Josh Rosenberg creates business-focused content about using AI in marketing and operations, including practical applications and risks. They also host the Smart AI Podcast, where they discuss how businesses can apply AI.
They are most useful for practical AI commentary on business operations, marketing, and security risks.
Best for
B2B AI consultancy webinar or co-marketing for agency owners, marketers, and business owners.
Not for
Enterprise cybersecurity vendor seeking technical keynote, guaranteed registrations, or published sponsorship package.
Key takeaways
The AI risk most companies miss is already inside the building
An automation agent is only as safe as the permissions you give it
The payoff from AI is a smaller business, not a faster one
Check first: Confirm sponsorship terms, audience details, and results from comparable work.
Main ideas
The AI risk most companies miss is already inside the building · An automation agent is only as safe as the permissions you give it · The payoff from AI is a smaller business, not a faster one
Josh Rosenberg creates business-focused content about AI, marketing, and operations, and hosts the Smart AI Podcast.
Their posts present AI as both a way to redesign business workflows and a source of operational and security risks.
One podcast post describes a conversation with email marketer Dave Miz about rebuilding an agency that once required 63 employees into a business run almost entirely by its owner with AI.
Rosenberg connects AI claims to recognizable business consequences.
In the Dave Miz podcast announcement, they focus on workflow replacement, operating systems, overhead, and adoption mistakes.
Their security posts examine what employees may expose through AI tools and what desktop agents may be able to access.
Other posts apply the same practical lens to client acquisition, advertising, and brand storytelling.
The range makes them a useful follow for business-oriented AI commentary.
Josh Rosenberg's posts return to a small set of arguments.
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AI's real risk, they say, is ordinary and internal: staff pasting work into tools and fast AI-assisted coding publishing private data.
The answer is architecture, not avoidance.
A second argument treats agents as a change in kind: once software can reach logins, send mail as you, and move money, the question becomes permissions.
On the value side, Rosenberg argues that AI pays off through a smaller business rather than a faster one, using operator interviews to show how, and that creators should prove capability with their own time and money before selling it.
The posts also show two priorities in tension: synthetic advertising nobody can detect and customers who buy from brands whose people they trust.
The AI risk most companies miss is already inside the building
Rosenberg's security writing focuses on ordinary risks, not exotic attacks.
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Employees can leak sensitive company data through AI tools without realising it, while most executives remain unaware of the exposure.
The named mechanism is "vibe coding": building software quickly with AI assistance and accidentally publishing private information.
The proposed response is secure AI architecture—deciding where data is allowed to go—rather than banning tools.
The framing targets business decision-makers, describing the risk in business terms rather than security jargon.
The data-leak post traces a chain: employees use AI tools, sensitive information leaves the company, the business is exposed, and secure architecture is the fix. It names the practice and the response without identifying a specific company.
An automation agent is only as safe as the permissions you give it
Rosenberg treats the shift from AI that writes to AI that acts as a change in kind.
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When an agent can reach saved logins, send email as the account owner, move money, and monitor the system, the question is no longer what the model says but what it is allowed to do.
The post urges readers to understand that permission surface before installing anything.
A second post makes the same point through a live incident, arguing that agents are becoming actors rather than tools.
The ClawdBot post lists concrete capabilities—saved logins, sending email as the user, moving money, and monitoring the system—and urges readers to read before installing. A separate post uses a rushed rebrand that produced scams and financial losses within hours as a news-style example; the reported details are in a linked article.
The payoff from AI is a smaller business, not a faster one
The operator interviews frame AI's value as structural: fewer people, different economics, and whole workflows replaced without losing quality.
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On the Smart AI Podcast, Rosenberg talks with Dave Miz, described as a long-time direct-response email marketer, about an agency that once needed 63 employees and is now run almost entirely by its owner using AI.
The episode covers replacing workflows, systems built to automate operations, how lower overhead changed the business economics, mistakes companies make when adopting AI, and where marketing and agency services go next.
The 63-to-1 claim comes from the guest and is presented in Rosenberg's episode description.
An agency that once required 63 employees is now run almost entirely by one person, with the episode broken into specific takeaways rather than general commentary on AI.
Prove an AI method with your own time and money before you sell it
One post makes a first-person claim: two weeks and hundreds of dollars spent learning to produce AI ads viewers cannot tell are fake, with the method promised in the linked article.
