I have been thinking, all week, about the difference between what looks like the thing and the thing itself.

It started at a coffee shop. Strangelove, on the east side of downtown Austin, late Sunday afternoon. I came there to clear my inbox and prep for the week ahead. The only sticker on my laptop is a small red dot covering the Apple logo, with the word Leica in white inside it. I was in for a treat! A analog photography meet-up was in full-swing. Within an hour, six different strangers had stopped at my table. They saw my sticker and wanted to show me their cameras. I saw two M6s, an M7, an M10, and someone’s grandfather’s M3 in a beat-up leather case. The guy with the M6 was running the same Walter Mandler-designed 35mm I have been quietly falling in love with for months.

That night, I intentionally left my Leica on the shelf at home. I had decided, that morning, that the responsible move was the to clear out my inbox ahead of a busy work-week.

The whole evening I sat among photographers, talking about light and lenses, without a camera in my hands.

The lesson was small and immediate. The sticker ≠ camera.

I noticed the same theme in two other conversations this week, both of them inside my day job.

The first was on customer calls. RightMatch integrates with dozens of applicant tracking systems, runs AI video interviews, and scores resumes. By any normal yardstick, that is the camera.

But the most candid feedback I heard, repeatedly, was that the AI work recruiters were proudest of was not happening inside their applicant tracking system. It was happening inside Copilot. Inside Claude. Inside ChatGPT. A recruiter writes a great Boolean string with her chatbot at eleven in the morning, closes the tab, and her teammate one floor up rebuilds the same string from scratch the next week. The wins are real but, they die in silos.

So we built MCP into RightMatch this month, so a recruiter can post jobs, source candidates, send Mira interviews, and connect calendar and ATS data without ever leaving the chatbot her CIO already licensed.

The second conversation was over coffee with another exited founder, talking about why so many great pre-seed companies feel stuck. Five years ago, the bar was clear. A working product, a real pain point, early traction, a network. Today those things are the price of entry. Open Threads or X right now and scroll #buildinpublic for thirty seconds. You will see two dozen beautifully shipped MVPs that were built in a weekend.

The product is not the moat anymore. The product is the sticker. The moat is whether you have an unfair advantage no one in that feed can replicate, and a customer acquisition motion that survives contact with the noise.

Three different rooms. The same uncomfortable lesson.

The visible artifact invites the conversation. Only the working practice finishes it.

So this is me trying to pay attention. To the integration count and to the integration. To the sticker on the laptop and to the camera on the shelf I should have brought.

More soon, once the next roll comes back from the lab.

Stay specific, stay honest, bring the camera.

This is part of a series of reflections written while building RightMatch in a volatile market.

The headline story about AI and jobs has always been humans versus machines. Layoffs, displacement, the shrinking middle. But the quieter story underneath is stranger and harder to look away from.

The same companies citing AI as a reason to lay people off are turning around and hiring those same people back, cheaply and without protection, to train the next model.

An Ivy League PhD on a contract that paid $35 an hour and disappeared overnight. A philosophy graduate reviewing AI-generated slop in the middle of the night. Median earnings under $23,000 a year for a workforce literally building one of the most heavily capitalized technologies of our time.

I keep coming back to a line I wrote in December, that every dataset has an origin. Every hiring funnel we optimize maps back to someone trying to move their life in a better direction. When I wrote that, I was thinking about the people moving through the funnel. This piece made me think about the people building the funnel itself, and how often they are the same people, just at a different point in the cycle. The Ivy League PhD applying for $55-an-hour contract work is the same candidate our industry was rejecting from full-time roles a month earlier. The supply chain of AI has people in it, all the way down, and most of them are invisible by design. Silicon Valley has spent a long time building an aesthetic where the model is the magic and the labor is a footnote. The footnote is the story.

What bothers me most is the ideology underneath it. There is a real belief, openly stated by some of the loudest voices in the space, that human input is friction. That the goal is fewer people in the loop. That a leaner team and a thinner middle is just progress. I do not think that is progress. I think that is a choice dressed up as inevitability.

That is not a side effect. That is the design.

