SODP Dispatch - 30 July 2026

What is open‑source AI? A software engineering researcher explains

By Jeffrey Young

You’ve probably heard artificial intelligence models described as “open” or “closed.” These are not descriptions of the model’s personality. Large language model AIs like the one under the hood of ChatGPT don’t have actual personalities, despite appearances.

The labels refer to whether all of the information about how an AI model works is publicly available and the model can be modified, or whether the model’s developer keeps its inner workings secret and the model itself private property.

Open-source software

The concept of open-source software originated in the free software movement of the 1980s and ’90s. The movement’s founders believed that software creators and users had the right to “four freedoms” – to run the program, to study and modify it, to distribute copies of the original, and to distribute copies of subsequently modified versions. The fundamental requirement was that the source code – the basic instructions – for a program should be made available.

In the late 1990s, software developers associated with projects such as the Netscape web browser and the Linux operating system coined and promoted the term “open source” to refer to these ideals.

As part of the evolving movement, certain organizations developed open-source licenses that specified how a particular piece of source code could be used and distributed, including the Gnu General Public License, Apache License, MIT License and the Berkeley Software Distribution. Each type of license also specified any potential restrictions on how software patents applied to the source code.

🚀 Introducing Benchmark Grid: Independent Benchmarking for Publisher Tech

Every publisher conversation surfaced the same problem: too many platforms, too little independent data to evaluate them with. Vendor directories are pay-to-play. Review sites go stale. Feature checklists tell you what a tool does, not whether it works for publishers like yours. So we built the missing layer, with 281 platforms assessed across 22 editorial categories spanning content management, distribution, revenue, advertising and analytics.

In practice, this means you can:

• Shortlist on evidence, not sponsorship — vendors cannot pay for inclusion, ranking or score changes, ever

• Compare on a published methodology — 45% Capability, 40% Publisher Fit, 15% TCO, applied consistently across every category

• Judge fit, not just a number — tools are grouped into Recommended, Worth Considering and Specialist, with assessments written by editors who actually work with newsrooms, magazine teams and B2B media

• Build your stack, not just pick a tool — the Build My Stack tool maps the right combination of platforms to your specific setup

As AI search becomes the layer through which recommendations get discovered, publishers need an independent source feeding it, one that gives every vendor a fair shot without tilting toward any single company. That is the solution Benchmark Grid is providing.

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📊 Publisher Revenue Dinner Miami 2026

We are hosting a private dinner in Miami for senior revenue leaders across news, gaming, newsletter, specialist, and brand publishing. This dinner is for publishing professionals who are actively driving revenue strategy and want a candid, peer-level conversation on how to win the biggest commercial quarter of the year. If you are a Chief Revenue Officer, CEO, Programmatic Head, or Partnerships Director making decisions around yield, inventory, first-party data, and Q4 growth, this evening is designed for you.

Date: Wednesday, September 16, 2026
Time: 6:30 PM
Venue: Coral Room, Rusty Pelican Miami Restaurant, 3201 Rickenbacker Cwy, Key Biscayne, FL 33149, United States

What you will walk away with

• Practical yield, personalisation, and first-party data moves that are lifting revenue per visit right now

• A candid read on how your peers are pricing, packaging, and positioning inventory for the Q4 surge

• Insight into which new formats — video, newsletters, and creator partnerships — are actually paying in 2026

• An attendee-only summary of the evening's key insights and growth playbooks shared around the table

The dinner is high-trust, collaborative, and entirely off-the-record under Chatham House Rule. No panels, no pitches, no slides — just one focused evening on how to make every session, impression, and first-party signal work at full value in publishing's most important quarter.

Seats are strictly limited to 15 attendees.

A big thank you to Ezoic for co-hosting this evening with us.

Apply for your seat here ▸

🎯 LiveRamp × OpenAI: Measure Your ChatGPT Ad Performance

If you're running or planning ads on ChatGPT, you can now close the measurement gap. LiveRamp's Conversions API (CAPI) Hub connects directly to ChatGPT ad campaigns via secure server-to-server data connections, meaning you get reliable conversion tracking without depending on browser-based signals that often break or go missing.

In practice, this means you can:

• See which ChatGPT campaigns are actually driving conversions — not just clicks

• Optimize spend in real time based on accurate performance data

• Justify and grow your ChatGPT ad budget with measurement data you can trust

As consumer journeys shift toward AI-powered platforms, this gives marketers the infrastructure to keep up, connecting data, measuring impact, and proving ROI on one of the fastest-growing ad surfaces in the world.

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By Vahe Arabian

⚙️ Why Disposable AI Tools Are the Truest Sign of a Healthy Publisher Roadmap

The publishing organisation that cannot tell how many tools it graduated out of last quarter does not have a disciplined roadmap; it has a slow one.

For twenty years, the cost of building software forced publishers to be conservative. Testing whether a newsletter personalisation idea worked meant a six-month engineering ticket competing against the paywall rebuild. Ideas died in the backlog. The ones that survived had to be big enough to justify the queue, which also made them too big to cancel. That constraint has quietly disappeared. Most publishing organisations have not updated their behaviour to match.

