# Mayur Macwan

Mayur Macwan writes about hiring and recruiting, with a focus on how AI can help screen applicants while people retain hiring decisions. The posts also describe building RoundOne AI, a tool for evaluating applicants against a role specification.

- Profile: https://b2binfluencer.marketing/creators/mayur-macwan/
- LinkedIn: https://www.linkedin.com/in/mayurmacwan
- Topics: artificial-intelligence, recruiting, ai-agents
- Based on 10 dated posts, latest 2026-10-07

## Summary

Mayur Macwan writes on hiring, recruiting, and AI screening, arguing people keep hiring decisions.

- Best for: A recruiting-tech startup commissioning argument-led thought leadership on AI in hiring.
- Not for: Consumer skincare or video-led campaigns that need on-camera demonstration.
- Check first: Confirm booking details, rates, audience analytics, and video samples before contacting them.

### Takeaways

- Give AI agents a controlled door instead of blocking them.
- One permission no AI agent should get in hiring.
- The first CVs read set the standard for every CV after.

## Main ideas

- **Give AI agents a controlled door instead of blocking them**: Macwan argues that companies fighting AI agents treat paying customers as intruders. Courts and partner platforms have already treated an agent acting on a user's instruction as the user. The better move is to set the terms the agent operates under: who gets in, and what it may do.
- **One permission no AI agent should get in hiring**: Mapping the agent-login model onto recruiting, Macwan separates what agents can carry from what they cannot. Volume, matching and the first conversation are fair game; the decision is not. They state the boundary as a rule, not a preference.
- **The first CVs read set the standard for every CV after**: Macwan describes anchoring bias in screening from their own experience as a hiring manager. The first few applications calibrate what "good" feels like, and later candidates are measured against that early frame rather than the role. They stress that this is not a recruiter's fault, but how a brain handles volume.
- **A shortlist ranks who was easiest to justify, not who is best**: Macwan argues that a ranked shortlist reflects what a process could defend on paper, while the strongest signal often has no box on an application form. Their response is to keep reading past the ranking and look at candidates a scoring system flags without selecting.

## Work to read

- [Would you build a second channel for AI agents in hiring?](https://www.linkedin.com/posts/mayurmacwan_recruitment-talentacquisition-aiinhiring-activity-7513629606214602753-nTsP)
- [Amazon locked it; its rivals started owning it](https://www.linkedin.com/posts/mayurmacwan_a-couple-of-weeks-ago-i-posted-about-two-activity-7513531057409998848-gVrs)
- [The first ten CVs set the standard for every CV after](https://www.linkedin.com/posts/mayurmacwan_hiring-recruiting-anchoringbias-activity-7511004575580160000-53kl)
- [The first employee did not make the shortlist](https://www.linkedin.com/posts/mayurmacwan_hiring-recruiting-shortlists-activity-7510638939012222977-wPcD)

## Brand relationships

- **RoundOne AI** (owned_business)

## Partnerships

Mayur Macwan’s four full LinkedIn posts cover recruiting, applicant screening, AI agents in hiring, and industry news about AI agents’ access to retail platforms. They do not describe partnership formats, sponsor policies, rates, deliverables, approval processes, disclosure examples, or an official inquiry route. Amazon, Perplexity, Tencent, Meta, Walmart, Shopify, Stripe, and Sierra appear as subjects of industry news, not as commercial partners. Before planning a campaign, ask for an official media kit or partnership contact.

## Alternatives

- Kalpesh Baxi: Choose Kalpesh Baxi for a campaign aimed at recruitment agency owners about what to fix before AI changes the economics of their business. Baxi starts from a task-level argument: AI will not replace a team; it replaces tasks. In a recruitment consultant's week, sourcing, screening, scheduling, chasing and CRM updates are repeatable work, while understanding people and closing deals remain human. Baxi also offers a competitive frame for a thought-leadership piece: the real threat is a business down the road with the same headcount and far more capacity because it worked out the difference between tasks and jobs first. The practical hook—list the tasks, not the roles—is a ready-made line a brand can build a structure around.
- Vas Georgiou: Choose Vas Georgiou for a campaign seeking a headhunter's firsthand account of why human judgement is the product. Georgiou's posts describe quitting an HR job in 2020 to build Heilsa after being frustrated by recruiters who sent irrelevant CVs, disappeared after a rejection and treated hires as transactions. Six years on, Heilsa has placed people across 30+ countries and narrowed its focus to Law, Cybersecurity & Tech, Financial Services and Healthcare. The strongest support for a thought-leadership piece is the client review quoted in the second post: understanding the company, team and hiring needs not just on paper, but in practice. Georgiou ties that directly to having worked in HR rather than heard about it. For a campaign about what remains human in hiring, that is a more grounded testimonial than a product-feature argument.
- Mehul Jain: Choose Mehul Jain for a campaign arguing that AI agents are becoming a full-stack part of recruiting, with funding and product examples. Jain's first post makes a practical sourcing argument: recruiters hiring for niche roles often wait for a mythical candidate with the exact skills, who fits the budget, is ready to move, applies and is also a culture fit. For ObjectARX, Fortran + HPC or a specific ERP implementation, the best people are not in the applicant inbox. Searching the old database, looking for adjacent experience, mapping the twenty companies where the skill exists, then reaching out by email, WhatsApp, phone and LinkedIn are all steps an AI agent could automate. Jain ties that workflow directly to Fabric searching existing candidate databases, ranking people and starting outreach after approval. The second post offers a market trend for a thought-leadership piece: Metaview raising $110M after moving from interview notes to an AI recruiting coworker, Jack & Jill's $40M Series A for candidate and hiring-team agents, and Juicebox's $116M for sourcing and outreach agents, with Fabric itself expanding from AI interviews and resume screening toward a full-stack hiring platform.

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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/