So goes the opening song of the sleep away camp movie “Meatballs” that introduced us to Bill Murray on the silver screen. (Fair warning: it’s a 1970s summer camp movie, so the humor is exactly what you’d expect, and you’ll both laugh and cringe when watching.)

Summer is the best season of the year. Trips to the beach, corn dogs at the county fair, and warm evening backyard barbecues. One of the themes of summer is summer reading. Kids with summer reading lists to tick off, and grown-ups with summer reads recommended by the New York Times packed in their carry-on bags.

Reading a book feels harder than ever for many of us. These books. So many pages. So many words. Screens full of videos, social media feeds, and AI conversations are so much easier to consume.

Which is why we have to force ourselves to read books. Actually, we have to push ourselves to do a lot of things we used to do more of before screens, social media, and AI arrived. As you plan out your summer, consider the list below of things to challenge yourself to do, not just because they’ll help you keep building your human uniqueness, but because they are also enjoyable experiences.

• Read an entire book, one that’s at least 200 pages long. Then, read another one, this time one that’s more than 300 pages in length. Push yourself to read every word on every page, and don’t skip ahead!

• Read three full articles from start to finish within the same week, and make sure one of them is a feature length piece from The New Yorker, The Atlantic, or The Economist. Take a break for a snack and some water if your head starts hurting from all that reading.

• Write a full article or draft a full report for a project you are working on at your job or in school. And yes, that means no use of AI to do it. Okay, if that feels too daunting a task to undertake without AI’s help, try to at least craft a full outline using just your brain and creativity.

• Watch a documentary that’s at least 90 mins long, and that addresses a topic you would normally have no interest in. And as you are watching the documentary, feel what it’s like to learn something new about a subject you never gave a second thought. You’ll be amazed at how excited you’ll be to tell everyone at your next dinner party what you learned.

• Prepare and cook a full three course meal, plus make a dessert, for you and a partner or friend. Find the recipes from an actual cookbook (call your parents to see if you can borrow one of them, and trust me, they’ll have dozens of them). Oh, and you can have your partner or friend help cook the meal, it will be more fun that way.

• Fix something in your house that’s screaming at you to deal with, clean out and organize your garage or whole floor of your house. Personally, I like the “fix something” challenge because I suck at using tools and being a handyman so this one’s a real mind stretcher for me.

• Watch a baseball game live from first pitch to last out. It can be a game at any level from T-ball to the Major Leagues, but whichever game you pick, commit to paying attention to the situations and see if your brain can learn how to anticipate the different things that might happen next. Now, if you choose a T-ball game I do have to warn you — this challenge could last six hours.

This list could go on and on. Go ahead and come up with a couple more things to do that don’t require a screen or an AI tool. Then actually do them. Who knows, you may love this feeling you get when you make the effort to truly think and learn using just your brain.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

There is always something enlightening that comes from traveling, especially when you venture outside the U.S. The genuine kindness and courtesy of the Japanese people resonates like no other country I’ve visited. From city to city it has been like this, and, as a result, it’s made me reflect on how much today’s narrative focuses on the individual.

Idolatry takes center stage these days. Social media algorithms feed us stories that might convince us that individual founders, CEOs, influencers, athletes, and artists are all that matter. As the storyline goes, the individual does it all and deserves all the credit.

But we should know better. We must know there are many individuals in the U.S., including in my homeland of Silicon Valley where the ethos “look at me” rings louder than anywhere else (except perhaps in D.C.), who embrace service, humility, and serendipity.

I encourage leaders to embrace these three traits and express them in their work. I encourage those seeking career roles to evaluate opportunities based on how much these three traits are clearly visible in how leaders lead in target organizations. I encourage you to define these three traits in your own way and to then express them in your work.

In Service of Others

The best leaders work in service of those they lead. They truly are inspired by the success of those they lead over their own success. Beware the CEO who shows up at the board meeting alone, and laud the CEO who brings executive team members to the board meeting so they may present on their functional areas. Beware the functional leader or manager who takes sole credit, and laud those who call out publicly the contributions of team members who make group successes possible.

You can practice being in service of others at any level. Ask teammates in a meeting for their input or ideas. Send an email that highlights the contribution of someone else. Speak in the person of “we” and “us”, and reserve “I” and “me” first-person for those instances where you take accountability when things don’t go as planned.

Moving with Humility

Working in service of others goes hand in hand with possessing a true sense of humility. Caring more about doing the right thing when nobody is looking versus making sure to get credit for what is accomplished is the mark of a humble leader. Beware the CEO who prioritizes their own brand image over the priorities and needs of the company.

The irony about humility is how it’s those very individuals who are hard to recall by name that embody humility the most. They aren’t out beating their chest telling the world how brilliant they are. They haven’t started a podcast and then implored their audience to vote for that same podcast in the latest podcast award balloting. Instead, these are the leaders focused on the never ending list of things that matter more to the organization’s success than trying to make sure people know how important they are.

Recognizing the Role of Serendipity

While service springs from humility, it is humility that keeps us honest about the role serendipity plays in the outcomes we enjoy. Too often, leaders are convinced - and then aim to convince us - that it is their unique set of intellectual gifts that deliver success and personal fortune. True leaders recognize the role that serendipity — the combination of luck, good fortune, and timing — plays in outcomes, and they know when they and their teams are positioned to act on it.

I think of my time at a specific digital media company. It was during the heyday of search engine optimization (SEO) and we happened to be operating in a category that had “news frequency” dynamics so we could avoid Google’s recurring efforts to squash SEO jockeys. We succeeded.

