# David Solomon

David Solomon is an AI systems architect who builds voice agents and business automation systems, and advises businesses on AI strategy and implementation.

- Profile: https://b2binfluencer.marketing/creators/david-solomon/
- LinkedIn: https://www.linkedin.com/in/davidsolomonstrategy
- LinkedIn followers: 5,287
- Topics: artificial-intelligence, ai-governance, ai-agents, business-automation
- Based on 10 dated posts, latest 2026-10-09

## Summary

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.
- Check first: Confirm sponsor policy, rates, audience breakdown and comparable post results first.

### 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.

## Main ideas

- **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. 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.
- **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. 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.

## Work to read

- [Superstar AI launch: what the company builds and who it serves](https://www.linkedin.com/posts/davidsolomonstrategy_aiagents-voiceai-businessautomation-activity-7441182869391314944-ET0E)
- [2026: AI agents move from suggestions to actions](https://www.linkedin.com/posts/davidsolomonstrategy_aiagents-executivestrategy-futureofwork-activity-7422444698973184000-Ix6-)
- [The AI race changed today: supply chain, regulation and copyright](https://www.linkedin.com/posts/davidsolomonstrategy_aistrategy-aitrends-supplychain-activity-7429340806555238400-93Dz)
- [Voice AI and the roles it displaces: CS and SDR](https://www.linkedin.com/posts/davidsolomonstrategy_what-could-somebody-in-cs-or-sdr-do-best-activity-7247274908601188356-kk9W)

## Brand relationships

- **Superstar AI** (owned_business)
- **Google** (employment)

## Partnerships

The posts don’t identify an official sponsorship or brand-partnership program. 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.

## Alternatives

- Ameer Hamza Mushtaq: A better fit when a campaign shifts from an operator-led pitch to a product-strategy pitch aimed at product and engineering leaders. 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.
- Alex Gonzalez: A better fit when the buyer works in the managed-services or telecom channel rather than with small businesses. 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.
- Mashal Fatima: A better fit for real-estate brokerages and teams. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/