David Solomon is an AI systems architect who builds voice agents and business automation systems, and advises businesses on AI strategy and implementation.
They are most useful for practical AI voice agent and automation content, plus governance and workforce effects.
Best for
Fits AI voice receptionist or lead-response campaigns aimed at mortgage, home services or professional services buyers.
Not for
Does not fit paid sponsorships seeking independently tested coverage of their own AI product.
Key takeaways
Most small businesses answer fewer than four in ten calls.
85% of callers who reach voicemail never call back.
Workforce post suggests customer success, sales coaching and conflict resolution roles.
Check first: Confirm sponsor policy, rates, audience breakdown and comparable post results first.
Main ideas
AI competition is about infrastructure, regulation and rights, not the best model · AI agents are moving from assistant to operator, so governance comes before tooling
David Solomon is an AI systems architect who builds voice agents and business automation systems and advises on AI strategy.
Their LinkedIn posts cover practical applications—such as answering calls, qualifying leads and automating follow-up—as well as how businesses govern AI as it begins taking action.
The posts also connect automation to its effects on workers, including possible next roles in customer success and sales for representatives whose phone work is automated.
Solomon links implementation detail with questions of oversight and consequence.
One post lays out specific voice-agent and workflow products; another gives executives three questions about human judgment, liability and auditing when AI acts autonomously.
A broader strategy post shifts attention from which model performs best to control of infrastructure, regulation and content rights.
The workforce post proposes customer success, sales coaching and conflict resolution as possible paths for displaced phone representatives.
Together, these examples show Solomon’s work across building AI systems, deciding how to govern them and considering whom automation affects.
Across the four posts, David Solomon returns to a handful of arguments: businesses lose measurable money when calls go unanswered, so that is what AI voice agents should be judged on; AI agents are crossing from suggesting to acting, putting decision rights, liability and auditing ahead of tool choice; the real competition in AI is in supply chains, regulation and copyright, not model quality; and automating phone work creates a responsibility to move displaced staff into adjacent roles.
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Underneath all four is a preference for production systems with verification and clear boundaries over demos and enthusiasm. One tension runs through the posts: the same author sells voice agents that replace call work and asks publicly what happens to the people whose calls they take.
AI competition is about infrastructure, regulation and rights, not the best model
David Solomon argues that the AI race has moved past whose model is smartest.
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The advantages that matter are control of the supply chain (memory and hardware), the regulatory framework and the intellectual property pipeline.
Memory is the fuel line, regulation the guardrail, copyright the battlefield.
The practical consequence: anyone still choosing AI vendors by which chatbot writes the best email is evaluating the wrong thing.
Companies positioned to win are those that locked down infrastructure, compliance and content rights before competitors noticed those were the moats.
The post stacks four events from a single day: an India-led Global South AI summit with homegrown models ready for release; memory manufacturers acknowledging HBM shortages that are raising device prices, described as an “AI tax”; UK regulators treating chatbots as platforms, with fines tied to global revenue; and a dispute over AI-generated likenesses of Tom Cruise and Brad Pitt produced without permission.
AI agents are moving from assistant to operator, so governance comes before tooling
The argument is that AI agents are crossing from drafting suggestions to taking action, outpacing the compliance policies meant to govern them.
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The questions Solomon puts to executives concern decision rights and control, not capability: which decisions need human judgment rather than human approval, where liability sits when AI acts on its own, and how decisions made at machine speed get audited. Their conclusion: companies handling this well are not moving faster but defining boundaries more clearly; those that get it wrong become cautionary case studies.
A dated progression drawn from boardroom conversations: in early 2025, executives asked whether AI could draft an email; by late 2025, whether it could send routine mail; by early 2026, they were describing AI handling scheduling, procurement under $5,000 and first-round vendor negotiations.
This is the clearest account of what David Solomon builds and sells.
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Start here for the offer, client segments, build process and founder background in one place.
This post introduces Superstar AI and details its commercial offer.
