTLonsdale@rabois@shaunmmaguire@balajis@realDonaldTrump