Today we read how Google has fundamentally changed search to now be the front end for Gemini, their own AI beast. As a result, digital publishers are seeing their SEO traffic referrals from Google decline dramatically. This will only continue, and digital publishers will feel more pain. Are today’s content platforms less brilliant than we were many years ago? No, they have merely ended up on the other end of the serendipity spectrum.

What to Look For

I don’t begrudge the self-promoters, or the individuals in leadership positions or positions of influence who seemingly ignore the traits of working in service of others, moving with humility, and recognizing serendipity. I do know there are many individuals who exhibit these traits as leaders, and that they exist in far larger numbers than we see because their contributions are drowned out by the algorithms that amplify the former. And, most important, I believe those who embrace service, humility, and serendipity are the leaders who, alongside their teammates, build enduring products, businesses, and cultures.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

1. Remove all the people working between executive management (i.e. the C-level folks) and individual contributors (ICs)

2. Fill the vacuum created by displacing managers with AI tools, platforms, and agents so the remaining C-level and IC folks all become instantly hyper-productive

3. Iterate on #1 and #2 above to magically scale revenue and operating profits

4. Make all stakeholders insanely wealthy, well, at least those who hold preferred equity and founder common shares

Realities of the Player-Coach

Embedded in the scenario above is the concept that everyone — from the CEO to the most junior-level IC — needs to become a “Player Coach”. Ah, the PC. That unicorn who can simultaneously lead, manage, be strategic, and do all the detailed, daily tasks over and over and over again that ultimately moves a company forward.

Keep in mind, Phil Jackson is arguably one of the top 3 NBA coaches of all time, but he was a pretty average (maybe even below-average) NBA player. Ted Williams may be the greatest hitter in MLB history, and was also one of the worst managers/coaches in MLB history.

The concept of “Player Coach” has been around forever, and it isn’t new to Silicon Valley either. I can’t personally recall a time as a manager or even as a startup CEO that I wasn’t also doing “IC” type work within one or more functional areas. That said, my IC work was always suboptimal compared to our IC experts, but I added it to my to-do list. It was how we got more done and could “play bigger” as a company.

This latest AI-driven push towards the utopia of the player-coach means one thing for sure. The workloads will scale dramatically for the humans drafted into these PC roles. Now I’m biased, of course. I’m a human, but I imagine that the pendulum will swing back to recognize that human managers will outperform a world where organizations subsist on a diet of PCs alone.

The Case for Keeping Human Managers

Why do I think human managers will outperform a world of AI-powered PCs:

• Humans will always have marginally more detailed and nuanced context for understanding an organization’s goals within the broader strategic context of competitive threats, partnerships, and balancing opportunities and risks. So long as we don’t abdicate this advantage to AI, I imagine AI will always be saying “Nice catch!” to us.

• A corollary to the bullet above, humans will excel at planning over long horizons while AI will excel at getting short-term tasks done. Given our incremental context advantage, human managers will be better at looking around corners, asking the “what if” questions, and appropriately challenging the assumptions from the C-suite and the work product of ICs.

• Managers make decisions, over and over. AI builds scenarios and then demurs, always essentially saying “well, you could do this or do that, and here are the pros and cons.” And C-level leaders relying solely on the work product generated via AI by ICs will lack the critical “decision-making” scrutiny professional managers are most skilled to provide.

• Responsibility and accountability go hand in hand, and they are embedded in the fabric of how humans operate in a company. Somewhere along the line “the buck stops here”. Ultimately we can point to the CEO, but there are many decisions that are connected to responsible and accountable operators before the CEO, and these are the managers. When AI makes a mistake or just fails to perform, how will it be held responsible and accountable for its actions?

• The multidimensional ability to guide, coach, support, and conduct difficult conversations with other humans is embedded in the best managers. How will these characteristics evolve in AI managers who today are unidimensional and oftentimes just sycophantic - as well as wrong and incomplete - in their responses. Can we expect AI managers to ever feel any shame in being wrong and take any meaningful stake in being right?

No doubt people are going to be asked to do more and increase their personal span of control. They are going to be asked to carry the entirety of the organization in their head, or at least be able to continually prompt Claude to play it back to them.

This will be cognitively exhausting, and will likely lead to not just human burnout for those tasked with trying to be “a LeBron James” in their organization, but it will also be prone to errors and costly missteps. Maybe these hiccups won’t matter. Maybe the pace of iteration and “fixing bugs” will be so fast with AI that the costs associated with losing the managers will be justified.

I remember when holacracy was all the rage in Silicon Valley. The spectre that companies could remove the hierarchical org chart and replace it with flowing circles of self-governing teams that somehow always knew what to prioritize and how everything aligned with the company-level success metrics. I’m pretty sure holacracy hasn’t become anything close to the norm in Silicon Valley, let alone anywhere else.

Sure, the “what’s different now?” answer may very well be that AI changes the game so dramatically that the technology will fundamentally change how organizations are designed. And yes, again, I’m a biased human making the case for humans, and in particular humans called “managers” (all of whom are already PCs by the way), to continue being relevant and valuable to organization’s design.

So how does this play out? I don’t know. Let’s see how organizational design evolves and how the work humans are doing changes over the next year. As is always the case, time will tell so long as we really pay attention.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

And by the way, who’s the Anthropic AI developer that came up with this as the go-to mea culpa every time Claude directs us down a rabbit hole that compels us to prompt: “No, that didn’t work, look at this screen shot! Do you even know what you’re doing?!”