It names six products: an AI voice receptionist that answers calls, qualifies leads, books appointments and transfers prospects; an outbound nurture engine using voice, SMS and email; a lead qualification hotline that scores and routes inbound leads with CRM integration; database reactivation for dormant leads; custom n8n workflow automation; and AI security and compliance with multi-model verification and self-hosted infrastructure.
It states that most small businesses answer fewer than 4 in 10 calls, 85% of missed callers never try again, and the average SMB loses $126,000 a year in missed revenue.
The post says systems are built in 4-week production sprints, run Claude, GPT, Gemini and Grok through systematic verification for sub-1% error rates, and are self-hosted so client data stays with the client.
Named client segments include mortgage lenders and brokers, home services companies, professional services firms, and SMBs or founders scaling faster than their team.
In a founder section, Solomon says they have 24 years of entrepreneurship, started a first company at 17, attended Stanford GSB, worked at Google as a People Development Consultant, are a 2x TEDx speaker, and scaled a previous company from $50K to $1.3M.
For brands planning a campaign
For a technology investment firm assessing consulting fit, this post offers a concise view of commercial positioning and delivery model. The revenue figures, error-rate target and credentials are claims made in the post and have not been independently verified.
This explains how Solomon frames AI governance for executives.
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It is a strong starting point for the advisory angle, showing the diagnostic questions used in boardroom conversations.
The post argues that 2026 is the year AI agents shift from drafting suggestions to taking actions, and says this is already happening in closed enterprise pilots.
It gives a timeline: in Q1 2025 executives asked whether AI could draft an email; by Q3 2025 they asked whether it could send routine emails; by Q1 2026 they were describing AI handling scheduling, procurement under $5K and first-round vendor negotiations.
Solomon says the shift from assistant to operator is outpacing compliance policy.
They put three questions to executives: which decisions require human judgment versus human approval; where the liability line sits when AI acts autonomously; and how decisions made at machine speed get audited.
The conclusion: companies getting this right are not moving faster but defining boundaries more clearly.
For brands planning a campaign
Useful for assessing whether Solomon can lead an executive-level consulting engagement. The post offers a governance-first framing a buyer could apply immediately, rather than a tools review.
This shows the macro market-structure lens Solomon applies to AI strategy, beyond product-level tool advice.
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Read it to assess whether the advisory work pairs a broad market view with implementation detail.
This post connects four developments reported in a 24-hour window: India launching the first Global South AI summit with homegrown models ready for release; memory manufacturers admitting HBM shortages are creating an AI tax that raises device prices; the UK declaring chatbots are platforms with fines tied to global revenue; and Hollywood clashing with ByteDance after Seedance 2.0 generated AI versions of Tom Cruise and Brad Pitt without permission.
The thesis: the AI competition is no longer about the best model but about control of the supply chain, the regulatory framework and the IP pipeline underneath.
Solomon sums up memory as the fuel line, regulation as the guardrail and copyright as the battlefield, and argues that companies evaluating AI by which chatbot writes the best email are watching the wrong game.
For brands planning a campaign
Relevant for an investment firm assessing pattern recognition and senior-level synthesis. It offers a concrete discussion frame for consulting conversations about infrastructure, compliance and content rights as competitive moats.
This shows how Solomon addresses the workforce side of automation.
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Read it to understand the responsible-AI posture and interest in retraining alongside the product business.
This post asks how customer success and sales development representatives could best use their time and skills once voice AI takes over most of their phone work.
Solomon raises responsibility for technological unemployment as the company grows and suggests possibly developing retraining that builds on existing careers rather than starting from zero.
The post names three transition roles: Customer Success Manager, Sales Coach and Conflict Resolution Specialist.
It argues these build on the interpersonal skills, empathy and problem-solving successful phone representatives already have, require minimal retraining, and offer more responsibility and growth than repetitive calls.
For brands planning a campaign
For a consulting partnership assessment, this shows a concern for human impact that could shape how a firm frames AI adoption for clients. The retraining tool is a possibility, not a launched offer.
David Solomon's launch post for Superstar AI frames the core problem in numbers a small business can check against its own phone logs: most SMBs answer fewer than four of every ten calls, 85% of callers who reach voicemail never call back, and the lost business is put at roughly $126,000 a year.