I run RightMatch, a hiring company. I sit adjacent to exactly the supply chain Karen is describing, and I am not pretending we have it all figured out. Founders in my position are the ones writing the job posts, designing the funnels, choosing what to automate and what to leave to a human. That puts a real weight on the work we do. The companies building AI on top of a hidden underclass are making a bet that none of us will look closely at how the sausage is made. The founders who win the next decade are not the ones who automate the most. They are the ones who are honest about who their tools touch, and how, and what they owe those people in return.

There is a version of this industry that treats the people doing the work, the people inside the funnel, the people training the model, as inputs to be minimized. There is another version that treats them as the whole point. Those are different companies. They will produce different futures.

Watch Karen's piece if you have twenty minutes. It is worth your attention, and attention is the thing we are all running low on.

If you're building in AI right now, what's the line you won't cross on labor? I'm trying to figure out what "building responsibly" actually looks like from inside a hiring company, and I'd genuinely like to hear how others are thinking about it. Drop a comment.

If this hit, restack it so more founders see it. If you want more like this, subscribe.

I’ve been overdue for a check-in here. Life has been moving fast, and when things move fast the instinct is to optimize, to compress the world into tasks and deadlines. But speed has a way of flattening meaning. I wanted to pause for a moment, recalibrate, and share a few things I’ve been thinking about while building RightMatch and trying to stay awake to the world outside my laptop.

The past few months have been a loud reminder that we’re all living in overlapping realities. On one hand, AI continues its relentless sprint, with new models landing so quickly that “state of the art” barely lasts a quarter. Productivity curves bend upward, barriers fall, teams get leaner, and the world suddenly expects every founder to operate like they have a neural network in their bloodstream.

On the other hand, the world of work feels more fragile than the headlines suggest. Layoffs have crossed the million mark this year in the US alone, the highest level since the pandemic. Entry-level hiring is at its weakest point in more than a decade. New graduates are struggling to find their footing, often taking longer to secure jobs than high school-educated workers. A degree no longer guarantees an edge. The job market is reshuffling itself in real time, and it’s creating a strange tension: economic acceleration paired with personal uncertainty. As someone building hiring technology, it’s impossible not to feel the weight of that, knowing the tools we create touch real people navigating moments they didn’t choose.

Running a company in this environment feels a bit like shooting analog film in the age of 8K smartphones. You can capture every pixel with perfect clarity and still miss the story entirely.

Photography has been its own form of calibration for me. Film forces patience. It forces trust. There’s no preview screen offering reassurance. You click the shutter and for a while you live inside uncertainty. Later, the negatives return with their tiny imperfections and uneven textures, somehow more honest than anything a sensor would have produced. The world feels slower on film, less manic, more interpretable. That slowness has been reminding me to protect the parts of building that don’t happen at high speed.

Even inside a company centered on automation and intelligence, the work remains deeply human. Every dataset has an origin. Every hiring funnel we optimize maps back to someone trying to move their life in a better direction. Every interview we automate is still a person’s inflection point. And in a year when millions are job searching longer, questioning their place in the market, and navigating uncertainty they didn’t choose, that human layer matters more than it did when the job market felt predictable. It shapes how I build, what I prioritize, and how I think about responsibility at scale.

I’ve also been noticing a quiet shift across the tech world. Conversations with founders and investors feel different than they did even six months ago. People are questioning efficiency for efficiency’s sake. Now that automation is nearly baseline, the differentiator is depth. Knowing which problems are worth solving. Understanding when to rely on models and when to lean on judgment. Choosing clarity over noise. Depth is becoming the new speed.

That shift isn’t just technical. It’s personal. We’re surrounded by more tools, more streams, more acceleration than any generation before us. Yet the scarcest resource is undivided attention. I’m trying to protect mine. Trying to see what’s actually happening, not just what’s urgent. Trying to widen the bandwidth for curiosity.

So this is me trying to pay attention. To the craft of building. To the people trusting us with their hiring pipelines. To the workers navigating a volatile market. To the strange, turbulent macro environment. To the quiet in between the decisions. To the way morning light hits a half used roll of Portra sitting on my desk.

The next few months will surprise us, as they always do. Markets will swing. Models will evolve. Teams will adapt. But I’m holding onto one principle, a simple mantra that keeps me oriented when things move too fast: stay honest, stay curious, protect the texture.