Let’s face it, while AI opens pathways to do all kinds of stuff that we couldn’t imagine doing, it also sucks us into black holes more often than we’d like to admit. I’m here to admit it.

Allow me to share how I spent a recent weekend. While I grant you that I do indeed need some new hobbies (just don’t tell me golf should be one of them), I do find tinkering with AI to do all the things I could never do on my own kind of like a hobby. Yes, a weird hobby I know.

The saga began Saturday before last, mid-morning. I picked up the old laptop, hopped on the couch, and spun through ESPN+ to find a college baseball game to put on in the background. It was time to start building out the updated version of my digital marketing stack to support my nonprofit MyNextPlay.org.

The plan was pretty simple: export contacts and their accompanying organizations, or “Company Name” (more on this later), from my donor fundraising focused customer relationship management (CRM) tool Givebutter to Hubspot.

Of course, before starting this endeavor I iterated within a Claude project to confirm which new CRM could streamline my nonprofit’s go-to-market efforts. When I followed up in the same project conversation to ask Claude how to go about actually executing this transition, he was so confident. He mapped out a plan in a very specific number of steps, assured me this would all be done in less than an hour, and sent me on my way to start exporting data from Givebutter. (Side note: I’ll be referring to Claude as he/him even though to be honest I’ve never seen Claude post preferred pronouns?)

Let the Gauntlet Begin

A few hours after updating my exported list of contacts from Givebutter, and then reformatting and cleaning the data with the proper field names, I got the first warning sign that trouble was brewing. Yep, the first import to Hubspot resulted in almost double the number of contacts that were in my “master” spreadsheet.

Allow me to say the quiet part out loud – Claude doesn’t know how Hubspot works. Instead of figuring out how to get the single CSV import to deduplicate the “Company” objects correctly, Claude suggested breaking the spreadsheet into two .xlsx files to sequentially import the unique list of Company items first, and then follow that by importing the Contact records.

Did I mention above that I need some new hobbies? Okay, back to the story.

With a new iteration, the dual spreadsheet approach seemed to deal with the duplication issue, but then good old Claude failed to alert me to the other nuance of Hubspot: Company Name isn’t the unique identifier that it had assured me would be the secret “key” to binding data from the two sheets. And to make matters worse, Claude hadn’t recommended turning off the annoying Hubspot feature that looks at contact email addresses and then assigns (often erroneously) company names.

Oh for Christ’s sake, “What is going on here?” I thought to myself. Actually, I think I blurted it out loud, not just because Claude was clearly dragging me into that special world of “AI merry-go-round,” but because it was now almost dark outside. By the way, it doesn’t get dark these days until well past 8 p.m.

For the rest of the evening it was cycle after cycle of me trying something and then uploading a screenshot to inform Claude what I was “actually” seeing, not what it thought I should be seeing. By the time I called it a night, my laptop’s desktop screen was so littered with screenshots I could no longer see even one pixel of the lovely wallpaper image Apple added during my last operating system update.

Sunday Morning Solution

During my morning walk Sunday I couldn’t stop processing what to do next in my dance with Claude and Hubspot. I think we ended Saturday night with an agreement that we’d go back to trying the single CSV upload option using something called multiple objects importing, an idea that actually came about when I ran out of tokens with Claude and decided to ask Gemini what the heck to do.

While on my stroll through the Golden Gate Park panhandle, I was asking Claude (via the mobile app) for an updated plan that would ensure this multiple object import strategy would work. I hustled back to my home office, clearly waving the “I have no life” white flag as I fired up my laptop and started back in on “Project Hubspot”. Two hours later came the best moment of the weekend: Claude recommended that I go with Brevo, not Hubspot.

“Nope!” I told Claude that was an absolutely unacceptable option, and proceeded to furiously type something along the lines of “You’re wrong and you gotta figure this out,” and that’s when I got the infamous “You’re right, that’s on me” response from him.

Finally, after another hour we got to the bottom of things. Just to bore you some more about Hubspot, apparently “Company Name” can’t be assigned to contacts without a unique Company Domain Name identifier, even if the domain URL is fake and doesn’t actually go to a live website. And, as it turns out, for many organizations in my contacts you can’t use their actual URL because Hubspot doesn’t accept the format of their website URL.

Again, for Christ’s sake. It’s 2026 and these cloud-based super SaaS software platforms still can’t figure this stuff out? No wonder the SaaS apocalypse is upon us. I’ll spare you the rest of this Claude vs. Hubspot drama and tell you that after some finessing with dummy URLs I got what appears to be all the unique contacts imported, with the right number of unique companies, and properly showing the Company Name for each contact (even if the domain name for a large chunk of contacts isn’t real).

Learnings?

What did I actually take away from spending basically an entire weekend trying to get AI (Claude) to help me set up a supposedly important software platform (Hubspot)?

• Don’t ever spend your weekend on an inane task like this. Even playing golf would be less painful.

• Assume that the AI doesn’t really know how to do the very specific things you need it to do, and as a corollary, don’t expect that you’re going to be able to craft prompt instructions so detailed that the AI will get your task completed correctly.

• Expect errors, frustration, and needing to re-do things over and over.

• Don’t get too frustrated, don’t give up, and don’t let the AI give up on finding a solution.

• Find a consultant on Upwork, Fiverr, or through your network to help you actually do the task the right way, for a fraction of the time. Whatever you pay them is more than worth it.