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The products described include an AI voice receptionist that answers around the clock, qualifies and books; an outbound nurture engine across voice, SMS and email; a lead-scoring hotline wired into a CRM; and reactivation campaigns for old leads sitting unused. Named target buyers include mortgage lenders and brokers, home services, professional services firms, and founders scaling faster than their team.
A sales argument built on a stated annual cost and a named list of verticals, with six defined product offers.
For brands planning a campaign
Strong fit for a demand-generation or lead-response brief aimed at service businesses with high call volume.
The argument is quantifiable, so a campaign can lead with the cost of an unanswered call rather than a general AI pitch.
The named verticals give a media plan a ready-made audience.
A post on enterprise adoption describes a progression observed in boardroom conversations: asking AI to draft email in early 2025, sending routine mail later that year, and handling scheduling, procurement under $5,000 and first-round vendor negotiations by early 2026.
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The argument is that the shift from assistant to operator is outpacing compliance policy, and that three questions matter before mid-year: which decisions need human judgment rather than human approval, where the liability line sits when AI acts on its own, and how decisions made at machine speed get audited. The conclusion: companies handling this well are not moving faster but defining boundaries more clearly.
Uses a dated progression to make an abstract shift concrete, then closes with three questions for readers to answer before a deadline.
For brands planning a campaign
Useful for an executive-education or governance brief. The angle is decision rights and auditability, not tooling, making it suitable for audiences that need internal sign-off before buying automation.
Infrastructure, regulation and rights as the real competitive moats
A market-commentary post links four developments in a single day: an India-led Global South AI summit with homegrown models, memory manufacturers acknowledging HBM shortages that raise device prices, UK regulators treating chatbots as platforms with fines tied to global revenue, and a copyright dispute over AI-generated likenesses.
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The connecting argument is that competition has moved from who builds the best model to who controls supply chain, regulatory framework and intellectual property, described as the fuel line, guardrail and battlefield. The closing claim is that 2026 winners locked down infrastructure, compliance and content rights early.
This is broad macro commentary, not a reference to David Solomon's own services; it shows analytical range rather than a directly purchasable brand deliverable.
For brands planning a campaign
Positions David Solomon for strategy-level conversations that go beyond tool selection. A B2B audience weighing AI investment could use this as the framing piece for a webinar or explainer on supply and compliance risk.
One post asks what customer success or sales development staff could do best with their skills once replaced by voice AI.
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It raises concern about technological unemployment as the company grows and floats retraining displaced workers for adjacent roles.
Three paths are named: customer success manager, sales coach, and conflict resolution specialist, chosen because they build on interpersonal skills, empathy and problem-solving rather than requiring full retraining.
The post notes that current phone work is repetitive enough for AI to take over most of it.
The post poses an open question rather than claiming a completed program, and presents retraining as a possibility under consideration.
For brands planning a campaign
Relevant to employer-brand, workforce-transition or CSR-adjacent campaigns, and to buyers who will ask what happens to their call teams after adoption. It also softens the automation message for audiences wary of headcount cuts.
The launch post sets out a delivery approach: production-grade systems built in four-week sprints rather than demos or proofs of concept, running independently after handoff, with multi-model AI verification across Claude, GPT, Gemini and Grok and a claim of sub-1% error rates.
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Systems are described as self-hosted and hardened so client data stays with the client, and every build is said to ship with compliance protocols. Private consulting is offered separately for founders and operators seeking help with AI strategy, prompt engineering, multi-model orchestration, or building their own systems.
Multi-model verification and self-hosted deployment are presented as differentiators from agencies that skip compliance work.
For brands planning a campaign
Gives a brand concrete delivery expectations to test—sprint length, handoff, hosting model and verification method—and an alternative consulting engagement for audiences not ready to buy a full build.
David Solomon's posts document two concrete brand relationships: Superstar AI, the AI voice and automation company whose launch post names them as founder, and Google, listed in the same founder bio as a prior People Development Consultant role.
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The posts document no sponsorships, affiliate deals, named clients, investments, or disclosed commercial partnerships; ask for confirmation of any before treating these as creator-work credentials.
