Ben Smith

founder

[Ben Smith @bcsmithx]

@pierreeliottlal BOT

[Ben Smith @bcsmithx]

@akoratana @JayaGup10 My cofounder and I are in. How to join? Were in SF

[David Ondrej @DavidOndrej1]

the single most important concept in 2026

[Ben Smith @bcsmithx]

We’re seeing the first interface forms that solve this. Claude Cowork lets you capture context, turn it into reusable skills, and orchestrate agents as a conductor rather than a coder.

[Aaron Levie @levie]

Context is king for AI agents. There’s going to be a massive premium in the future on having up-to-date context for all your most important best practices, decisions, roadmap items, specs, marketing materials, and other critical knowledge across your company.

People get a lot of context “for free” in a company. They know where they work, they have people next to them they work with, they can remember the rough outline of the company’s most recent quarterly goals to know if something seems good or bad to work on.

AI agents, on the other hand, come in overly eager and ready to work on whatever you give them. At one moment they’re a lawyer for one company and an engineer for the next. This is why context remains absolutely critical for them to execute well on what you want.

The teams and companies that take this seriously will have huge leverage and be steps ahead of those that don’t.

[Ben Smith @bcsmithx]

I get most of my exercise when claude is down

[Ben Smith @bcsmithx]

One trillion parameters, available for fine-tuning.

The largest model in their lineup now accessible to anyone with a credit card.

The ceiling for what you can customize just got a lot higher.

[Thinking Machines @thinkymachines]

Tinker is now generally available. We also added support for advanced vision input models, Kimi K2 Thinking, and a simpler way to sample from models.

https://t.co/nvaJHkGxc0

[Ben Smith @bcsmithx]

@fchollet most scientists spend their careers doing incremental curve-fitting within existing paradigms, the “invention” bar is set higher for ai than for phds

[Ben Smith @bcsmithx]

@deedydas still Opus

[Ben Smith @bcsmithx]

@theo

[Ben Smith @bcsmithx]

Still Opus

[Ben Smith @bcsmithx]

A researcher gave Claude Sonnet 4.5 an economics paper and its replication data.

The prompt: replicate the findings from the dataset. No detailed instructions.

Claude read the paper, sorted through the archive, converted the statistical code from STATA to Python, and worked through every finding.

A different AI model verified the results. Spot checks confirmed accuracy.

Here's why this matters: Academic research has a credibility problem. Many landmark studies can't be reproduced when other scientists try to verify them. The fix was always obvious: to systematically check the papers. But verification takes hours of expert time per study. Nobody could afford to do it at scale.

Now an AI agent does in minutes what took researchers hours. We could actually audit the research that shapes policy, medicine, and business decisions.

The question isn't whether we can verify science at scale anymore. It's whether we will.

[Ethan Mollick @emollick]

And of course, just making more PowerPoint is not a good outcome on its own... https://t.co/WUCjLIQBH3

[Ben Smith @bcsmithx]

2026 is all about Service as Software

[Jaya Gupta @JayaGup10]

"Service as Software" is Silicon Valley's hottest buzzword right now.

Everyone's talking about SaaS becoming service providers, but no one's explaining HOW. The answer? After 6 months of research and 100s of startup conversations, we have the answer: Systems of Agents.

We're looking at a $4.6T opportunity.

[Ben Smith @bcsmithx]

People want to buy outcomes

[Ben Smith @bcsmithx]

vector databases for vibes. knowledge graphs for relationships. SQL for transactions. the best architectures query all three.

[Ben Smith @bcsmithx]

AI lowered the cost of building software. it didn’t lower the cost of maintaining it.

that’s why SaaS isnt going anywhere in one sentence.

[Ben Smith @bcsmithx]

@OpenAINewsroom when competitors agree on plumbing it means the real race moved to a different floor

[Ben Smith @bcsmithx]

@yacineMTB Same

[Ben Smith @bcsmithx]

Two ways to sell vertical AI: the trojan horse or the boardroom.

Cursor and Clay enter through the individual contributor. They build communities where users share prompts and workflows. The bottom-up swell forces enterprise adoption.

Harvey and Sierra sell strictly top-down. Harvey used manufactured scarcity and a PwC partnership. Sierra's founders left Salesforce and Google to poach their former customers.

Clay hit $3B on shared recipes. Harvey hit $8B on enterprise contracts.

Both work. Neither is wrong.

[Ben Smith @bcsmithx]

The hottest role in vertical AI isn't prompt engineer. It's forward deployed engineer.

50% developer, 50% consultant. They write Python scripts to clean client data during the sales cycle. They fine-tune RAG pipelines for specific use cases. They build the product while selling it.

Harvey, Hebbia, Palantir—all hiring aggressively for this role.

Vertical AI isn't plug-and-play. It's platform plus services.

[Ben Smith @bcsmithx]

@thepatwalls