I will say, despite spending way more time than I should have, I got the project completed (sort of), figured out a fair amount about how to navigate Hubspot, and learned even more about how to navigate the nuanced dynamics of how to utilize AI to get more done than I realistically could have without it.

But still, I need some new hobbies. Any ideas for me?

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Here’s an overly simplified framework that lands somewhere in the middle — but one worth taking seriously in a world where the aggressive displacement scenario actually plays out.

The Human to Human Economy

Even if tens of millions of jobs get handed over to AI platforms and robots, there’s still a massive job creation opportunity hiding in plain sight: humans helping other humans. Three areas can become the foundation of this “human to human” economy.

• Education. More teachers, coaches, and instructors, all focused on helping people learn continuously from pre-K through their entire working lives. Not the current model with its cost barriers and credentialing red tape. A version where educators are compensated well enough to do their best work without stressing about rent. Make learning a public good so it’s always there to help them retool and pivot into their next human-focused career.

• Health and Wellness. More doctors, nurse practitioners, nutritionists, child care workers, and fitness professionals — all focused on helping people live healthier lives across every stage. Not the current model where quality health and wellness support is a privilege of income. A version where pathways into these careers are open and well-compensated, and where the services themselves are accessible to everyone who needs them. Make health and wellness a core quality of life layer, not a luxury tier reserved for people who can afford it.

• Guidance. This one matters most to me. People working with people as therapists, advisors, mentors, and counselors, and being available when a fellow human needs another human instead of an AI agent or chatbot. This may be our real litmus test for how serious we are about a human future. The day we hand guidance fully over to AI is arguably the day we’ve handed everything over to this technology.

These three career pathways aren’t exhaustive. Restaurant workers who create real community dining experiences, construction and trade workers who build and maintain our physical world, and likely many other “human to human” purpose-driven roles, should be viewed as critical to enabling the human to human economy.

Funding is the Hard Part

And this is where the optimistic framework typically falls apart. Capitalism keeps score with one metric: earnings. There is massive incentive for any business owner to push expenses toward zero, and assuming AI only accelerates that, don’t expect capital allocators to voluntarily redirect their expanding returns. Billionaires, millionaires, and corporations aren’t going to fund politicians who raise their taxes in the name of spreading AI-generated wealth.

Universal Basic Income (UBI) alone doesn’t solve the problem either. Handing people a check without rebuilding the connective tissue of meaningful human work will fall short too.

What’s actually required is a policy commitment, like an “AI dividend” formula that fairly compensates investors on a risk-adjusted basis while directing a meaningful public ownership share toward education, health care, and guidance. And not just from AI companies but also from the thousands of businesses whose profit margins will swell because of AI. The incremental global income generated by these technologies has to flow in a meaningful way back to all humans. Right now it flows entirely to capital allocators. That’s a choice, and it’s a choice that can be modified with political will.

The doomer scenario gets more attention today because it’s scary, and scary writes better click-bait headlines. But the human-to-human economy is a real structural alternative that federal and state governments, and companies of all sizes, need to start building right now. Without doing so, we’ll idly watch capitalism’s incentives widen the global wealth and quality of life gaps the doomers predict. The future most people hoped to avoid becomes the one we built by default.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Drawing on three years of interviews and workshops with more than fifty companies across healthcare, insurance, finance, manufacturing, and retail, the MIT researchers found that organizations are today pointing AI at three common problems:

• Eliminating the bottlenecks that prevent workers from focusing on more valuable work

• Enabling individuals to synthesize expertise that previously required a whole team to complete

• Helping newer employees more rapidly scale learning curves so they can contribute at more strategic levels

Further, MIT’s study surfaced six lessons for capturing AI’s productivity benefits without eroding the skills and judgment teams need to run the business: (1) minimize daily drudgery, (2) promote learning, (3) preserve teamwork, (4) prioritize good interface design, (5) encourage domain expertise, and (6) build in processes for accountability.

Why This Matters

The MIT findings give CEOs something the cognitive surrender conversation didn’t. Consider it a potential guide for how to deploy AI across every team member in your organization.

Most startup CEOs are focused on evaluating AI deployment investments based on productivity results, doing more for less expense. However, AI should be viewed as long-term talent development commitments that may take a bit more time before they noticeably move the needle on the OKRs and KPIs that matter most. The MIT findings surface a practical framework that gives CEOs something the cognitive surrender conversation didn’t: a potential guide for how to deploy AI across every team member in your organization.

Here are three ways to ensure AI empowers your people to do their best work:

1. Audit how your team’s time is actually being spent before you deploy the next AI tool. The clearest win MIT identifies is using AI to eliminate the tasks that drain productivity and therefore take away valuable time that can otherwise be spent building knowledge and expertise. A customer success team spending four hours weekly documenting support ticket summaries is an example. An AI workflow should manage that task so the four hours get reallocated to support team members finding patterns in customer feedback that can be turned into new retention strategies and tactics. It’s the same support team, but now they’re engaged in applying meaningful learning.

2. Encourage your team to use AI to build knowledge and develop expertise, not just produce output. AI can accelerate learning, but only when it’s consistently used in that manner. A marketing leader who uses AI to generate and ship a campaign brief is not moving up the learning curve in the same way as the marketer who uses AI to stress-test their assumptions, challenge their strategic thinking, and surface dynamics they hadn’t considered. As CEO, you carry a crucial responsibility here to ensure your team members can identify critical aspects of your industry and they fully understand your organization’s strategic priorities. They need this strategic scaffolding to retain and apply what AI helps them learn.