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It names a spend and a time cost rather than a tool, and ties the work to direct-response and ecommerce performance marketing rather than brand awareness. The post is a teaser; the process is in the linked article.
"I spent two weeks and hundreds of dollars mastering AI ads that nobody can tell are fake," followed by a promise to show exactly how it was done.
AI-made creative and customer trust pull in opposite directions
Across the posts, Rosenberg argues that AI can produce advertising indistinguishable from real footage and that customers increasingly buy from brands whose people they know, like, and trust—a shift they expect to matter into 2027.
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Those positions are in tension: the more convincing the synthetic creative, the more the trust argument has to carry on its own. Ask Rosenberg how they reconcile the two.
The AI-ads post promises advertising "nobody can tell are fake"; the brand-story post argues that a great product is no longer enough because customers want to know the people behind the brand. Neither post connects the two.
Rosenberg calls "GTM engineering" a quiet shift in how businesses find clients, claiming that companies using it close deals without a single cold call and that understanding it changes how people think about client acquisition.
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The post introduces the concept but leaves the mechanics to a linked article. It is the clearest sign that go-to-market operations, not just content production or risk, are among Rosenberg's AI application areas.
A short post introduces GTM engineering as a named practice and claims that businesses already using it close deals without cold calls.
Shows what Rosenberg produces, not just what they post about: an episode of their own podcast, with a named guest and a stated business outcome readers can debate.
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Rosenberg frames the episode around a concrete before-and-after: an agency that once needed 63 employees, now run almost entirely by its owner using AI.
The post sets out five threads: replacing whole workflows without losing quality, the systems built to automate operations, how lower overhead changed the economics of the business, mistakes companies make when adopting AI, and where marketing and agency services go next.
The guest, Dave Miz, is introduced as a long-time direct-response email marketer.
Ask for the episode audio to assess the conversation itself.
For brands planning a campaign
A clear hook for an agency-owner audience: a webinar or co-produced episode on what breaks when you automate a service business would extend a conversation Rosenberg has already published and build on an existing guest relationship.
Shows the hands-on production side of the AI work and how Rosenberg frames cost and effort when pitching a method.
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The claim is compressed into one line: two weeks and hundreds of dollars spent learning to make AI ads that viewers cannot tell are fake, with the method promised in the linked article. The hook stands out for naming a spend figure rather than a tool.
For brands planning a campaign
Useful for a collaboration with brands weighing AI-produced creative: the cost-and-time framing is likely to interest a budget holder. Check the underlying method before repeating it as advice.
Explains the risk framing that runs through much of the material and why Rosenberg's AI-adoption pitch comes with a security argument.
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The argument is that employees leak sensitive company data through AI tools without realising it, while most executives are unaware of the exposure.
The named mechanism is 'vibe coding' — building software quickly with AI assistance and accidentally publishing private information.
The promised payoff is a set of secure AI architecture practices for companies trying to avoid that outcome.
For brands planning a campaign
Adds exposure to an adoption-focused pitch. The two-part structure supports a webinar with a security segment alongside the transformation segment, rather than a single-topic session.
The most specific risk example in Rosenberg's posts: a named tool and a list of concrete capabilities rather than a general warning.
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Rosenberg asks what a desktop automation agent costs the user, then lists what such an agent can reach: saved logins, sending email as the account owner, moving money, and monitoring the system.
The post's instruction is to read before installing.
Its point is the permission surface of agent tools, aimed at people who would install one to automate business tasks.
For brands planning a campaign
A concrete, checkable example to anchor a security-focused session, specific enough to demonstrate rather than assert. Verify the tool's actual behaviour before repeating the claim publicly.
Shows how Rosenberg turns a fast-moving news event into a business-risk argument, a move that recurs across the posts.
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The post reports that a rushed rebrand produced scams, crypto chaos, and real financial losses within hours. It uses the incident to argue that AI agents are moving from tools to actors that take their own actions, and promises an account of what happened, why it matters, and what it means for AI and business.
For brands planning a campaign
Rapid-response content is hard to plan around, but the format offers a lesson: Rosenberg takes a live incident and draws an operational implication for businesses. That suits a reactive collaboration better than a fixed calendar slot.
The one post here aimed at the sales side of a B2B business rather than at tooling or risk.
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Rosenberg describes a 'quiet revolution' in client acquisition in which businesses close deals without making a single cold call, and gives the practice a name — GTM engineering. The post asserts that once readers understand what it does, they will not think about finding clients the same way again.
