Hiring has always had one unfair advantage that most companies talk about but few truly master: the trusted referral. A strong referral can cut through noise faster than a polished resume, a cold LinkedIn message, or a crowded job board. It carries context. It carries a reputation. It gives hiring teams a reason to pay closer attention.
That is the problem Greg Gunn is taking on with Cedar.
Cedar is being built as an AI-native professional network, with recruiting as its first major use case. The idea is simple, but the execution is ambitious. The best people often do not apply for jobs. They are usually already working, building, advising, investing, or quietly helping other teams grow. They may never show up in a company’s inbound applicant pool. But someone inside the company probably knows them.
The challenge is that most organizations do not know how to activate those hidden networks at scale. Employees know talented people from past roles, startup communities, investor circles, engineering teams, universities, customer networks, and founder relationships. Yet those connections usually stay scattered across memory, inboxes, LinkedIn lists, and private conversations.
Greg Gunn and Cedar are trying to turn that messy, human network into something companies can actually use without stripping away the trust that makes referrals valuable in the first place.
Who Is Greg Gunn
Greg Gunn is the co-founder and CEO of Cedar, but his interest in talent networks did not appear overnight. His career has moved through startups, company building, remote work, professional communities, and recruiting problems that show up when companies grow fast.
Before Cedar, Greg was closely associated with Hootsuite and Commit, two names that help explain why Cedar’s mission makes sense. His public founder story shows a clear pattern: he has spent years thinking about how people find opportunity, how companies find talent, and how professional networks can be built in a more useful way.
At Hootsuite, Greg saw what rapid growth looks like from the inside. Scaling a company from a small team into a much larger organization creates pressure in every direction, but hiring is usually one of the biggest. When a company is small, referrals happen naturally. Everyone knows someone. The team can ask around. Founders can reach into their personal networks.
As the company gets bigger, that informal system starts to crack. The number of roles increases. The number of employees grows. The number of possible referrals becomes too large for recruiters to manage manually. People still know great candidates, but the company has no easy way to see those trusted paths.
That experience matters because Cedar is not being built around a theoretical recruiting problem. It is being built around a pain point that fast-growing teams often feel deeply. Greg Gunn’s background gives Cedar a practical founder story: someone who has seen the value of referrals, seen where they break, and is now building a product to make them scale.
What Cedar Is Building
Cedar is building around a clear belief: the next great hire may already be connected to someone inside the company.
Instead of treating recruiting as only a database search, Cedar focuses on professional relationships. Its model is based on mapping trusted connections and using AI agents to help surface high-signal candidates from those networks. In plain terms, Cedar wants to help companies understand who their employees know, who they trust, and which people might be relevant for important roles.
That makes Cedar different from a traditional job board or applicant tracking tool. A job board starts with people who are actively looking. An applicant tracking system organizes people who have already applied. Cedar starts from a more human place: the trusted relationships that already exist around a company.
This is important because modern hiring is not just a search problem. It is a signal problem. Recruiters do not only need more names. They need better context. They need to know why someone is worth reaching out to, who can introduce them, what kind of work they are known for, and whether there is real trust behind the connection.
Cedar’s approach brings together AI agents, professional referrals, relationship intelligence, and talent acquisition in one system. The goal is not to replace recruiters or remove people from hiring. The goal is to make the best human signals easier to find.
Why Professional Referrals Matter So Much in Hiring
Professional referrals work because they reduce uncertainty.
A resume can show experience, but it rarely tells the whole story. A profile can list skills, but it does not always show how someone handles pressure, communicates with a team, leads a project, or earns trust. A referral can add that missing layer of context.
When someone trusted says, “I worked with this person and they are excellent,” that signal carries weight. It does not guarantee a hire, but it helps a recruiter focus attention in a crowded market. It also makes the candidate experience warmer. Instead of receiving another generic message from a stranger, the candidate may hear about an opportunity through someone they already know.
For startups and growth-stage companies, referrals can be especially powerful. These companies often need people who can handle ambiguity, move quickly, and take ownership without waiting for perfect systems. Those traits are hard to judge from keywords alone. Trusted networks can help uncover people who have already shown those qualities in real work environments.
That is why referrals have always mattered. The problem is that most companies still manage them in a passive way. They send a message asking employees to refer people. They offer a bonus. They wait. Sometimes it works. Often it does not.
The issue is not that employees do not know good people. The issue is that most referral systems depend on employees remembering the right person at the right time, for the right role, and then taking action without much help.
Cedar is built around the idea that this process can become more intelligent, more active, and more scalable.
The Problem Cedar Is Trying to Solve
The biggest problem with referrals is not trust. Trust is the strength. The problem is access.