More soon, likely once the next roll comes back from the lab.

When you see your company featured in Times Square, you know something big is happening.

Last week, I stood in the heart of NYC watching RightMatch AI flash across the NASDAQ billboard.

Being part of the 2025 global cohort of Morgan Stanley ISV has been incredible so far, and seeing our work celebrated alongside other innovative founders was surreal.

Here's what this moment represents:

→ Recognition for founders solving real problems → A spotlight on innovation that comes from lived experience

Standing next to Scott Poniewaz, watching our company name light up Times Square - the significance hit me.

This is what the future of innovation can look like.

We're grateful for the capital, curriculum, mentorship and networking opportunities that are helping us scale RightMatch AI's mission to match people to opportunities based on their skills, not their pedigree.

Earlier in the week, we brought together the community for an event at our Ethical AI House on the Vineyard with the one and only . These connections remind me why we're building RightMatch: to bring humanity back into hiring through intelligent technology and to share our journey of building high-impact tech using AI.

As founder of RightMatch AI, I’ve seen both sides: technology that automates candidates into oblivion, and technology that opens space for authentic, revealing conversations. We’re building the latter.

This newsletter aims to cut through the noise and deliver actionable intelligence that will help you make better hiring decisions and build stronger teams.

3-Point Friday: AI & Talent Acquisition (Korn Ferry, 2025)

• 🏀 The Assist: The Assist – 67% of recruiters upped AI use this year, but most tools make hiring robotic. The real win? AI that amplifies human connection.

• 🏀 The Steal: Skills-based hiring is 2025’s top priority, yet execution lags. The firms who crack this first will own the talent market.

• 🏀 The Fast Break: With 75% of companies locked into hybrid work, demand is spiking for interview tools that assess remote collaboration skills.

The Interview Problem No One’s Talking About

Most interviews are stuck in rigid scripts. A candidate says, “I know Python”, and the interviewer moves on. The good stuff—the thinking, the problem-solving—never comes out.

RightMatch changes that.

Example:

Candidate: “I’m experienced with Python, JavaScript, and Java.”

RightMatch: “Tell me about the trickiest Python bug you’ve debugged. How did you approach it?”

No generic checklists. Just intelligent, adaptive follow-ups that make interviews feel like professional dialogues—and reveal how candidates actually think.

The result? Candidates feel respected. Hiring teams get sharper insights. Everyone wins.

Sterling Smith
Founder & CEO, RightMatch AI

P.S. We’re inviting a small group into our Done-For-You hiring beta. If you want in, reply with BETA and I’ll personally reach out.

The AI Hiring Revolution: Faster, Fairer, and Still Human (www.rightmatch.app)

AI is revolutionizing hiring – but where did all the humans go? This question, raised in The Washington Post’s recent piece on AI-driven recruiting, captures a common anxiety among job seekers today. In that article, a 21-year-old applicant described how chatbot assistants and even a “talking robot” interviewer handled most of his 150+ job applications – leaving him feeling that “nobody is going to see it” and that companies aren’t truly serious about hiring . Such experiences reflect a broader trend: the rapid adoption of AI tools to streamline hiring is boosting efficiency, yet also sowing distrust on both sides of the hiring equation.

As the founder of RightMatch AI, an AI-powered pre-screening platform, I’ve watched this evolution from the front lines. The Washington Post’s piece resonated with me because it highlights exactly the challenges and opportunities that inspired me to build RightMatch. In this post, I’ll reflect on the article’s themes – efficiency, bias, and the human touch – and share how, in our journey to transform hiring, we’ve learned to harness AI’s power without losing fairness or humanity.

AI Hiring at Scale: Efficiency and Reach

It’s no surprise why employers are embracing AI in recruitment. Hiring teams today face tsunami-level volumes of applications, especially for attractive roles. AI offers a lifeline by swiftly screening and sorting candidates, a task that would drown human recruiters in work. In fact, 81% of companies now use AI for preliminary screening of resumes, and 60% use AI for at least initial interviewing. This scale of adoption means chances are high that, like Jaye West in the Post story, your resume or application will be parsed by an algorithm before a human ever reads it.