Superstar AI
Owned business
The launch post says "We just launched Superstar AI" and its "About the founder" section names David Solomon.
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The company builds AI voice agents, outbound nurture systems, lead qualification, database reactivation, workflow automation, and AI security and compliance systems.
The operational strand argues that businesses lose real money to unanswered calls and that an AI voice agent should be judged on whether it captures the lead, books the appointment and transfers the caller correctly—not on how human it sounds.
The Superstar AI launch post sets out that pitch and their product line in full.
The strategic strand argues that as agents move from drafting suggestions to taking actions, decision rights, liability and auditing take priority over tool choice; the wider AI contest is about supply chains, regulation and copyright, not which model is smartest.
A fourth post asks what customer success and sales development staff should do once voice AI takes over their calls.
Across the strands, they favor systems that run in production with verification layers over demos and enthusiasm.
Writes about the same voice AI problem Solomon sells against: ensuring business calls are handled reliably and produce an outcome.
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The measure is whether the agent completes the task, not how human it sounds. Both argue that reliability and completed actions matter more than an impressive demo.
Gonzalez approaches voice AI from the communications and telecom side, building for managed service providers and focusing on reliability and measuring what each call accomplishes. Solomon builds and sells SMB-focused voice receptionist and lead-response systems across several verticals, and ranges beyond Gonzalez's narrower, single-theme commentary into enterprise governance and macro AI strategy.
Works on the same lead-response problem: leads go cold when follow-up is slow, and AI voice agents that answer immediately offer one fix.
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Both argue that the bottleneck is the process after a lead arrives, not the number of leads bought.
Fatima writes specifically for real estate brokers and teams, emphasizing process discipline: speed-to-lead SLAs, after-hours rotation and CRM-tracked response time, with AI as one option among several. Solomon addresses a wider range of verticals and leads with the lost-revenue math behind their own product line, making the treatment broader and more product-led.
Writes about applying AI inside software through predictive features, personalization, conversational support and workflow automation, overlapping with Solomon's automation and AI-adoption themes.
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Mushtaq's posts offer product-development guidance for teams building AI features into their own software, rather than delivery of AI systems to business clients.
They include one AI-feature post and a hiring post.
The overlap with Solomon is thematic, not close in audience or approach.
The only measured audience figure is the LinkedIn following for the davidsolomonstrategy profile: 5,287 followers, observed 9 October 2026.
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Across four LinkedIn posts published between 2 October 2024 and 21 March 2026, David Solomon writes for two overlapping groups: owners and operators of small and mid-sized businesses weighing AI adoption, and executives and revenue leaders making governance decisions about AI agents.
The launch post for Superstar AI names mortgage lenders and brokers, home services companies, professional services firms, and founders as the businesses served, and cites the cost of missed inbound calls.
A post on where to draw the liability line and what authority to delegate addresses executives and revenue leaders, as does a post on supply-chain, regulatory, and copyright control as the real competitive moats.
A fourth post speaks to customer success and sales development staff about roles they could move into as voice AI absorbs routine phone work; its subject and tone suggest an audience of practitioners rather than buyers.
The posts do not include engagement data or follower composition.
If a campaign depends on reaching a particular function or seniority level, ask David Solomon for a recent audience survey or another documented breakdown of the LinkedIn following, plus results from comparable posts—product/company news, executive-governance commentary, and workforce commentary—to compare reach across those three kinds of content.
The posts don’t identify an official sponsorship or brand-partnership program.
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David Solomon’s company, Superstar AI, offers private consulting on AI strategy and systems, plus client builds such as AI voice receptionists, outbound nurture, lead qualification, database reactivation, and workflow automation.
These are direct service engagements for businesses, not creator sponsorships.
Ask about sponsor policy, rates, and the best inquiry route; the posts don’t publish those details.
Private AI consulting
Buyer requirement
Have a defined AI strategy, prompt-engineering, multi-model orchestration, or system-building problem. The post offers hands-on help in these areas for founders and operators.
AI voice and lead-handling systems
Buyer requirement
Have inbound calls that go unanswered or after-hours demand, and be ready to provide call-flow, qualification, scheduling, and CRM-routing details. The post describes an AI voice receptionist, outbound nurture, a lead qualification hotline, and database reactivation.