For brands planning a campaign
Opens a second audience for the same body of work: revenue and growth teams rather than agency owners. A session on how AI changes pipeline generation fits this post better than the security ones.
Shows the direct-response and ecommerce side of Rosenberg’s interests, and a claim about what customers are buying alongside the product.
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The post argues that a great product is no longer enough: customers want to know, like, and trust the people behind the brands they buy from.
It predicts brand story will be a competitive advantage heading into 2027.
The audience is DTC and ecommerce marketers working in direct response.
Ask for the linked article for the supporting reasoning.
For brands planning a campaign
Best suited to a DTC or ecommerce brand rather than a B2B software audience. Paired with the AI-ads post, it supports a creative-and-storytelling collaboration rather than an infrastructure one.
AI security and data exposure inside ordinary businesses
Several posts focus on how everyday use of AI tools creates security exposure that executives underestimate.
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One describes employees leaking sensitive company data through AI tools, ties that to "vibe coding" and accidental public exposure, and points to secure AI architecture as the response.
Another warns that AI desktop agents can reach saved logins, send email as the user, move money, and monitor a system, urging readers to understand the risks before installing one.
The treatment is plain-spoken and aimed at business decision-makers rather than security specialists.
Takes a widely used tool or practice, lists specific exposures in plain language, then names a mitigation strategy.
For brands planning a campaign
A security or enterprise-AI vendor can use this as a bridge between AI adoption enthusiasm and governance. A webinar or article could pair the adoption case with concrete guardrails, since Josh Rosenberg frames the risk in business terms rather than technical jargon.
Autonomous AI agents and the fallout when systems act alone
One post argues that AI agents are moving from tools to actors that make decisions on their own, using an example where a rushed rebrand triggered scams, crypto chaos, and financial losses within hours.
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It explains what happened, why it matters, and what it means for the future of AI and business. Together with the desktop-agent warning, it shows a consistent interest in the consequences of giving AI systems the ability to act rather than just generate.
The rebrand incident is referenced as a news-style example, not a documented case study. Before using it as evidence in a campaign, ask for a source or documentation.
For brands planning a campaign
A good entry point for a campaign about agent oversight, rollout policy, or incident response. The example-driven framing suits a short video or newsletter collaboration aimed at owners deploying agents who have not considered what happens when an agent moves money or posts under a brand identity.
AI transformation told through operators who have done it
Josh Rosenberg hosts the Smart AI Podcast.
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One post promotes an episode with email marketer Dave Miz, who describes how an agency that once needed 63 employees became a business run almost entirely by one person using AI.
The episode covers replacing workflows without losing quality, automating operations, cutting overhead, common implementation mistakes, and where marketing and agency services are heading.
The post names business owners, agency owners, marketers, consultants, and executives working out where AI fits as the audience.
An interview built around a named practitioner, with specific takeaways rather than general commentary.
For brands planning a campaign
A strong fit for a speaker or webinar collaboration on practical AI adoption. The operator-interview format gives a sponsor a credible way to reach agency and business owners without a product pitch, while the before-and-after numbers provide a concrete hook.
AI-generated video advertising that is hard to detect
One post describes spending two weeks and hundreds of dollars learning to produce AI ads that viewers cannot tell are fake, and offers to show exactly how.
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It sits alongside direct-response and ecommerce hashtags, connecting AI production capability to performance marketing rather than brand awareness.
A first-person testing narrative that states the time and money spent, then promises the exact process.
For brands planning a campaign
A tooling, creative, or ad-tech brand can build a demo-led campaign around a walkthrough of the production process with real spend and real output. The theme also supports a session on how AI video changes creative testing economics for DTC and direct-response teams.
GTM Engineering as a replacement for cold outreach
A post introduces "GTM Engineering" as a quiet shift in client acquisition, claiming that businesses using it close deals without making a single cold call and that understanding it changes how people think about finding clients.
Read more
The post sets up a concept rather than detailing the mechanics. It is the clearest example of Josh Rosenberg treating go-to-market operations, not just content production, as an AI application area.
For brands planning a campaign
Useful for a campaign targeting sales and revenue leaders rather than marketers. A collaboration could have Josh Rosenberg unpack the GTM Engineering concept, since the post names the idea but leaves the implementation open.