The benefits of AI in hiring are significant. Automation dramatically speeds up hiring cycles – Hilton, for example, cut its time-to-fill open positions by 90% using AI-driven tools . Recruiter chatbots can handle repetitive tasks like scheduling and Q&A, saving human recruiters hundreds of hours; one company reported saving 1,200 hours of recruiter time in just three months after integrating an AI chatbot . These efficiencies translate to real results: faster responses for candidates, and hiring managers freed to focus on high-value activities. There’s evidence that AI can even boost hiring outcomes: candidates identified by AI were 14% more likely to pass interviews and receive offers than those selected solely by humans, according to one study. By casting a wider net and crunching data objectively, AI tools help ensure strong candidates don’t get overlooked.

At RightMatch, we’ve seen how the right AI tools can slash hiring timelines by up to 75%. We built our platform to take over the initial screening workload – conducting structured, multi-sensory interviews (text, voice, and video) on demand – so that hiring teams get a rich, data-driven profile of each candidate before the first call . Instead of drowning in résumés and endless phone screens, recruiters receive an interactive assessment summary that highlights skills, personality insights, and even flags keywords or trust scores from the AI interview. This means companies can efficiently screen hundreds of applicants in the time it used to take to phone-screen a dozen, without missing those promising candidates who deserve a closer look. The end goal is not just speed, but finding the right match (yes, we named our company after that idea!) more reliably.

Key efficiency gains from AI in hiring:

• Volume Screening: AI algorithms can scan thousands of resumes quickly, filtering by qualifications so recruiters review only the top tier – a necessity when 200+ applicants vie for one role. Mercer research finds 81% of employers now use AI to handle this screening step.

• Faster Time-to-Hire: Automated scheduling, chatbot FAQs, and AI interviewers compress what used to be weeks of coordination. Companies like Hilton improved hiring rates by 40% while cutting fill times nearly in half with AI tools. Some recruitment chatbots have reduced time-to-hire by up to 70% for high-volume roles .

• Consistent Candidate Experience: An AI assistant never forgets to follow up. Every applicant gets the same initial experience – which can include instant updates or next-step instructions – ensuring no one falls through the cracks due to human busy schedules.

The Washington Post article rightly notes that this “speedy embrace” of AI aims to make hiring more efficient. And on that promise, AI is delivering. But efficiency is only part of the story. We must also ask: efficient for whom, and at what cost? This brings us to the critical issues of fairness and trust.

Tackling Bias and Ensuring Fairness

One of the greatest hopes for AI in hiring is that it could reduce the biases – conscious or not – that human recruiters might have. 68% of recruiters in one survey agreed that introducing AI can help combat unconscious bias in hiring. By evaluating candidates against consistent criteria, an AI might ignore irrelevant factors like appearance, gender, or background that shouldn’t influence hiring. In theory, a well-designed algorithm cares only about who’s qualified.

In practice, however, AI is only as fair as the data we feed it and the design we give it. The Washington Post piece surfaces fears that automated hiring can perpetuate or even worsen biases if not handled carefully. We have some stark real-world lessons here. Amazon, for instance, famously tried to build an AI resume screener – only to find that their model had taught itself to downgrade female candidates, because it was trained on past hiring data dominated by men. The AI concluded (incorrectly) that being male was a qualifier, penalizing resumes that mentioned women’s groups or all-female colleges. Amazon had to scrap that tool entirely, a cautionary tale that blind reliance on historical data can bake in discrimination. As one Carnegie Mellon researcher put it, “How to ensure the algorithm is fair, how to make it explainable – that’s still quite far off”.

To address these concerns, there’s a growing push for transparency and accountability in AI hiring. New regulations are emerging: New York City, for example, now requires companies to conduct regular bias audits on their hiring AI and publish the results. This kind of oversight is crucial. Candidates and companies alike deserve to know that an algorithm isn’t unintentionally screening out qualified people due to race, gender, age, or other protected traits. Lack of transparency erodes trust – if you’re rejected by a faceless system with no explanation, it’s natural to feel frustrated or suspicious. (No wonder job seekers like the one in the Post story are assuming “nobody is going to see” their application – the process can feel like a black box.)