Workflow automation builds
Buyer requirement
Identify repetitive intake-to-fulfillment processes and the tools and data they touch. The post describes custom n8n builds connecting tools, data, and processes, delivered as production systems in 4-week sprints.
Good fit — a campaign for a company selling AI voice receptionists or lead-response systems to mortgage lenders, home services firms and professional services businesses, looking for a named operator to front a webinar, a missed-call teardown or a co-authored buyer guide.
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The launch post gives the campaign a spine of numbers buyers can check against their own phone logs: most small businesses answer fewer than four in ten calls, 85% of callers who reach voicemail never call back, and the stated loss is about $126,000 a year.
It names six products, four buyer segments and a delivery model — four-week production sprints, self-hosted systems, multi-model verification — giving campaign copy concrete anchors instead of general AI language.
For a senior rather than operational audience, the governance post offers a different register: three questions about human judgment versus human approval, the liability line when an agent acts on its own, and how machine-speed decisions get audited.
The workforce post offers a softer hook for buyers whose call teams will ask what happens to their jobs.
It frames retraining into customer success, sales coaching and conflict resolution as a possibility under consideration, not a launched program, so present it accordingly.
The campaign’s main limit is audience proof.
The posts provide no breakdown by geography, seniority, employer or function, and no engagement figures.
If success depends on reaching a specific function or seniority level, ask for a recent follower breakdown and results from product news, governance commentary and workforce commentary posts to compare reach across those three kinds of content.
When to choose a specialist
Poor fit — a paid sponsorship placement for a vendor seeking neutral, independently tested coverage of its own AI product.
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Three things in the posts point the wrong way.
First, they do not mention a sponsorship program: none of the four posts includes a sponsor policy, rate card or dedicated partnership inquiry route.
The commercial offer is Solomon's own service business, which puts a vendor-neutral review in direct conflict with the products being reviewed.
The same account promoting an AI voice receptionist is not a credible independent tester of AI voice receptionists.
Second, there is no reach data to price or plan against.
The only published audience figure is an account-level follower count; there are no demographics, engagement data, comments or comparative post performance across roughly seventeen months of posting.
Third, the content mix is narrow — own-company promotion plus macro commentary on AI supply chains, regulation and copyright.
A campaign needing a steady stream of third-party product evaluations, hands-on tool benchmarks or consumer-facing lifestyle content has nothing to draw on.
A practitioner-led or governance-led campaign for the AI services audience is a different question and belongs with the good-fit case above; the mismatch here is specific to sponsored, vendor-neutral review.
A worked campaign example
Worked campaign angle for a technology investment firm assessing consulting fit with David Solomon.
The wedge is portfolio-company governance, not AI tooling.
Build a closed 45-minute briefing for portfolio operators around the three questions in the governance post: which decisions need human judgment rather than human approval, where the liability line sits once an agent acts autonomously, and how decisions made at machine speed get audited.
Open with the assistant-to-operator timeline — drafting email in early 2025, sending routine email later that year, then scheduling, procurement under $5,000 and first-round vendor negotiations by early 2026 — to make an abstract shift concrete in dated steps.
In the middle twenty minutes, take one portfolio company's actual approval process and mark every step that currently involves a human, separating those that need judgment from those that only need sign-off.
Use the closing fifteen minutes to produce a one-page decision-rights memo naming which steps can be delegated, which need an audit trail and which should not be automated at all.
End with a written diagnostic the firm can reuse across its portfolio, plus a follow-on scoping session for the company with the most unresolved decisions.
Sequence the supporting content deliberately: lead with the governance post for the senior audience, pair the missed-call economics from the launch post with it so the operating case and control case arrive together, and hold the workforce post for the internal communications conversation that follows any adoption decision.
Set commercial expectations before the pilot: the offer is private consulting for founders and operators on AI strategy, prompt engineering, multi-model orchestration and system building, delivered through production sprints rather than demonstrations.
Scope the pilot as advisory work with a defined deliverable, not a product trial.