3. Create human interactions where your team can demonstrate what they actually know. In my experience leading organizations, the sharpest indicator that separates a senior versus junior leader isn’t the quality of their presentation, it’s whether they can field challenging questions about their work product without a screen in front of them or access to an LLM in real time. Build these “human” moments intentionally. Weekly working sessions where leaders present strategic work and answer questions without slides or AI assist are still the best forums for finding out what your team actually knows.

What Should You Do Next?

This week, ask each member of your executive team to identify one recurring task AI could take off their team’s plate. Then ask your e-team to answer a second question in writing: “What will your team do with that time?” Specifically, what learning or domain-building activity replaces the mundane task work?

From there, assign one team member to lead a “lunch and learn” session about a topic they researched using that newly found time. Share the research results prior to the meeting so the session can be run as a group discussion that makes Q&A the focus. The human conversation that ensues, building from what AI helped frame, is where durable learning may actually occur.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Spring is a time I find myself observing how so many elements of baseball translate to “real life”, and how true this is no matter the competition level. While the speed and polish improve from level to level, so many of the truisms remain consistent from how the game evolves from youth to high school to college and to ultimately the professional level.

Every sport provides learnings that apply to one’s worklife, but there’s something about the game of baseball that I find particularly relevant to what it takes to navigate the ambiguity and challenges of working in a company, especially an early stage company. Specifically, there are four characteristics inherent in baseball that I see embedded in what also matters for people working in a startup organization either as the CEO, a people manager, or an individual contributor.

Failure and Resilience

There’s a lot of failure in baseball. The best hitters succeed well below 40% of the time and the best pitchers can all recite countless outings over their career in which they got “lit up” by opposing hitters. Likewise, there’s a lot of failure in startups. Most of these companies fail, and the failure rate of startups is even lower than the batting average of the weakest MLB hitters.

If you can’t handle failing and aren’t willing (or able) to learn how to be resilient, then don’t play baseball or start/join a startup. In baseball, the line is “flush it”, a refrain used to remind the hitter to forget taking that called third strike so they can immediately refocus their attention on playing great defense and can restart preparing mentally for their next at-bat.

The same applies to what you do in a startup. Forget that customer product feature that bombed or that sales pitch that resulted in a “no thanks”. Flush it. Figure out what you’re building and shipping next, or shift your focus to how you’ll tweak that next sales presentation.

Repetition and Iteration

Baseball is a game of repetitions or “reps”. You can’t master the game just because you can run fast, jump high, or have amazing strength – just ask Michael Jordan. It’s a game that requires hitters take thousands of swings at thousands of pitches that move and dart in different directions and varying speeds. It’s a game that requires pitchers to throw thousands of pitches that they are endlessly trying to command moving in multiple directions and speeds.

Startup companies are also built on “reps”. Ask any startup founder or CEO how many times they have to iterate on their fundraising pitch. Most of these CEOs will tell you stories of the thousands of versions of their presentation they had to create over the course of their fundraising efforts, and the hundreds of meetings in which they presented those pitches to investors.

With each rep those CEOs got a little better; they told their company’s story a little more clearly and with increasing conviction and confidence. But they never stopped getting reps, they never stopped iterating, and neither does anyone in a startup no matter what your role or level might be. Like baseball, you’re constantly trying to perfect the imperfectable and, just when you think you’ve got it all figured out, you get smacked down. The pitch falls flat, the marketing channel doesn’t react as hoped, or the hiring strategy yields lackluster results. At that moment you know it’s time to tap into your resilience and get in some more reps.

Playing Every Out

Baseball has no clock (well, the pitch clock doesn’t really count). Both teams get the same number of outs and you learn pretty quickly that no matter the score or situation, neither team can hold the ball and run out the clock.

This is exactly why maintaining resilience and getting in your reps are so crucial. As Yogi Berra said so well: “It ain’t over until it’s over.”

This ethos applies so beautifully to life in a startup. While venture capitalists like to ask “what’s your burn, how much runway do you have?”, this doesn’t ever mean as a startup CEO you are on a defined clock. With any amount of capital raised, as a startup CEO you and your team have a certain number of outs you can play. You need to play every one of them because it may be the bottom of the ninth when you get the breakthrough you need, like figuring out product market fit or getting that first customer closed.

Unlike a baseball game that eventually ends once the losing team has used up their 27 outs, a startup has levers to create more outs it can play. Whether that’s raising more capital, slowing down the pace of investment, or both, your company can continually adjust the length of the game so long as you stay mindful that there’s no clock defining when the game’s over and that every “out” matters.

Individual and Team

Being resilient, focusing on getting reps, and playing every out are all elements deeply inherent in both baseball and startups, but my favorite dynamic by far is how both experiences combine the individual and team.

In so many ways baseball is the most individualistic team sport out there. The pitcher on the mound, the hitter in the batter’s box, and the fielder on the diamond are all alone, individual in their respective pursuit of getting the hitter out, getting on base, or successfully fielding the ball hit to them. At the same time, there are ten players (nine position players and a designated hitter) in the lineup at any time and it takes their collective team effort to score as many runs as possible when at bat while concurrently yielding as few runs as possible when pitching and playing defense.