Building fairness into AI has been a core focus for us at RightMatch. Our approach is to explicitly design AI assessments that focus solely on job-relevant skills and experience, and avoid inputs that could act as proxies for bias . For instance, our system doesn’t ask for or factor in a candidate’s age, gender, ethnicity, or unrelated personal background – it zeroes in on competencies, work samples, and situational responses. The goal is to give every candidate a fair shake based on what they can do, not who they are. We also continually monitor our models for any disparate impact. If the data show that certain groups are advancing at lower rates, we dig in to adjust the algorithms or the question sets to correct any imbalance.

Another aspect of fairness is providing feedback and transparency to candidates. One complaint raised in the Post article is how impersonal an AI-driven process can feel – candidates submit information and never hear back beyond generic bot emails. To mitigate this, RightMatch actually gives personalized feedback to applicants, even those who don’t make the cut. After an AI interview, candidates receive a brief report highlighting their strengths and suggestions for improvement. For example, if someone’s answers indicated they lack experience in a certain software tool, we might encourage them to build that skill and try again in the future. This kind of transparency turns a potentially discouraging silence into a learning moment. It’s our way of saying: we do see you, and here’s how you can get closer to the “right match” next time. In doing so, we hope to demystify the AI’s decisions and keep the talent pipeline more engaged and informed.

Of course, technology alone can’t guarantee fairness – human oversight and ethical guidelines are essential. I strongly believe in a “human-in-the-loop” approach for hiring AI. In practice that means our AI flags top candidates and potential concerns, but final hiring decisions rest with people, and there are checkpoints to review the AI’s recommendations. Many recruiters agree with this blended approach: 75% say AI can aid in hiring decisions only if humans are involved in the process. The algorithm might rank or score applicants, but then a hiring manager reviews those rankings with a critical eye, aware of the AI’s known limitations. This partnership between human judgment and machine consistency is how we get the best of both worlds. On the flip side, some in the industry predict that in the near future AI might handle hiring end-to-end – with nearly 80% of HR professionals in one poll expecting that humans won’t need to be involved at all. Count me as a skeptic on that front. Removing humans entirely from hiring is a bad idea – not just from an ethical standpoint, but also because hiring is about humans working with humans at the end of the day. Which leads us to the final and perhaps most important piece: the human touch.

Why the Human Touch Still Matters

Hiring is inherently a human endeavor. Even as we delegate certain tasks to AI, relationships, intuition, and empathy remain at the heart of a successful hire. The Washington Post article highlights this through voices on the employer side: one recruiting lead described how, in an era of remote recruiting and AI tools, “preserving a human touch matters more than ever”. I couldn’t agree more. Technology should enable more human connection in hiring, not less.

We’ve all heard horror stories of robotic hiring processes – like the “utterly freaky” AI-proctored video interview that Jaye West experienced . No smiling interviewer, no nods or laughs, just answering questions to a screen while an algorithm watches and evaluates. That can be unsettling! It’s a good reminder that candidate experience matters. If a qualified person walks away from an interview feeling like they were just interrogated by HAL 9000, your hiring process has failed even if the algorithm scored them 5/5. Candidates are evaluating your company culture at every step, and a cold, opaque process will turn great people away.

So how do we keep the human element alive, even as AI takes on a bigger role? First, AI should augment human interaction, not replace it entirely. For example, at RightMatch we use AI to conduct the initial Q&A with candidates, but we design that interaction to be engaging and respectful – candidates can do the interview on their own time, in a comfortable setting, with a friendly interface that explains what to expect. We avoid the trap of an impersonal interrogation by, say, allowing candidates to rerecord a video answer if they had a technical glitch or to chat with a real human if any question is confusing. Then, once the AI evaluates and transcribes the responses, a human recruiter reviews the highlights and the raw footage. By the time the recruiter speaks with the candidate live, they already have a feel for the person’s communication style and strengths (thanks to the AI summary), and can spend their time in the next interview digging into deeper topics and building rapport. In short, the AI handles the grunt work, but the human-to-human conversation is still front and center where it counts.