Companies are surrounded by valuable relationship data, but they rarely know how to use it. One employee may know a great engineering leader from a previous company. Another may have worked with a strong product manager years ago. A founder may know a sharp operator from an investor network. A customer success leader may know someone perfect for a sales role.
All of that knowledge is useful, but it usually lives in people’s heads.
As a company grows, the math gets harder. More employees means more networks, more potential connections, and more possible candidates. But it also means more complexity. Recruiters cannot manually interview every employee about every person they have ever worked with. Employees do not always know which roles are open. Hiring managers may not know which internal person has the warmest path to a candidate.
This is where Cedar’s AI-agent approach becomes interesting. Instead of waiting for referrals to arrive randomly, Cedar can help map relationships more systematically. AI agents can support short conversations, organize relationship context, and help build a talent graph that becomes more useful over time.
That matters because recruiting teams do not need another giant pile of names. They need a smarter way to identify the right people, through the right relationships, at the right moment.
How Greg Gunn’s Hootsuite Experience Connects to Cedar
Greg Gunn’s experience with Hootsuite gives the Cedar story more weight because Hootsuite was not a small, slow-moving environment. It was a company that had to think about growth, scale, and talent at speed.
When a startup begins to grow quickly, hiring changes from a simple founder-led task into a serious operating challenge. The company needs engineers, salespeople, marketers, product leaders, customer teams, finance talent, and managers who can keep pace with the business. At the same time, the culture is still forming. Every hire matters.
In that environment, trusted referrals can be one of the best hiring channels. People who already understand the company can point toward others who may fit the pace, values, and work style. But as the company expands, the original referral flow becomes harder to manage.
That is the key link between Greg Gunn’s past work and Cedar’s current mission. Cedar is not just about making recruiting faster. It is about making one of the strongest hiring signals usable when a company has outgrown informal systems.
Greg’s founder story also connects to Commit, which was built around engineers, career growth, remote work, and access to better opportunities. That background adds another layer to Cedar. It shows a long-running interest in how talented people move through the professional world, how companies reach them, and how networks can open doors.
Cedar feels like a natural next step in that journey. It takes the professional network idea and rebuilds it for an AI-first hiring market.
How Cedar Uses AI Agents to Make Referrals More Scalable
AI agents are often described in complicated ways, but Cedar’s use case can be understood simply.
An AI agent can help do the structured work that humans do not have time to do manually. In Cedar’s case, that means helping map trusted professional relationships, understand who employees know, and surface candidates who may be relevant for specific roles.
The value is not only automation. It is memory, structure, and timing.
A recruiter may not know that an employee once worked with a brilliant backend engineer at a previous company. The employee may not think to mention that person when a role opens. The candidate may not be actively applying anywhere. Without a system, the connection is invisible.
Cedar’s AI agents are designed to make those hidden connections easier to discover. By turning professional relationships into a living talent graph, Cedar can help companies move from passive referrals to proactive referral intelligence.
This does not mean hiring becomes less human. In many ways, it makes the human part more visible. The important signal is still trust. The difference is that Cedar helps companies find that trust before it gets lost in scattered conversations.
Why Cedar Feels Different From Traditional Recruiting Tools
Most recruiting tools were built around process. They help companies post jobs, track applicants, search profiles, manage interviews, and move candidates through a pipeline. Those tools are useful, but they do not always solve the hardest part of hiring: finding the right person before everyone else is chasing them.
Cedar is working from a different starting point.
Job boards depend heavily on active applicants. That can work for some roles, but many of the strongest candidates are not scrolling through postings every day. They may be happy where they are. They may only listen if the opportunity comes through someone they trust.
Cold outreach has the opposite problem. It can reach passive candidates, but it often lacks context. Candidates receive too many generic messages, and recruiters struggle to stand out. A warmer path through a trusted connection can make the conversation feel more relevant and more respectful.
Traditional referral programs also have limits. They usually rely on employees taking the initiative without much support. A company may announce a referral bonus, but that does not mean employees will scan their entire professional history and identify the best person for each role.
Cedar’s bet is that referrals should not be random. They should be intelligent, structured, and easier for both employees and recruiting teams to use.
The Role of Trust in Cedar’s Professional Network
The timing behind Cedar is important because AI is changing how companies think about work, hiring, and competitive advantage. As more tasks become automated, the quality of people inside a company becomes even more important.
That is why Greg Gunn’s public framing around talent as a moat fits Cedar so well.
In an AI-first world, companies may have access to similar tools, models, and workflows. What separates one company from another is often the team: who understands the customer, who can make sharp decisions, who can build trust, who can sell the vision, who can ship under pressure, and who can attract more great people.
Cedar sits directly inside that shift. It is not only trying to help companies fill roles. It is trying to help them understand their talent networks as a strategic advantage.