The startup company is no different. Everyone plays a position in the field and hits in the lineup. The metaphor can be extended however you want. Maybe the CEO is the pitcher, maybe she’s the manager, or maybe the catcher, shortstop, and centerfielder represent the head of product, go-to-market and finance respectively. However you slice it, the theme is the same. Every functional role is critical in the company, and every individual has to make plays on defense and do their best job possible when it’s their turn to hit.

And yet, the team doesn’t succeed because of a single player’s performance, nor does it fail because of a single player’s error or strikeout. A team can – and often does – lose because the commitment to win isn’t held by every player, the individual effort isn’t contributed by every player, and the culture of “picking a teammate up” doesn’t exist within the team.

It’s this last point that brings it all together. Because the experience of playing baseball and working in a startup company are so challenging and full of failure on the individual level, the team ethos of supporting every player when they struggle can play an outsized role in how a baseball team or company navigates tough times. It’s the combination of CEOs that lead in this way, managers who manage in this way, and individual contributors who step beyond their individual efforts in this way that powerfully transforms a game of individual contributions into the result we all remember most – team success.

And perhaps no open source digital platform has helped as many businesses – through the power of Search Engine Optimization (SEO) – organically grow as Google Search has over the last 25 years.

So it makes perfect sense that AI optimization (AIO) is the next growth opportunity for every business big and small. Indeed, it feels as though for many businesses the race is on to create and push as much of their information into the frontier AI platforms in hopes that links to their brand appear in billions of AI conversations. And if this works, these businesses believe, they’ll reap the rewards from this new open source customer acquisition channel called AI.

I’m not so sure history will repeat itself here.

Google Search and the Open Web

Using content publishers and brand marketers as examples, the overly simplified evolution of Google Search goes something like this:

• In the early years publishers and brands could invest resources to reverse engineer how Google Search worked, learn how queries could be relevant to their business, and, as a result, generate organic traffic, audience, and potential customers. This is the general concept behind SEO.

• During this same period, Google invested billions to optimize its role as the “middleman” between publishers and brands, and their target audience and customers. This is the overly simplified description of how search engine marketing (SEM) evolved.

• Google’s financial aspirations pushed the company to make it harder for businesses to consistently succeed at the SEO game. Most notably the moving target for SEO success has been Google’s persistent schedule of “search update releases” designed to curb the volume of very low cost audience and customer acquisition traffic publishers and brands could garner. Concurrent with the narrowing of the SEO opportunity, more of the consumer search results experience has become filled with SEM-driven paid marketing placements.

Despite this not-so-subtle shift from SEO to SEM, you can still argue that Google Search remained an open, non-walled-garden environment. Why is this? Well, think of Google Search as a 2-dimensional (2-D) platform.

Search is 2-D because it’s presented in a single user interface, or doorway, that includes a single screen measured by your device’s height and width, and when the user clicks on a search result they are routed to a third dimension that exists beyond Google Search – whether the user gets there via an SEO or SEM link.

So based on the way history has played out in search, you can’t fault publishers and brands from looking at AI platforms as the next big traffic generating “open source” opportunity. But they shouldn’t.

AI Conversations and the New Walled Gardens

At first glance the parallel looks enticing. If you’re a publisher or brand, all your amazing content and intellectual property should be accessible by ChatGPT, Claude, or Gemini, and when it’s relevant to serve up your content in an AI conversation – boom! – users will find it, and happily click links to find more at your website. Not this time.

AI platforms like OpenAI, Anthropic, and Google are actually 3-dimensional, “3-D” versus 2-D like Google Search. What does 3-D mean here? Think of your prompt like a search query, and you enter it into a screen with height and width (2-D), but instead of getting a result that clicks you to a 3rd party for the third dimension, you enter the third dimension right there in the AI’s response.

And the loop continues inside the AI platforms, with each subsequent prompt, response, and ensuing conversation. Heck, you may never leave, get everything you need and never visit any of the sources ChatGPT, Claude, or Gemini used to help answer your questions. Welcome back to the AI walled garden.

What’s a Business to Do?

If you are a publisher, content creator, or IP generator that connects potential customers to a digital service you hope to get paid for, expect AI “walled gardens” to find this information and republish it in conversations - maybe with attribution and a link to your business - but likely not. Until we see how the copyright lawsuits that publishers like the New York Times have brought to the likes of OpenAI and Anthropic, it’s safe to assume your IP will stay in the AI ecosystem where the vast proportion of value will accrue to the AI platforms.

If you are a brand selling a product or service that can’t be fulfilled within the AI conversation itself, you’ll see the gap between AI “optimization” (AIO) and AI “marketing” (AIM) collapse way faster than the gap did between SEO and SEM. Google’s aggressive effort to inject their AI platform Gemini into the “search UI” is a clear sign they are replacing the 2-D model with a more lucrative 3-D model where Gemini subscriptions cover experiences you don’t need to leave Gemini to consume, and brands will continue to pay Google for customer acquisition through AIM instead of SEM. At the same time, OpenAI’s public admission that “ads” will be coming to ChatGPT clearly implies they’ll be working on the same calculus, trying to optimize the subscription revenue users pay them and the AIM revenue 3rd party businesses pay them.

