TLDR Founders 2026-06-15

The list is in your head. The messaging too. What's missing is the four hours it takes to find the right contacts, write sequences that don't sound generic, and actually send them. Every single week.

Lightfield's outbound agents run on your CRM, scoring accounts against your real won deals, drafting sequences from your actual customer language, and surfacing warm intro paths from your network. You review what's ready. You sell. 4,000+ startups already have.

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Most people who try AI are just occasional users. Large chunks of the population aren't using AI at all. AI use hasn't shifted much in the past six months to a year. The only thing that has substantially changed in that time is that the negative sentiment about AI has gone significantly up. Many people are holding back on AI use because of real AI concerns and a lack of perceived AI value.

The AI industry should be building a frontier ecosystem so that value flows broadly across every company, industry, and country. This ecosystem would own the learning loop that encodes its institutional knowledge, compounding its human and token capital. Platforms enable more value on top than is captured inside. Once implemented, companies will be able to create value for themselves and for the economy around them.

AI investing breaks software's pattern-matching because the system has more variables, they couple unevenly, and each decomposes into sub-variables on independent curves, so solving one constraint just changes which constraint matters next.

Everyone building AI sales agents reaches for voice first, but the data keeps showing that around 85% of prospects would rather handle it over chat, which is also far easier to deploy, monitor, and fix when it breaks. The teams seeing real results point AI at the routine top of the funnel, qualifying and triaging, then hand the high-value conversation to a human the moment nuance matters. If you are wiring up an AI sales motion, start where it actually converts, and resist making the machine do the part people still want a person for.

Stripe used to have a Slack channel called #crazyideas where employees could pitch ideas they thought the company could build. The only rule was that the ideas had to carry the real possibility of being wrong, as otherwise they could just be an obvious to-do. Anyone in the company, regardless of seniority or function, could submit an idea for anything. The channel worked because people weren't precious or protective of their domain - someone could pitch something related to a separate team, and if it was good, the person would get transferred to that team to help build it.

AI-forward companies are now reaching $10M to $25M in revenue with about 20 go-to-market people, while the rest of the field still needs closer to 35 to achieve the same result. The benchmarks under that shifted too, with median net revenue retention sitting around 108% to 110%, the top quartile holding above 123%, and more comp plans tying pay to net-new recurring revenue. Pricing is the other big move, as outcome-based and usage-based models spread because they track the value you actually deliver and can pull gross margins toward 90%.

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The Fusion API allows OpenRouter users to achieve Fable-level intelligence at half the price. Panels of models consistently outperform individual models. Beyond-frontier model performance can be achieved with frontier panels at much lower cost. Fusion creates a panel of several models, each with web search and bash tools enabled, to increase performance. A judge model reads every response and extracts the structure, then a synthesizer writes a final answer grounded in analysis.

The Open Knowledge Format (OKF) is an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. It is a vendor-neutral, agent- and human-friendly standard for representing metadata, context, and curated knowledge. The specification formalizes the small set of conventions needed to make LLM wiki patterns interoperable. This post details what OKF can solve for organizations and how to get started with it.

The honest truth about launch day is that almost nobody is as invested in it as the person planning it, and forcing a giant grand opening usually just sets you up to be let down. The real job is not to maximize the crowd but to get the right people there, the ones who will give you the benefit of the doubt and tell others because spreading it makes them look good. The Newton had one of the biggest launches of its era and faded anyway, because nobody recommended it afterward.

For decades, you picked a side, a company sold to businesses or to people, and the whole playbook followed from that one choice. The fastest-growing AI-native companies have stopped fitting in either box and increasingly win both at once, which scrambles how you price, sell, and defend the business. The moats that hold up are the unglamorous ones - proprietary data, the workflow loops people live in, network effects, and brand - the trust that makes someone pick you once the underlying model is a commodity.

Make something users like so much that they tell their friends and then scale the business.

Software development remains a collaborative, "multiplayer" effort, where code review plays a central role in engineering judgment.

A look at what data each platform looks at and considers.

Pivoting costs more now than it did five years ago.