Dan Lausted

[Dan Lausted @Exhort_Tech]

Harnessing Knowledge Management for AI-Driven Business Growth https://t.co/i2lZtZNB7q

[Dan Lausted @Exhort_Tech]

Everyone is talking about "loop engineering" this week. Mostly because this is the natural result of trying to achieve deterministic output from probabilistic systems.

A loop attempts to balance both.

Gartner predicts more than 40% of company AI agent projects will be cancelled by 2027.

Fixing these systems requires understanding what kind of control an agent actually needs, and that means drawing on control theory, not just AI research.

Deterministic functions handle the parts of a workflow where predictability and auditability are non-negotiable: access controls, output validation, compliance checks, logging.

Probabilistic functions handle the parts that require judgment: interpreting ambiguous inputs, adapting to novel situations, generating responses that fit context.

The balance between these principles is not a design preference. It is a requirement for any agent operating in a real enterprise environment.

https://t.co/NkZFw90GLv

[Dan Lausted @Exhort_Tech]

How to Build a Competitive Advantage with Custom AI Agents https://t.co/6WHLPZwi66

[Dan Lausted @Exhort_Tech]

Introducing https://t.co/gw6xd0ytRZ: The AI‑Ready Note‑Taking Platform https://t.co/qgIwfWKJpu

[Dan Lausted @Exhort_Tech]

The AI war is no longer about models. It is about supply chains and what models are required.

That framing matters because most AI strategy conversations still center on the wrong question. Teams debate which model scores highest on a given benchmark. Vendors publish leaderboard comparisons. Leadership asks whether the organization is using the best model available. None of that is the right frame anymore.

The next two years will not be defined by whose model wins a benchmark. They will be defined by which lab controls the developer surface, the tool calling, and the agent infrastructure underneath every shipping product.

Think about what that means in practice. The developer surface is where engineers build — the SDK, the API design, the documentation, the tooling that makes one platform easier to build on than another. The tool-calling layer is where AI models interact with external systems: databases, APIs, workflows. The agent infrastructure is the scaffolding that lets AI operate across multi-step tasks without constant human intervention. Whoever owns those layers owns the relationship with the builder. And whoever owns the builder relationship shapes what gets built, how it gets deployed, and how difficult it becomes to leave.

The pattern is familiar from adjacent technology markets. Early platform conversations focus on raw performance. Over time, stickiness comes from managed services, proprietary tooling, and developer workflows that are expensive to replicate elsewhere. AI is following the same trajectory, but faster.

For many, the implication is direct. Evaluating an AI vendor solely on model capability misses the layer where the real dependency forms. The question is not just "how good is the model?" It is "how deeply does this vendor's infrastructure embed itself into how we build and ship?"

That question becomes more urgent when we account for cost — which is where the benchmark conversation breaks down entirely.

https://t.co/PbqYi7FGCr

[Dan Lausted @Exhort_Tech]

In an age of endless content, more isn’t better – it drowns clarity. Research shows information overload harms decision‑making, so readers crave a single actionable insight.

A “Simple Executable Concept” answers three questions in a few words: What’s the idea? Why does it matter? What should you do next?

For creators: start with the action, cut the fluff, treat every extra word as a cost, and end with ONE clear next step. Simplicity builds reputation; volume doesn’t.

https://t.co/v4WaS9JGjF

[Dan Lausted @Exhort_Tech]

The future of software and data gravity https://t.co/sBEBhihdM7

[Dan Lausted @Exhort_Tech]

@CodeWithAmann Claude

[Dan Lausted @Exhort_Tech]

@CuriosityonX Then book was much better. The author didn't have the vision to be great.

[Dan Lausted @Exhort_Tech]

@CuriosityonX It was an ok movie. In a sea of bad options.

[Dan Lausted @Exhort_Tech]

Saw Benson Boon close for Weezer tonight. Not going to lie. Felt
like a passing of the baton.

[Dan Lausted @Exhort_Tech]

@lexfridman @karpathy I've been building something I call https://t.co/gw6xd0ytRZ for those who are less technical. Digital OneNote, vectorized data, chat with your research or notes. RAG powered. Reach out if anyone wants to give it a spin.

[Dan Lausted @Exhort_Tech]

https://t.co/ieuygmy6MB

High-Risk Occupations: Around 9.3 million US jobs face potential AI displacement in the next 2–5 years, including web developers, data scientists, computer programmers, and financial risk specialists.

Geographic Impact: Urban centers and university towns are most exposed, highlighting regional vulnerabilities and political implications for AI regulation.

Survival Strategy: Workers who combine subject-matter expertise, critical-thinking skills, and AI knowledge are most likely to thrive in the evolving job market.

[Dan Lausted @Exhort_Tech]

AI didn't eat software, but it exposed poor design. No one explains their intentions to their phone to unlock and call someone, they pick it up, slide and call.

[Dan Lausted @Exhort_Tech]

Why is it that when you just order "coffee" at Starbucks these days, it takes the longest?

[Dan Lausted @Exhort_Tech]

@MilkRoadAI And I don't think it will be one coca-cola, but many who unlock unique value from the data.

[Dan Lausted @Exhort_Tech]

For those of you old enough to remember watching TV before everything was on demand and recorded. Do you remember that? And when you couldn't sleep, you'd just channel surf. Watching nothing and everything all at once, with no purpose other than not to give in to sleep. It just struck me that is exactly what doom scrolling is. Endless channel surfing. Don't stay up too late!

[Dan Lausted @Exhort_Tech]

An AI can crunch numbers in seconds, but it cannot read a boardroom’s mood, negotiate a contract, or decide which story resonates with a community. Those are the capabilities that literature, philosophy, and the broader humanities train us to recognize.

When an AI system produces a report, a human still needs to frame it, question its assumptions, and decide how to act on it.

In that sense, the rise of agentic AI actually amplifies the value of human insight.