The evidence is concrete. Scott Klein scanned roughly 360 newsroom GitHub accounts and found that about a third of newly created public newsroom repositories in 2026 carry at least one AI-coding signal — a figure that was near zero through mid-2025 and hit 37% by February 2026. The individual cases are more instructive than the aggregate: a Washington correspondent with no programming background built 14 internal tools, one of which scans hundreds of Federal Register entries daily and flags the ones worth a reporter's attention. The prototype took a few hours. None of these started as strategy. They started as someone being annoyed by a repetitive task, which has always been the actual origin story of useful internal tooling.

The frame that clarifies everything here comes from Zeta Global's Chief Data Officer, who defines disposability not as a quality problem but a replaceability one. If a tool can be rebuilt in a day from a prompt, it is disposable, and it should be scoped and funded accordingly. He splits value into three layers: code (abundant, replaceable), products (differentiated but fragile), and systems (embedded, compounding, defensible). AI has collapsed the value of the first and pushed all the advantage into the third. For publishers, the systems layer is clear: your archive and its structured data, your first-party audience behaviour, your subscriber lifecycle, your editorial standards. Those compound. A dashboard that surfaces them does not.

The strongest counterargument came from media innovation journalist Ulrike Langer: time-to-prototype is irrelevant; what matters is time-to-trustworthiness. Turning a generated prototype into something trustworthy still takes months of testing and alignment. Her sharpest question remains unanswered: who is responsible in five years when there are hundreds of small helpers embedded in the stack and only a fraction are still in use? So the discipline is not "build less." It is build with an expiry date attached.

Here are three things worth acting on for publishers:

1. Report tools retired alongside tools shipped. A zero retirement rate means nothing is being honestly evaluated.

2. Surface the shadow AI use that is already happening. Your journalists are almost certainly already building things in personal accounts. You can bring that into a sanctioned space with guardrails, or leave it in a space with no visibility and no institutional memory.

3. Move the scarce resource . The bottleneck is no longer implementation; it is knowing what to build. That reallocates value toward people with deep domain knowledge of editorial workflow, audience behaviour, and revenue mechanics.

The publishers who navigate the next two years well will not be the ones with the best AI strategy document. They will be the ones who got comfortable building things they fully expect to delete.

Newsletter lessons from the top 50 news subscription brands | INMA

The world’s 50 largest digital news subscription brands now publish nearly 1,600 newsletters and alerts. Publishers increasingly treat newsletters as editorial products — not only promotion channels. When I first reviewed publishers’ newsletter strategies in 2018, most news brands offered fewer than 10 newsletters. At the time, newsletters were usually discussed as an inexpensive channel to drive traffic. In the past decade, the conversation has changed. E-mail is widely recognised as an engagement and conversion workhorse and increasingly treated as an editorial product on its own.

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Despite concerns about AI Overviews, some nonprofit news outlets see a surge in search traffic | NiemanLab

Election season is a busy time for any newsroom. In the second quarter of 2026, some nonprofit newsrooms saw their coverage of the primaries — especially voter guides — pay off in both interest and traffic. In Nieman Lab’s quarterly ranking of nonprofit news outlets, four news outlets saw a bump in traffic thanks in part to their coverage of primary elections in May and June. We bring you four of these each quarter, looking at four different market segments: for-profit local news sites, local newspapers, nonprofit news outlets, and public media stations.

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LinkedIn links app usage to profiles for over 1 million members. | PPC Land

LinkedIn last month expanded a feature that pulls real-time data from third-party software directly onto member profiles, turning daily use of tools such as HubSpot, GitHub, and Buffer into a visible credential that the company says more than a million members have already activated. The announcement, published on the LinkedIn Pressroom on June 17, 2026, describes a "connected apps" section that will appear on profiles once a member links an eligible application.

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Google On SEO Impact Of URLs Injected By CMS Platforms | SEJ

Google’s John Mueller answered a question on Reddit about a link to an internal web page that was automatically created by Squarespace, a closed-source platform. The link to the web page was also blocked from crawling by robots.txt, apparently serving no purpose for the Redditor’s client. The person asking the question was frustrated because the CMS didn’t allow editing to remove the link and was concerned about SEO issues caused by this rogue internal link.

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Meta still has widest social media reach | SocialMediaToday

Meta remains the leader of the pack in terms of total social platform reach and usage, while Threads continues to gain on X and Pinterest continues to rise. The information comes from the latest snapshot of overall users, as measured by app. This is worth noting not only in terms of reach, but also in regards to where attention is shifting. The data can help brands and marketers think about where to spend promotional budgets, and also serves as a measure of general online trends.

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What 15.7 million AI Mode citations reveal about getting quoted by Google | Search Engine Land

Click a citation inside Google’s AI Mode and watch what happens. You don’t land at the top of the page. The URL carries a text-fragment directive (it ends in #:~:text=…), and your browser scroll-jumps to a passage highlighted in purple. Google didn’t cite the page. It cited roughly 117 words of the page. I first noticed those purple highlights piling up in my clients’ citation data about a year ago. Once I started counting, I couldn’t stop. The final tally: 15,699,298 AI Mode citations across 148 industries.

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