Ultimately for CEOs and business leaders, the important question is determining how quickly AI platforms can disintermediate what you do, particularly if the IP that makes your business valuable becomes fully embedded within these AI environments. For many businesses, as counterintuitive as it sounds, the answer may be going back to building and maintaining their own “walled gardens” in an effort to directly acquire and engage your target customers. And even if that’s the answer for your business, be prepared to migrate a major portion of your marketing spend to AIM in order to grow and maintain your own healthy walled garden.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Within this trend, Klein draws a distinction between “cognitive offloading” — handing discrete tasks to AI so you can focus on more important work — and “cognitive surrender”, which is what happens when you stop thinking deeply and instead “surrender” the hard part of work to your AI companion as well. Klein’s punchline is simple enough. Struggling through a hard problem is exactly what deepens human thinking and, in turn, builds expertise that can be applied when needed in the future.

Though Klein maps the concept of AI-fostered “cognitive surrender” to his own journey as a writer, it can easily be applied to the workplace where it might impact your role as a leader. When your team members use AI to skip the struggle, the near term output can be done quickly and, at first glance, looks good enough. A strategy deck gets produced, a financial model gets built, and a marketing plan lands in your inbox. But long-term you get the same results as when your body scarfs down a bunch of empty calories. Yep, you get a short-term high that comes from getting the work done so quickly, but what isn’t happening is the development of real expertise and judgement. Your team members are surrendering those important benefits to the LLM not just today, but also in the future.

And if you poke at those AI-generated results a few times you’ll fully recognize they’ve been crafted without the durable foundation needed to benefit your organization long-term. Examples you might come across include:

• the customer acquisition strategy presentation AI created in minutes, but assumptions behind the plan can’t be defended by the marketing director

• the product roadmap plan that AI put together but the customer problems being prioritized to solve can’t be clearly articulated by the product manager

• the financial model whose revenue build came right from AI but the questions asked by board members can’t be adequately responded to by the finance team leader

The output is there, but the depth of expert understanding isn’t.

So as a CEO, functional department leader, or someone working anywhere in an organization, what should you protect when it comes to owning your cognitive development? Start with these five domains where doing the hard work will yield durable human expertise versus surrendering it to AI:

• Setting goals and defining OKRs that define how to drive your organization forward

• Outlining and fully understanding the core elements of your product, customer acquisition, and overall business strategy

• Learning how to recognize when a strategy is being executed well, and when it isn’t, so course corrections and “reforecasts” can be developed in real time

• Developing partnerships with other humans and learning how to negotiate those relationships to support the operating goals for your company

• Calculating return on both people and technology investments, and being able to defend those calculations to your executive team and board of directors

These are not tasks to delegate to an AI agent. They require a deep understanding of how your business works and the strategy you’re pursuing, and how to apply that understanding in ways that enable your organization to be successful. Truly developing the skills above (and others like them) defines whether your team is actually learning and can continue to adapt, or whether they’re just learning to prompt this competitive advantage away in conversations with AI.

Starting this week, pick one meeting where your team presents strategic work and ask the presenters to walk you through their thinking, not just their output. Push on the assumptions. See who can go deeper with you as you ask more specific questions. Use this session to shine a light on examples where some “cognitive surrender” may be creeping into your organization, and to also highlight examples where “doing the hard work of thinking” shows up so your team can clearly see the important differences between the two.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

In case you missed it, Auriemma had a not-so-great “stay composed” performance at the end of his team’s NCAA Final Four semi-final loss to South Carolina. With the clock showing 0.1 seconds left the long-time Connecticut coach walked to mid-court and instead of gracefully congratulating South Carolina’s head coach Dawn Staley, Geno used the moment to turn into a grumpy sore loser, and worse, he then walked out on his team and ambled to the locker room, a long way from the reputation his decades of coaching success has built.

Perhaps it’s time for Coach Auriemma to hang up the whistle. Let’s add up all the reasons why Saturday’s incident means maybe it’s time for Geno to call it a career:

• Rather than congratulate the opposing coach, he argued with her that he had supposedly been slighted before the game started because she didn’t come to shake his hand at mid-court.

• He decided to act this way towards (1) a younger coach who happens to be (2) a woman and (3) a Black person. Would Geno have pulled this move if it were University of Texas women’s head coach Vic Schaefer on the other bench?

• He whined after the game that the mid-court hand shake is some time honored, never to be “not done” tradition. Give me a break, I’m willing to bet the guy has blown off plenty of other coaches when it comes to time honored traditions.

• He complained about the opposing coach during an in-game interview with ESPN’s sideline reporter — what a petty, bush league move from a guy who complains as much as any coach in the NCAA Tournament.

• He walked off after his yelling match with Staley leaving his team behind. He walked to the locker room alone, leaving his team behind. Leaving his team behind. Let that one sink in a bit more.

• He offered up a lame, formulaic apology aimed at South Carolina and the general coaching staff, and couldn't bring himself to mention Staley by name and apologize to his direct counterpart from the game. Reports during Sunday's final indicated he separately reached out to Staley, though as of Sunday evening Staley confirmed she hadn't heard from him.

• Listen I get that Auriemma’s won a ton of games, and a boatload of championships. Good for him. He’s recruited a lot of talent and many of them have moved on to do great things in basketball and beyond. Good for him. By elevating UConn women’s basketball to such lofty heights, he’s arguably done as much as anyone to raise the visibility of women’s basketball. Good for him.

But when you see how someone behaves when their undefeated season and 50+ game win streak comes to an end, and how he not only treats the opposing coach and also abandons his own players after a crushing loss, it makes you wonder if he’s in service of his players, his university, and his sport — or just himself?

Should this incident be enough for Coach Auriemma to hang ‘em up? I don’t know, that’s up to Geno and the UConn administration. At the very least hopefully there’s a sincere reflection by the coach, a sincere apology made to coach Staley, and a closed door meeting apologizing to his team for walking out on them in Phoenix.