Brand storytelling as a competitive advantage in DTC and ecommerce
One post argues that a good product is no longer enough: customers want to know, like, and trust the people behind the brands they buy from.
Read more
It positions brand story as a major competitive advantage heading into 2027. This is the least AI-focused item in the reviewed set and shows the account also covers brand strategy and direct-response marketing for consumer brands.
This theme appears in one post, making it a secondary topic rather than a defining one.
For brands planning a campaign
This broadens the fit beyond AI vendors. A DTC or ecommerce brand can use the theme for a content collaboration on founder-led storytelling, giving a sponsor an angle that does not depend on AI news.
Described as a friend and “one of the best email marketers in the world” who cut an agency from 63 employees to a business run almost single-handedly with AI.
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Appears as the guest in a Smart AI Podcast episode.
Josh Rosenberg's posts are short and cluster around a few arguments, so read them by theme rather than date.
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Start with the security writing: the data-leak post and the ClawdBot warning explain how ordinary AI use exposes company data and why an acting agent's permissions matter more than what it says.
The AI adoption material makes the value case: the Smart AI Podcast episode with email marketer Dave Miz describes an agency that went from 63 employees to almost solo operation, while the GTM Engineering post points to client acquisition as the next system to rebuild.
For a different angle, one post describes two weeks and hundreds of dollars spent learning to make video advertising viewers cannot detect; another argues customers buy from brands whose people they trust.
Read those two together: they pull in opposite directions.
The tags below group posts by subject, specialty, format, and audience, so readers can go straight to the thread that matches their interests.
Westra uses a podcast to explore how operators build and scale businesses.
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Episode posts cover a founder's move from franchisee to franchisor, exit planning from day one, and the balance of intuition and systems in leadership—the same interview-led format Josh Rosenberg uses to show what AI changes inside a real company.
Westra focuses on general business growth, franchising, and exit strategy. AI is not the organising theme; the episodes explore leadership and scaling decisions rather than technology adoption or risk.
Grace Eva writes about producing UGC-style ad creative with AI avatars, voiceovers, automated scripts, and product visuals—the same territory as Josh Rosenberg's post on AI ads viewers cannot detect.
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Both treat faster creative testing as the reason to use AI and lay out the work as a sequence from idea to script to video to testing.
Grace Eva's posts are practical tooling guides: which AI UGC tools are worth using, what to look for, and how to build a production workflow. Josh Rosenberg frames AI ads as a direct-response capability and pairs that topic with writing on security and adoption rather than tool selection.
Tahir iqbal makes the same chatbot-versus-agent distinction as Josh Rosenberg: an agent scores a lead, drafts the follow-up in brand voice, and flags it for approval rather than simply answering a prompt.
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Both write for agency and business owners about putting AI to work in operations, grounding their arguments in first-person accounts of building an agency.
Tahir iqbal's writing on agents centres on marketing and CRM workflows—lead scoring, reporting, and content planning—within an agency offer. Josh Rosenberg's writing centres on what an agent is permitted to reach, send, or move, making it a security argument rather than a workflow one.
A buyer planning a webinar or co-marketing push with Josh Rosenberg should treat this as a small, targeted audience rather than a reach buy.
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The only measured audience figure available is their LinkedIn following: 3,286, captured 9 October 2026.
That is a modest base for a B2B AI webinar, so a collaboration is more likely to work when the partner brings the distribution and Rosenberg brings topic credibility and a practitioner-level discussion—the format their posts already support.
Across seven posts dated 3 February to 16 September 2026, the subjects cluster around AI in marketing and business operations, AI security and data-leak risk, AI agents, direct-response advertising, brand storytelling for DTC and ecommerce, and client acquisition under the label "GTM Engineering."
Several posts address business owners, agency owners, marketers, consultants, and executives directly, and one promotes a Smart AI Podcast episode with email marketer Dave Miz about running an agency almost entirely on AI.
That framing and hashtag set point to an intended audience of operators, founders, and marketing leads, but don’t establish the job titles, company sizes, or seniority of actual followers and readers.
No platform demographic breakdown is published for any channel.
LinkedIn does not publish age, gender, or geographic breakdowns the way a YouTube audience report does, and their posts do not cover YouTube or another channel, so those attributes are unknown.
If your decision depends on who is in the room—job function, seniority, company size, or region—ask whether they have audience survey results or past webinar registration data with those fields.