I also advocate for companies to be transparent with candidates that they are using AI, and explain the purpose. In my experience, people respond well if you explain, for example: “We use an AI tool to ensure every applicant gets a fair initial interview and to help us review all responses consistently. The AI doesn’t make final decisions, but it helps us not miss anyone. You’ll also receive feedback from it as part of our commitment to transparency.” This kind of message can turn a potentially alienating experience into a selling point for the company’s culture. It tells candidates: we value fairness and your time, so we invested in tools to make the process faster and more objective – without forsaking the personal touch.

Ultimately, certain aspects of a hire will always need human judgment. Cultural fit, for instance, is hard for any machine to gauge accurately, since it involves the subtle chemistry between people and teams. Likewise, soft skills like creativity, leadership, or empathy are complex and context-dependent; AI can pick up cues (tone of voice, word choice) but a human interviewer will best sense the authenticity and nuance of those qualities. As one survey found, about 62% of recruiters believe that while AI will handle initial screening, the final hiring stages will always be driven by humans. I’m in that camp. AI can rank resumes by keywords or even analyze facial expressions, but it takes a human to say, “This person will inspire their coworkers,” or “I trust this candidate to represent our brand.”

In the Post article, despite all the high-tech screening going on, it was clear that both candidates and hiring managers felt something was missing – that “where did all the humans go?” moment. The answer, I believe, is that humans should go right back into the loop – armed with better information from AI, but fully engaged in the decision and in making the candidate feel seen.

A Balanced Path Forward in the Age of AI

Reading “Job hunting and hiring in the age of AI” made it clear that we’re at a crossroads. The efficiencies of AI in hiring are undeniable – we’re able to process applications at a scale and speed unimaginable a decade ago, and use data to make smarter choices. Yet the challenges – keeping the process fair, transparent, and human-centric – are just as real. As a founder working to integrate AI into hiring, I carry these dual truths with me every day.

The way forward is not to swing to either extreme, but to strike a balance: AI and people working together. AI should handle what it’s good at (volume, speed, objectivity in criteria), and people should handle what we’re good at (empathy, instinct, holistic judgment). When thoughtfully implemented, AI can actually enhance the human side of hiring – by freeing recruiters from drudgery so they have more time to personally connect with candidates, and by surfacing insights that help humans make less biased decisions. It’s encouraging to see that regulators and industry leaders are pushing for AI transparency and audits, because that will keep us vendors honest and ultimately build public trust. I also foresee companies distinguishing themselves to candidates by how they use AI – making it a part of their employer brand (“we use AI to ensure a fair and fast process for you”).

For those of us building these tools, it’s an exciting and humbling time. At RightMatch, we certainly don’t have it all figured out – but we remain committed to continuous learning and improvement, listening to feedback from both hiring teams and applicants. We’ve learned that small tweaks, like adding a short human intro video before an AI interview, can make a big difference in comfort levels. And we’ve learned that AI can uncover great candidates from unconventional backgrounds, but human recruiters need to be open-minded and trust the data. Every success story – like a startup saving dozens of hours by finding their perfect hire through our AI screening – motivates us. Every criticism – like an applicant saying they felt confused by a fully automated email – reminds us there’s always room to do better.

The future of talent acquisition will undoubtedly be shaped by AI to a great extent. But the heart of hiring will always be human. It’s about people making life-changing decisions together – a new job, a new team member. My perspective, as an innovator in this space, is that we must use AI in service of that human connection, not as a substitute for it. Efficiency and fairness are achievable, as we’ve seen, but they must go hand in hand with empathy and transparency.

I’ll end with a question for you, the readers: How would you feel about having an AI play a part in your next job search or hiring decision? Have you experienced it already, and did it make the process better or worse? I invite you to share your thoughts and stories. After all, as we navigate this brave new world of AI-driven hiring, it’s vital that we keep listening to all the humans in the loop – including you.

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The shift away from remote work isn’t just about location; it’s about how companies are rethinking the way they hire and what they value in talent. Remote work expanded the pool of opportunities, but now, as the market tightens, competition for roles—whether remote, hybrid, or in-office—becomes more intense. Employers are becoming more selective, which means you need to be more intentional in how you present yourself.

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