고정된 트윗
Jonathan
34 posts
This is counterintuitive for some, which is why there’s a paradox named after it. But if you lower the cost of something that was previously supply constrained, demand for that thing goes up. Software engineering is just one of the easiest examples to contemplate.
The process goes like this: every small business, every IT team, every large enterprise sees that engineering can now drive vastly more output. They then start to consider all the new things they can build or automate. They even test building prototypes themselves.
They only get so far with that approach because they realize there are still 50 other tasks that go into building software and maintaining it. So they start to hire more engineers to do that work. All of this for work they never would have considered automating or having software for if AI didn’t exist.
So yes, automating tasks, in plenty of fields, will lead to demand for experts, not less.
Puru Saxena@saxena_puru
The software industry is apparently dying but job postings for software engineers are rapidly rising!
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The atomic unit of work is a task.
Imagine being able to leverage, customize, and integrate world class task blueprints for “Systems of Work” from @Microsoft @Google @OpenAI @AnthropicAI
A task blueprint is a holistic representation of how work is completed.
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Work Intelligence Operating System
@AnthropicAI Cowork + Plug-Ins and similar systems of work will redefine the workflow and how tasks get completed.
Let’s go build. 🛠️🚀
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@ramkris @JayaGup10 A system of intelligence captures business context and action
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reality of enterprise finance:
the ERP isn’t the system of record.
the general ledger (ERP) stores state.
the sub-ledger layer stores decisions – exceptions, overrides, precedent.
that layer lives in the execution path today as:
ERP add-ons, Excel patchwork, and human judgment.
that’s where accounting actually happens:
why this recon broke,
why this accrual was overridden,
why this treatment changed,
why this escalation happened this time.
which is why AI-native ERPs miss the point for MM & enterprise.
the next system of record captures decision traces, not just rebuilding another GL.
Jaya Gupta@JayaGup10
Every other week the same debate comes back: agents kill everything vs systems of record survive. It's a fun debate. It's also the wrong one. The better question: do entirely new systems of record emerge? Systems of record for decisions, not just objects. And do those become the next trillion-dollar platforms?
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Which company should @Apple acquire?
I choose @perplexity_ai as it can become Apple’s intelligence interface.
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@Joris_DLN @fairmint Onchain Equity Will Become The Default Technology Infrastructure That Will Power Businesses.
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$1B+ in onchain equity using @fairmint 🚀
Two years ago, putting cap tables onchain sounded crazy.
Today, everyone's asking how fast they can migrate & have assets DeFi-ready.
The infrastructure shift is happening. It's now🚀
Read this thread👇
Fairmint@fairmint
$1B+ in equity is now onchain with @fairmint . The spreadsheet era is ending. Programmable equity is here, paving the way for regulated DeFi. 🧵 Full story below 👇
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@jessepollak @coinbase I believe @Joris_DLN and @thibauld from @fairmint can provide insights on bringing equity onchain.
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today: @coinbase is the first onchain company to join the S&P500
next step: bring the S&P500 onchain 🔜
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AI Agents are *the* big topic for enterprises right now. In most conversations with IT leaders, this is the main thing they are figuring out strategies for.
There is a lot of excitement and momentum, and equally a realistic sense of the work that’s ahead. Here are some of the major topics that come up when companies start to think about deploying AI Agents at scale:
1. Agent interoperability: enterprises don’t have their data in just one environment. Salesforce may have their CRM records, Box their documents, Workday their HR data, or ServiceNow their HR and IT workflows; and plenty of Agentic use-cases will span these systems. As an industry we will need to design more interaction patterns for how AI Agents talk to each other and exchange data in the future.
2. Over-permissioned systems: lots of enterprises deal with the fact that software has “overshared” information over the years. This is fine when a human can’t possibly find everything across the tools, but a huge liability when Agents can get access to nearly anything, instantly, and return them to a user. Software products will have to take permissions and access controls more seriously, and we may need new AI Agent permission paradigms to help customers deal with this.
3. Data needs to be in an AI-ready environment: decades of technology being adopted in an enterprise means decades of systems that have important data but are not in environments that AI Agents can easily talk to. There will need to be a continued modernization push to modern, cloud environments, as retrofitting these systems will almost certainly not work.
4. Compliance: given we’re insanely early in the adoption of AI, most industries still haven’t figured out their official stance on where AI can provide suggestions or make decisions. Most regulated industries (like healthcare, life sciences, or financial services) are still in the early innings of developing shared standards for this, and some will need regulatory clarity to be able to do far more.
5. AI Agents executing the work: for a while the standard has been to have a human in the loop when AI is invoked in a workflow. But this becomes increasingly impractical for all steps of the work as workflows become more agentic. Companies will have to go through their own processes for setting their own policies on how much and when you can hand off to an AI Agent.
6. AI model quality: we continue to see rapid breakthroughs from the latest AI models on performance and capabilities, but for certain high value workflows we’re still not 100% there. For instance, you wouldn’t want to vibe code huge pieces of software for a production system, or rely only on AI for mission critical decisions fully just yet. This means we still need constant progress in the model space to get us there.
7. Change management: this will probably realistically take the longest. You can’t take a process that’s been run for decades or a century and expect it to radically transform overnight. In particular, most companies still are working what the best order is of deploying AI to get the biggest impact relative to the change required.
8. Business models of AI Agents: at the moment AI Agents still have a variety of business models being tested. Customers can somewhat feel that we’re early as an industry in getting to the ultimate pricing model of agents, and more stability here will likely go a long way.
In all, enterprises are rapidly trying to figure out this space, but the tech industry as well will need to continue to make major progress on these topics to accelerate the transformation. Exciting times ahead!
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@awilkinson @awilkinson check out Destiny Tech100 Inc. (Ticker Symbol = DXYZ) listed on NYSE.
destiny.xyz/tech100
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Are there any public companies that have exposure to OpenAI/Anthropic outside of the obvious ones (Microsoft/Amazon)?
As in, have any holding companies bought secondaries in OpenAI/Anthropic or similar?
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