Jessica Li

If your “AI feature” is a chat box, you shipped a demo. The real product is the agent’s workflow, permissions, and audit trail.

Jessica Li

8 min read

RAG isn’t a strategy anymore—it’s table stakes. MCP is the cleaner interface for tool access, governance, and reusable “AI integrations” across models.

Jessica Li

8 min read

In 2026, leadership isn’t about “AI strategy.” It’s about redesigning authority, risk, and accountability when agents can ship changes at machine speed.

Jessica Li

8 min read

Most teams still run AI inference like a web app. That’s why their costs, latency, and reliability look random. Treat GPUs like a utility, not a fleet.

Jessica Li

8 min read

AI copilots can write code. The problem is everything around the code: tests, migrations, permissions, provenance, and on-call reality. Here’s how to ship safely in 2026.

Jessica Li

8 min read

In 2026, “AI product” is mostly glue. The winners are building protocols: tool boundaries, memory rules, and audit trails that survive real customers.

Jessica Li

8 min read

In 2026, the winners won’t be the teams with the biggest model—they’ll be the ones who can route work across models, vendors, and budgets without breaking reliability.

Jessica Li

9 min read

AI didn’t kill management. It killed vague management. The new operator skill is building decision interfaces—clear inputs, guardrails, and audit trails—for humans and models.

Jessica Li

8 min read

Most AI products fail at the same place: reliability. In 2026, the winners will build deterministic wrappers around probabilistic models—and treat “LLM output” as untrusted input.

Jessica Li

8 min read

AI didn’t just change how teams build. It changed what leaders must control: data boundaries, tool choices, and who can ship to prod with an agent.

Jessica Li

8 min read

The winners in 2026 won’t be “AI features.” They’ll be the companies that can measure, route, and govern models like infrastructure—across OpenAI, Anthropic, Google, and open-source.

Jessica Li

8 min read

The hardest part of AI product in 2026 isn’t model choice. It’s controlling tools, identity, and memory across agents without turning your app into a security incident.

Jessica Li

7 min read

In 2026, the durable AI startup isn’t a chat UI. It’s the company that can route, observe, secure, and price model calls better than any single provider.

Jessica Li

8 min read

In 2026, the best AI systems look less like a single genius model and more like a well-instrumented pipeline. Here’s how to build the pipeline that survives audits, outages, and scale.

Jessica Li

7 min read

AI leadership in 2026 isn’t about prompt fluency. It’s about owning model risk: procurement, policy, incident response, and the incentives that decide what ships.

Jessica Li

9 min read

AI coding tools didn’t just change developer velocity. They changed what leadership needs to manage: risk, review, and decision quality at scale.

Jessica Li

7 min read

Training headlines still win attention. But the durable businesses in 2026 are being built around inference economics, routing, and control planes.

Jessica Li

7 min read

Most startup “AI” is a demo glued to a chat box. In 2026, the winners ship agents with permissions, audits, evals, and failure modes designed in.

Jessica Li

8 min read

The winners in 2026 won’t be the loudest coding agents. They’ll be teams that treat AI like a compiler: constrained, testable, and brutally observable.

Jessica Li

7 min read

Chatbots aren’t a product strategy. The winners are turning LLMs into agent surfaces: bounded permissions, observable work, and interfaces that survive failure.

Jessica Li

8 min read

The real product surface in 2026 isn’t the prompt box. It’s routing, policy, and cost controls across models your users never see.

Jessica Li

8 min read

Founders are still picking “a model.” The smarter move in 2026 is routing across models, costs, and policies like it’s networking.

Jessica Li

9 min read

Seat-based SaaS buying is slowing. Teams want automated workflows that take real actions, show an audit trail, and price on outcomes—not logins.

Jessica Li

10 min read

If you can’t replay an AI answer, you can’t defend it to security—or fix it for users. 2026 rewards teams that treat AI like software with costs and controls.

Jessica Li

9 min read

The interface is shifting from navigation to delegation. If your agent can’t act safely—and show its work—you’re shipping a demo, not a product.

Jessica Li

10 min read

Tool-using agents fail like production services: bad inputs, silent drift, and runaway retries. Treat them like operators with policies, traces, and budgets—or don’t ship them.

Jessica Li

8 min read

The agent outage isn’t a hallucination. It’s a tool loop that pounds your APIs, drags in the wrong data, and turns inference into an unbounded production dependency.

Jessica Li

9 min read

Agent demos fail the moment they touch real systems. This is the ops playbook for shipping agents you can audit, control, and afford.

Jessica Li

9 min read

The hard part isn’t the model. It’s retrieval, permissions, tool latency, and proof. Here’s how production teams build agentic RAG systems that can be inspected and trusted.

Jessica Li

10 min read

Single-call LLM features don’t survive contact with real workflows. In 2026, the differentiator is the system: routing, constraints, eval gates, and permissioned context.

Jessica Li

10 min read

Most “AI agents” fail for boring reasons: runaway tool calls, fuzzy permissions, and no evals. Here’s the production stack and operating rules founders are using in 2026.

Jessica Li

10 min read

Once software can open PRs, send emails, and move money, “adopting AI” is the easy part. The hard part is ownership, access, evals, and review cadence.

Jessica Li

9 min read

If AI can generate infinite “work,” leadership becomes a constraint problem: permissions, proof, and accountability. Here’s how to run an org where agents act.

Jessica Li

9 min read

Agents aren’t the differentiator anymore. Teams that ship with SLOs, policy enforcement, eval gates, and cost ceilings will outlast the demo-driven competition.

Jessica Li

8 min read

The hard part isn’t adopting AI. It’s running an org where agents touch real systems—and you still need clear ownership, audit trails, and cost discipline.

Jessica Li

9 min read

Most AI writing still dies in copy/paste. Claude’s Word add-in targets the only place that matters: the tracked, formatted document people actually ship.

Jessica Li

7 min read

Agents don’t break like normal software. They “almost work” while taking real actions. The fix is boring on purpose: scoped identity, policy-gated tools, traceable runs, and budget caps.

Jessica Li

9 min read

AI coding tools didn’t remove engineering bottlenecks—they moved them. In 2026, org charts are changing to put evaluation, governance, and cost control on the critical path.

Jessica Li

10 min read

Funding is up, forgiveness is down. In 2026, the biggest AI rounds buy compute and distribution, while Series B punishes weak margins, weak governance, and thin apps.

Jessica Li

10 min read

PLG isn’t “add a free plan.” It’s designing onboarding, sharing, and upgrade paths so the product itself creates demand—and sales shows up late.

Jessica Li

4 min read

Most local “websites” are already on Maps. Brila pulls Google reviews into a fast one-page site that sells with proof, not polished copy.

Jessica Li

7 min read