Nick Johnson

Thoughts not my own. Worried about where they come from.

[Aaron Levie @levie]

At Box, we just surveyed 1,640 IT leaders across the US, Japan, and Europe about agentic AI adoption. Many standout findings, but a big one was that the companies that adopted AI the most are planning to grow headcount the most.

Obviously lots of ways you can read that data and variables mixed in, but it’s actually quite intuitive that the companies that become most productive want to (and are able to) reinvest back into the business to keep getting the gains going.

The narrative of jobs being wiped out assumes that companies will take a fixed approach to what they want to be able for work on. What’s happening in practice is it’s causing companies to want to light up more engineering projects, sell to more customers, automate more processes to give time back, and more. That all leads to more work to be done by people.

[Box @Box]

https://t.co/qVKtdMoqJv

[Nick Johnson @gnjohnson]

@itsolelehmann Cowork

[Aaron Levie @levie]

When thinking through the future of software, it’s helpful to think through what will we produce more of vs. less of in the future due to agents. And what systems are tied to that production or consumption.

Whether it’s a new startup or existing platform, any system that is directly tied to areas where agents dramatically lower the cost or complexity of doing something that was hard before will see all new use cases emerge for its products.

Ali Ghodsi at Databricks called out that they’re seeing major growth because AI agents have made it far easier to query your data, which means any user in the enterprise can do this. This drives up data use cases.

Equally, any software that’s inherently tied to the increase in software that agents will produce will do well. Mike Cannon-Brookes at Atlassian has shared that they see higher adoption from users that are using coding agents.

At Box, we see this with the growth of companies wanting to tap into their enterprise content in a ways that would have been impossible to scale manually before. Extracting data from documents, analyzing research, or automating the production of content all become possible where it would have been infeasible before.

There are endless examples like this. So while some parts of software will get squeezed as use-cases compress into agents in some areas, there equally will be a ton that grow far more.

[Bret Taylor @btaylor]

AI is your ghostwriter, but you are the author.

I was speaking with my friend Arya about the complex dynamics software teams face now that most code is being generated by AI agents. There are very few natural bottlenecks to code “slop” - features over quality, and functionality over polish.

I have made a point of banning speaking about AI as the author of code. Codex/Claude didn’t write that code, just like your table saw didn’t cut the wood - you did, with the help of AI. If we start speaking about AI that way, we remove the accountability of the engineer for the quality of the system they are producing.

Arya put it succinctly in a way that I hadn’t heard before: AI is your ghostwriter, but you are the author. It’s your name on the cover of the book.

I think the next year of software engineering will be building out the tools that enable all of us to be proud to sign our name to and take accountability for the increasingly complex systems we produce with our agents.

[Paul Graham @paulg]

One of the biggest problems afflicting young writers is the belief that writing has to sound fancy — that it can't just sound like spoken English. Actually the more it sounds like spoken English the better.

[Matthew Berman @MatthewBerman]

OpenAI CTO of B2B: "We’re moving from an era of asking questions to stating goals"

[Box @Box]

SaaS will be supercharged by AI Agents. @levie tells @MatthewBerman why domain-rich platforms are ideal terrain for specialized agents steeped in deep workflow context.

[Raj Khandwalla, MD, MA, FACC @rajkmd]

🚨 New in @JACCJournals: TURING-HF Trial 🚨
Can AI + remote monitoring optimize heart failure therapy and reduce hospitalizations?
Our answer: Yes—by a lot.
🧵

[Aaron Levie @levie]

We live in a brief and fascinating moment right now where there’s an insanely wide gap in productivity between two people just based on the tools they use and their specific workflows with AI.

Just a few variables can be the difference between getting a 25% boost in productivity with AI or a 250% boost. Here are just a few that seem to be emerging;

* Choosing the right AI model creates a substantial amount of leverage. While models for basic querying in our personal lives have all generally reached the same level of utility, it’s clear there’s still major differences for complex tasks like coding, deep research, medical use cases, and other critical vertical tasks.

* Picking the right tool for interacting with an AI model is a major variable because the agentic experiences *and* the AI Agents within these platforms differ so heavily. The system prompts, context that each AI Agent is given, and tool use drive very different levels of performance for any given task.