A registration list, signup form fields, or a post-event survey from a comparable AI webinar would answer the question directly; follower count alone cannot.
Request results from a recent comparable piece of work—a webinar, sponsored post, or podcast episode promotion—including impressions, click-through or registration rate, and audience details captured at signup.
Ask specifically for a session aimed at agency and business owners so the numbers match your campaign.
LinkedIn followers
3,286
Follower count on Josh Rosenberg's LinkedIn profile, from the profile snapshot · 2026-10-09
Josh Rosenberg's public posts do not list a sponsorship offer, rate card, media kit, partnership terms, or official inquiry route for commercial collaborations.
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The one partnership-adjacent asset is the Smart AI Podcast, which they host and use to interview operators and marketers about applied AI—for example, email marketer Dave Miz on rebuilding an agency around AI.
A brand seeking a podcast appearance or joint content piece could approach them about a format they produce.
Ask for the guest policy, sponsorship specifications, and booking contact; none are published.
Need: a B2B AI consultancy wants a 45-minute webinar plus a follow-up article for agency owners, marketers, and business owners on putting AI into day-to-day operations while managing the security exposure that comes with it.
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Rosenberg is a strong fit for this campaign.
The reason is that the format already exists rather than needing to be invented.
Rosenberg hosts the Smart AI Podcast and has published an episode built around a named operator, email marketer Dave Miz, whose agency went from 63 employees to something one person runs almost entirely with AI.
The episode is framed around five threads a sponsor can borrow directly: replacing whole workflows without losing quality, the systems used to automate operations, how lower overhead changed the economics, the mistakes companies make when adopting AI, and where agency services go next.
That is a ready-made webinar spine for an audience of owners.
The security half of the campaign is also already part of Rosenberg's public argument, which is unusual and useful.
Three posts deal with it in business terms: employees leaking sensitive company data through AI tools and the practice of "vibe coding" accidentally publishing private information; a desktop automation agent that can reach saved logins, send email as the account owner, move money, and monitor a system; and a rushed rebrand that produced scams, crypto chaos, and real financial losses within hours because agents acted on their own.
Rosenberg frames these for executives rather than security specialists, so a session pairing adoption with guardrails matches how they already talk, and it gives the consultancy a sharper angle than another generic AI webinar.
Two other posts support smaller beats.
One names "GTM engineering" as a replacement for cold-call client acquisition, which opens the door to a revenue-team audience.
Another describes two weeks and hundreds of dollars spent producing AI video ads viewers cannot tell are fake, which is the most specific cost-and-effort claim in the posts here and works as a concrete example inside a session.
One constraint should shape the arrangement.
The only measured audience figure is 3,286 LinkedIn followers, captured 9 October 2026, with no engagement, impressions, or demographic data published in the posts here.
Treat this as a credibility hire, not a reach buy: the consultancy should carry promotion and ask Rosenberg for registration or attendance results from a comparable session before committing to a target.
When to choose a specialist
Need: an enterprise cybersecurity vendor wants a technically credentialed keynote, a sponsored awareness campaign with a guaranteed registration number in the agreement, and a published sponsorship package.
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Rosenberg is a poor fit as it stands.
The posts are short promotional and commentary pieces: one podcast announcement, three that lead into linked articles, and three argument-led posts.
The technical substance sits in the linked articles and episode audio, which the posts do not discuss.
They do not establish the depth in threat research, incident forensics, or technical credentials a security vendor’s keynote audience would expect.
Verification is another issue.
The ClawdBot post lists what a desktop agent can reach, but that is Rosenberg’s characterisation of the tool, not an independently verified account.
It could start a conversation with business owners deciding whether to install an agent, but a security vendor should check the tool’s behaviour before using it as a factual claim in collateral.
The rebrand incident is presented as a news-style example, not a documented case study, so it needs the same scrutiny.
The posts contain no sponsorship offer, rate card, media kit, guest policy, or inquiry route for collaborations.
The Smart AI Podcast is the only partnership-adjacent asset, so a vendor would have to open discussions through Rosenberg’s public presence.
A vendor that needs a priced, scoped package cannot buy one from the posts.
The guarantee is also unsupported.
The only audience figure is 3,286 LinkedIn followers as of 9 October 2026; there are no engagement figures, impressions, or demographics, and no data from other channels.
A registration number cannot be responsibly promised.