The posts provide no audience reach data.
If the firm needs the briefing to generate measurable inbound demand rather than brief a known internal group, ask for a follower breakdown and post-level results before planning the campaign.
A better fit when a campaign shifts from an operator-led pitch to a product-strategy pitch aimed at product and engineering leaders.
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The AI-feature post speaks to people deciding what to build next: predictive intelligence, personalisation, conversational support, semantic search and workflow automation.
Examples include a project-management tool that forecasts deadlines or a CRM that suggests the best time to contact a lead.
Buyers seeking someone to explain which AI capabilities a software product should ship next, rather than someone who runs a phone-answering service, will find more usable material here than in Solomon's launch and governance posts, which focus on missed calls and delegated decisions.
The two posts cover neither voice agents nor lead response, and say nothing about a business the poster operates.
One is a general capability list with a comment prompt; the other is a hiring notice posted for a friend seeking a business development executive and two interns in Lahore.
The notice shows network reach and recruiting activity, not product or campaign experience.
There is also no governance or risk material, so a senior audience asking about liability and audit would find less here than in David Solomon's governance post.
Two posts across roughly ten months offer thin evidence.
Ask for identity, employer and credentials to be verified.
A better fit when the buyer works in the managed-services or telecom channel rather than with small businesses.
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Both posts address MSPs, telecom providers and UCaaS resellers weighing voice AI for their business customers.
The argument is specific: an agent that answers faster, follows instructions, completes tasks and transfers calls correctly beats a human-sounding agent that does not.
Judge it by leads captured, appointments booked and calls transferred.
The second post urges the same audience to measure outcomes — appointments booked, leads captured and requests resolved — instead of call volume and minutes handled.
For a channel campaign targeting providers who will resell or host AI voice, this framing is more useful than David Solomon's SMB missed-call economics.
Both posts tie back to SkipHold and invite readers to explore the product.
They use channel vocabulary — UCaaS, routing, uptime and managed services — that may lose mortgage brokers or home-services owners.
There is no coverage of the SMB verticals David Solomon names, workforce material or independent product evaluation.
A brief requiring neutral testing or a named sponsor offer will need another creator.
Two posts from consecutive days in October 2026 support the argument, but not a record of sustained commentary.
A better fit for real-estate brokerages and teams.
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The campaign decision is the same one David Solomon addresses — how to answer leads quickly — but the context is different.
The two posts argue that lead-generation spend on Zillow, Facebook, Google and referrals goes to waste when nobody calls back promptly.
They offer brokers actionable fixes: a strict speed-to-lead service level, routing to whoever is available, an after-hours rotation, an automated first SMS or email, an AI voice agent for immediate calls, and CRM tracking of actual response times.
For a real-estate lead-response campaign, this is a closer, more specific case than David Solomon's broader SMB pitch.
Both posts build a before-and-after argument around the same problem.
The material covers only one vertical.
There is no macro, regulatory or workforce commentary, so it lacks the second register David Solomon's governance and retraining posts provide; senior audiences outside real estate will find little to use.
Both posts tie back to MashaAI Connect, the product the poster says they are building, creating the same vendor-neutrality conflict as the other alternatives.
No sponsor program is documented.
Two posts from July and August 2026 support the argument, but not a long record.
David Solomon's posts here include the Superstar AI launch post setting out the product line and founder background, a post on AI agents moving from assistant to operator inside enterprises, a market-commentary post on AI supply chains, regulation and copyright, and an earlier post on what customer success and sales development staff might do once voice AI handles their calls.
No publication dates appear in the posts here, so the posts' order and timeline are not established, and the dated progression quoted in the agent-governance post is that post's own framing rather than a verified record.
The posts here include no videos, transcripts, articles or other channels.
The only audience figure is the account-level follower count of 5,287, observed 9 October 2026; no demographics, engagement figures or per-post performance are available.
Business results, error-rate figures, credentials, client relationships and market statistics are reported as claims made in the posts and are not independently verified.
The posts here identify no book, show or other earlier signature work, and state no sponsor policy or partnership route, so neither is claimed.
The posts here do not provide a fuller name-search picture.