Trust is central to that idea. A professional network without trust becomes just another database. A referral system without structure becomes too random to scale. Cedar is trying to combine both sides: the depth of trusted relationships and the power of AI to organize them.
That combination could become especially useful for companies hiring in competitive markets like software engineering, product leadership, AI, go-to-market, design, operations, and executive roles.
Cedar’s Early Momentum and Market Signal
Cedar is still early, but its public positioning already shows why people are paying attention. The company has been described as operating in stealth while building around AI agents, recruiting, and professional relationship mapping. Its association with a16z Speedrun also gives it a clear place in the current wave of AI-native startups.
The company’s public profile highlights several strong signals: an experienced founding team, early traction, and advisors connected to major talent and technology organizations. Names associated with Cedar’s advisor network include Laszlo Bock, known for his work at Google, Tiffany Fenster from Stripe, and Brendan Browne from Replit.
Those names matter because Cedar is not solving a small administrative problem. It is touching recruiting, talent strategy, professional networks, and company-building. Advisors with experience in people operations, high-growth technology companies, and talent systems can bring useful perspective to that kind of product.
Still, the real test for Cedar will be execution. Recruiting teams have seen many tools promise better sourcing, better automation, and better hiring outcomes. What makes Cedar interesting is its focus on trusted relationships rather than simply bigger databases.
If Cedar can help companies consistently discover high-quality candidates through internal networks, it could become a valuable layer in the modern hiring stack.
Why Greg Gunn’s Founder Story Works for a Success Article
The story of Greg Gunn and Cedar works because it is not just about launching another AI startup. It is about a founder applying hard-earned lessons from company building to a problem that many growing teams quietly struggle with.
Greg’s career has touched several areas that now come together inside Cedar: startup scale, professional networks, technical talent, remote work, referral systems, and AI-driven workflows. That gives the company a stronger narrative than a simple “AI for recruiting” pitch.
The achievement angle is also clear. Greg Gunn is building from experience. He has seen how teams grow, how hiring pressure builds, and how valuable referrals can become when a company needs talent quickly. Cedar turns that experience into a product thesis: professional relationships are one of the most powerful hiring assets a company has, but they need better infrastructure.
That is a strong founder-market fit story.
It also makes Cedar relevant beyond recruiters. Founders, operators, investors, people leaders, and startup employees can all understand the problem. Most people have seen a great hire come through a trusted introduction. Most have also seen referral programs fail because they were too passive or too hard to manage.
Cedar is trying to close that gap.
What Cedar Could Mean for the Future of Recruiting
If Cedar’s approach works, recruiting may become more relationship-driven, not less.
That may sound surprising in an AI-heavy market. Many people assume AI will make hiring colder, faster, and more automated. Cedar points toward a different possibility. AI can also be used to make human trust easier to find.
Instead of replacing the referral, AI can strengthen it. Instead of reducing people to keywords, it can help identify the real-world relationships around them. Instead of forcing recruiters to search blindly, it can show warmer paths into high-quality talent networks.
That could change how companies think about employee referrals. A referral program may no longer be just a bonus page in an HR portal. It could become an active talent intelligence system that helps teams understand the professional graph around their company.
For candidates, this could also create better experiences. A warm introduction through someone trusted is usually more meaningful than a generic message. It gives the candidate a reason to listen and gives the company a better reason to reach out.
For hiring teams, Cedar could help reduce wasted effort. Recruiters may spend less time digging through low-signal profiles and more time building thoughtful conversations with candidates who come through trusted paths.
Greg Gunn and Cedar’s Place in the AI Recruiting Shift
The broader recruiting market is moving toward better signals. Companies already have access to huge amounts of candidate data. The problem is knowing what to trust.
That is why terms like talent intelligence, relationship intelligence, candidate sourcing, passive talent, employee referral system, agent-based recruiting, and AI hiring tools are becoming more important. Hiring teams want systems that help them see beyond surface-level profiles.
Cedar fits into this shift because it focuses on the relationship layer. It treats the professional network as something deeper than a public profile page. The real value is not only who someone is connected to. It is who they have worked with, who they trust, who trusts them, and which relationships can lead to meaningful hiring conversations.
That is where Greg Gunn is positioning Cedar. The company is building at the intersection of AI agents, trusted professional relationships, scalable referrals, and modern talent acquisition. It is a timely space because every company wants better people, but few have a strong system for turning employee networks into a reliable hiring advantage.
Cedar’s promise is not that AI alone can solve hiring. The stronger idea is that AI can help companies use the human signals they already have.
That is what makes Greg Gunn’s work with Cedar worth watching. He is not trying to make referrals less personal. He is trying to make them work at the scale modern companies need.