Ask for comparable campaign results before setting a target.
There is an audience mismatch, too.
The security content is written in plain language for executives and owners, not security practitioners.
A vendor selling to security teams would be buying a speaker whose framing is aimed at the buyer’s internal stakeholders.
The picture changes if the session targets owners rather than practitioners, drops a contractual reach guarantee, and treats a guest appearance as one part of a broader campaign.
Otherwise, another creator is the better choice.
A worked campaign example
A worked angle for the good-fit brief.
Working title: “From 63 to 1: what actually breaks when an agency automates.”
Positioning.
A joint session between Rosenberg and a practitioner from the consultancy, aimed at agency and business owners who have run an AI pilot and are deciding whether to scale it.
The promise is not AI enthusiasm; it is what breaks, what it costs, and what to lock down first.
Run of show.
Open with the before-and-after: an agency that once needed 63 people, now run almost entirely by one person with AI.
Spend the first half on adoption, covering the five threads Rosenberg published for the episode: which workflows were replaced and how quality was held, the operating systems built around the automation, what lower overhead did to the business’s economics, the mistakes most companies make during implementation, and where agency services are heading.
Spend the second half on exposure, using three examples the audience can picture: employees leaking company data through everyday AI tools, a desktop agent with access to saved logins, email, and money movement, and an agent incident that turned into scams and financial losses within hours.
Close with operating rules the audience can apply the following week.
Why it holds together.
Each segment maps to something Rosenberg has already published, extending an existing conversation rather than bolting a topic onto their name.
The 63-to-1 number gives a budget holder a reason to click, and the security half differentiates the session from a generic AI webinar.
The consultancy’s practitioner carries the vendor’s point of view, keeping the session from reading as a pitch.
Assets.
One article covering both halves, structured as adoption then guardrails.
One short checklist of agent permissions to review before installing a desktop automation tool, drawn from the capabilities Rosenberg lists.
One episode announcement in Rosenberg’s existing style, a format already used on their account.
The ask.
Invite Rosenberg to co-host rather than keynote, have the consultancy carry promotion and registration, and request registration and attendance data from a comparable episode or session to set the target from evidence.
Confirm the guest arrangement directly with Rosenberg; no booking contact or guest policy is published.
Before production, review the episode audio or ask Rosenberg for details: the linked articles and audio were not reviewed, so the specific methods behind the 63-to-1 result cannot be scripted in advance.
Treat the rebrand incident as a conversation starter, not verified evidence.
Re-check the desktop agent’s capabilities before stating them as fact on air.
Choose Todd if the webinar should focus on scaling a business through people, processes, systems, and exit planning.
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In a podcast post about home-care franchising, Todd highlights those themes and the move from franchisee to franchisor, making them a closer fit for an owner-focused discussion of business growth than a session centered on AI adoption and security.
Todd’s posts do not show experience with AI workflows or security risks. Because the episode focuses on business scaling, the consultancy would need to supply that expertise rather than build on topics in Todd’s posts.
Choose Tahir if the session’s main goal is to show small businesses how AI agents could support marketing operations.
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One post gives a concrete chatbot-versus-agent example covering lead scoring, follow-up drafts, reporting, and content planning; another uses an agency’s early client story to discuss earning trust. Tahir is a better match for a practical marketing-automation session.
The reviewed work focuses on marketing workflows and agency development, not security exposure or the broader operational transformation in Josh Rosenberg’s 63-to-1 agency example. Ask for evidence of experience with AI security before choosing them for a session that covers security risks.
Choose Grace if the webinar should demonstrate AI-assisted ad production, especially producing UGC-style videos and variations for paid-social testing.
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The posts describe a workflow spanning avatars, scripts, voiceovers, product visuals, and creative testing, offering a focused, concrete demonstration for ecommerce marketers.
That narrower creative-production focus does not address the consultancy’s central combination of company-wide AI adoption and security risks. Ask for evidence of experience with operational automation and security practices.
This profile covers seven LinkedIn posts by Josh Rosenberg, including a Smart AI Podcast episode announcement and posts about AI security, agents, advertising, client acquisition and brand storytelling.
The posts here do not describe the underlying methods and evidence in the linked articles and podcast audio.
A LinkedIn profile snapshot dated 9 October 2026 showed 3,286 followers.
Josh Rosenberg's posts do not say whether books, businesses or earlier work should also be included.
