White papers by Fast Growth Advisors, freely available
Fast Growth Advisors published three white papers in July and August 2026, freely available with no form to fill in: on the LinkedIn algorithm, on the recoverable message and on verified AI traffic. Each one backs its claims with a verifiable public source, or flags that none could be found. All exist in English and in French, online and as PDFs.
Why does Fast Growth Advisors publish white papers?
For a simple reason: on most technical subjects that touch B2B marketing, the verifiable and the invented read exactly alike.
Telling them apart means tracing every claim back to its primary source, or establishing that it has none. That takes time, and it is rarely done: almost none of the articles explaining LinkedIn's "new algorithm" cite the research paper that twenty-four of its engineers published on 12 February 2026. The same goes for the error rates attributed to AI crawlers, computed on what bots declare rather than on what they are.
These documents do that work in public.
At Fast Growth Advisors, this work extends our trade: we audit the clarity of B2B companies' messaging and measure how legible it is to buyers, to analysts and to answer engines. The white papers publish what those measurements and readings teach us, with their sources.
Three came out in 2026, freely available, and they may be cited with attribution.
Which white papers has Fast Growth Advisors published?
Three, most recent first.
August 2026
Counting bots, not lies: measuring AI traffic with verified identity
A bot is whatever it declares itself to be, and almost every published figure on AI crawling sums those declarations. Fast Growth Advisors applied to thirteen days of server logs, on two sites, the identity verification that publishers make possible: of 1,129 requests presenting themselves as an AI assistant, 876 were proven impersonations. They are what manufactures the error rate blamed on engines. Authenticated requests, by contrast, almost never knock on the wrong door: zero missing pages across 154 requests.
At the end of the document comes the one question to ask your measurement tool, and a summary covers the essentials.
Read “Counting bots”August 2026
The recoverable message: what answer engine crawlers read of your site
An answer engine can say what your company does, not what sets it apart.
Twenty edge cases submitted to three extractors, trafilatura, jusText and Resiliparse, run on the served bytes of a demonstration page, show why: the narrative survives, the proof dies. Client logos, figures, testimonials and prices injected by JavaScript disappear at extraction, and the engine cites as the category reference the competitor who put its proof in HTML. This document measures the share of positioning that survives a read without JavaScript, and what French tech loses there, across 94 complete 15-criteria audits: readability by AI engines is the weakest of the fifteen sub-criteria measured, at 0.91 out of 5.
Read “The recoverable message”July 2026
LinkedIn Algorithm 2026: what is proven, what is measured, what is invented
On 12 February 2026, twenty-four LinkedIn engineers published the model that ranks the feed for 1.2 billion members. Almost none of the articles explaining "the new algorithm" cite it. This document compares what LinkedIn publishes, what independent studies measure, and what everyone repeats.
First finding: in the documents from the first quarter of 2026, the feed is not ranked by a generative AI. LinkedIn did build a language-model ranker and tested it on live traffic, but it did not beat the model already in service. The system predicts six named actions, and on external links three serious studies reach incompatible conclusions.
Read “LinkedIn Algorithm 2026”How are these white papers built?
On a shared methodology: every claim goes back to its primary source, or it is flagged as untraceable.
Sources are named, dated and linked where the claim is made. Our LinkedIn white paper sorts every claim into four levels, proven, measured, disputed or invented; the recoverable message one separates the measured, the deduced and the not verified.
When Fast Growth Advisors measures something itself, the measurement is described so that it can be replayed: a window from 11 to 23 August 2026 for the server logs, a test page of twenty markers submitted on 29 July 2026 to the three extractors. Our measurements carry their base and their date.
Limits are stated, not hidden.
Each document ends with its own: a 13-day window for the bots, modest volumes, sources that will age. It also closes with a glossary of the terms it uses and a list of its sources, all linked.
Frequently asked questions
Are the Fast Growth Advisors white papers free?
Yes: they are freely available, with no form to fill in.
Each white paper's full text is published online, and a PDF version can be downloaded from its page. No email address is requested, either to read the document or to download the PDF.
Can these documents be cited?
Yes, with attribution.
Fast Growth Advisors publishes these white papers so that they get picked up. Every figure in them carries its base and its date, and every external source is linked where it is cited, which lets anyone check a claim before repeating it.
Is there a French version?
Yes, each of the 3 white papers exists in French.
French versions are gathered in the Livres blancs section, with the same content, the same figures and the same sources as the English versions. Each one also has its own PDF version.
Do these white papers give recommendations?
Not the LinkedIn one, and that is deliberate.
The white paper on the LinkedIn algorithm sets out what is known and how it can be known, with no advice and no tactics. The one on the recoverable message ends with a targeted action plan, and the one on verified AI traffic with the single question to ask your measurement tool.