* Understanding the best ways to prompt an AI Agent to maximize the results has a wide amount of variance. This may be the single biggest difference in outcomes in a lot of cases. Some people just type in a few lines and think the AI will take care of the rest, but the pros clearly spend a meaningful chunk of time just getting the prompt to be perfect.

* There are also bespoke workflows emerging that are hyper tuned to specific types of tasks. Some people will use a certain model and tool for creating a product spec, then another for writing the code, then another one for reviewing the code, and so on. Another common way of working is to give multiple AI Agents the same task and just compare which delivered the best result.

It’s wild that this particular pattern of work is both so effective, and resulting in substantial differences in productivity. Yet here we are. These differences should theoretically converge over time, but for the foreseeable future we’re seeing very different levels of output based on the user’s approach.

[Box @Box]

60% of companies expect to be transformed by AI within two years.

But here's the reality: most are still bolting AI onto legacy workflows instead of reimagining how work gets done. That's not transformation—that's just expensive automation.

Our latest research reveals what separates AI leaders from laggards. The companies winning with AI aren't just adopting new tools—they're redesigning how their business creates value from the ground up.

The challenge: Most organizations are nowhere near putting AI at the center of their strategy. They're missing the exponential gains that come from true AI-first thinking.

The solution: A complete playbook for IT leaders and CIOs ready to move at the speed of AI.

🎯 5 Core Principles of AI-First Companies:

1️⃣ AI as a capability expander — Enable work that wasn't possible before, not just faster versions of old tasks

2️⃣ Human-AI partnership — Augment human creativity and decision-making rather than replacing it

3️⃣ AI-native design — Build systems that seamlessly integrate thousands of AI agents working behind the scenes

4️⃣ Trust & governance foundation — Remember: "AI agents can't keep a secret"—never rely on AI to maintain core security models.

5️⃣ Data as strategic fuel — Transform unstructured content into your competitive advantage

The companies implementing these principles today are building insurmountable competitive advantages. They're not just working faster—they're working in entirely new ways.

Ready to lead your industry's AI transformation?

📖 Get the complete framework

Want to explore the benefits? Get the full details here: https://t.co/ePB1qsd740

[Aaron Levie @levie]

The most important factor for AI Agents is to get them the context necessary to execute the task successfully. No matter how powerful AI models get, context will always be king. Data, workflows, tools, domain knowledge, and tuned instructions will all be critical.

[Nick Johnson @gnjohnson]

The Democrats must make a choice via @FT
https://t.co/8U00dMImvu

[Bryan’s Gunn @bryansgunn]

I tell you what, that Santa Claus has got real presence about him

[Salesforce News & Insights @SalesforceNews]

Salesforce leaders including @benioff unveil Agentforce. ⬇️

With autonomous agents for service, sales, and more, plus a new Atlas Reasoning Engine and low-code Agent Builder, Agentforce is what AI was meant to be.

Learn more: https://t.co/bplJxbDXWC

[Marc Benioff @Benioff]

AI is truly revolutionizing sales! 📈 83% of AI-driven sales teams saw revenue growth this year vs. 66% without AI. Plus, 68% of AI teams expanded their headcount. Discover how AI boosts sales performance in @Salesforce’s latest State of Sales report! https://t.co/1YKfyp7PGj

[Salesforce News & Insights @SalesforceNews]

Behind the #1 AI CRM are scientists, researchers, and engineers enhancing @Salesforce products and pushing the field of AI forward.

@silviocinguetta, @shelbyh_ai, & @nikhil_ai share how @SFResearch has influenced CRM & society.

Get the full story: https://t.co/rbM3d1dbN3 #DF23

[Salesforce News & Insights @SalesforceNews]

Since 2014, @Salesforce has been performing groundbreaking research in #AI.

Principal researcher, @nikhil_ai, shares some of the highlights. ⬇️

Get the full story: https://t.co/rbM3d1dbN3 #EinsteinAI #AskMoreofAI #DF23

[Salesforce News & Insights @SalesforceNews]

Say hello to Einstein 1 Copilot 👋

Here's everything you need to know about @Salesforce's new generative AI-powered conversational assistant, and more of today's biggest news from @Dreamforce.

#DF23