The reality today is that it’s easier for companies to measure their AI ROI on the cost (COGS and OpEx) side of the P&L. In fact, many organizations have rolled out AI-based productivity tools, automated a few workflows, and have quietly (or not so quietly) noted that the same work is getting done with fewer people hours. That’s material learning for sure, but if that’s the whole story, you’re playing defense instead of offense with one of the most powerful tools your business has ever had access to.

CEOs should be prioritizing ways they can spur revenue growth within their AI ROI formula. Cost savings matter, but they’re the second-order benefit, and without top-line growth you’re just relying on AI to find expense reductions. And that approach basically becomes a race to the bottom.

So what can the revenue-first approach look like?

Start with what your customers are telling you they need, and ultimately what they’re most likely willing to pay you for. AI can synthesize customer feedback, support ticket insights, sales call patterns, and churn signals at a speed and scale that outpaces what teams can do on their own. The output of this investment in customer insights isn’t just a more robust dashboard, it’s a sharper view of the specific problems worth building solutions for, including the ones your customers haven’t figured out how to even fully describe to you yet. Companies that use AI here are surfacing product opportunities in weeks that used to take quarters to identify and act on.

Next, use these customer signals to prioritize and accelerate what you build. Once you’ve identified the problems worth solving, AI dramatically compresses the cycle from insight to working prototype and testable solutions. The question your product and engineering leaders should be answering isn’t “what can we build with AI?”, but they should be asking “are we building something a customer would pay more for, or pay us for the first time?” With this question at the center of their AI strategy, Intercom rebuilt their core product around an AI agent called Fin and the resultant revenue growth tells a compelling AI ROI story.

The third step is about using AI to accelerate the iterations between your product development and go-to-market motions. Get your portfolio of product updates in front of customers before they’re fully baked. Rapid prototyping with AI means your sales and marketing teams can put a working solution “concept” in your customers’ hands and work through the “last mile” tweaks with current or prospective customers. Whether you use dedicated forward deployed engineers (FDEs) or pull your best customer-facing team members into these customer build efforts, the test is the same: does the customer say “I’d pay for that” or at least guide the process to ultimately making that commitment?

Finally, imagine how your AI investments can be expanded to determine whether there are new customers who have the same problem. The same process that helps you deepen relationships with existing customers can help you identify adjacent market segments, model new buyer personas, and run faster go-to-market experiments against them. With a revenue-centered AI investment focus your organization has the potential to widen its addressable market, thus creating another path towards revenue growth.

Once your team has wired your AI investments to drive revenue growth, the cost savings opportunity then becomes more aligned with how your company is scaling enterprise value. AI-driven margin expansion comes from enabling your team (even while it might grow in number) to serve customers more productively, while also reducing what you spend on third-party software and tools. To create enduring and compounding value, sequence your organization’s focus on how AI boosts revenue growth first, and then margin expansion as a fast follow.

In the coming weeks, bring your leadership team two questions. Start with: “Where in our business are we actually using AI to grow revenue beyond our current operating plan targets?” Then follow up with the more challenging one: “For the dollars we’re spending on AI tools and services, what percentage of that investment can we connect to a specific revenue line?”

The answers will tell you everything about whether you have an AI strategy that’s driving future growth or merely an AI story built on “activity” but not lasting value creation.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Research backs this up. Students who complete at least one internship before graduation significantly reduce their risk of ending up underemployed after graduation. And yet, for too many students and recent grads navigating the job market, real work experience remains an item buried way down their list of priorities. It’s something they’ll get to next semester, next summer, or once the right opportunity magically comes along.

Unfortunately this is a trap many fall into, thinking there’s nothing they can do during college (or even after they graduate) to improve their odds of finding a fulfilling opportunity early on. But there are several ways to add relevant work experience to your resume that, in turn, can stack the deck more in your favor when you’re trying to land a job doing something you really want to be doing.

Your Degree Isn’t Enough

We’ve all read how challenging the job market is right now for new college graduates. Employers are more cautious about hiring, entry-level roles are increasingly competitive, and a diploma alone doesn’t automatically wave you into the job market like it used to.

What employers are increasingly hiring for is demonstrated ability. Not what you studied, but what you’ve actually done. Can you show up, do the work, take both initiative and direction, and deliver something useful? Those questions don’t get answered by a transcript. They get answered by work experience. This is true whether you’re a junior deciding how to spend your summer, a senior heading into recruiting season, or a recent grad wondering why a solid resume isn’t getting traction.

What You Actually Learn Outside of Class

A good internship teaches you things no course can deliver:

• You learn how work actually works. Organizations have a pace, a set of priorities, and an internal dynamic that you need to figure out how to navigate. You start to understand how decisions get made and what getting things done actually looks like in practice, particularly given today’s workplace environment.

• You learn how to put your initiative and work ethic on display. Frankly, a project you complete and can speak to specifically in an interview is worth more than a top-tier GPA. Real work experience derisks the hiring decisions managers need to make because they see that you’ve “done it” before and they don’t have to roll the dice that you’re going to be productive right away.

• You learn, perhaps most importantly, how to fail, bounce back, and strengthen your resiliency muscles. Getting critical feedback on a project, pivoting when something doesn’t work out like you expected, and being able to recover from a mistake when the stakes are relatively low, collectively builds the kind of resilience that employers expect in their team members.

“Free” Might Be Your Best First Move

The traditional pipeline for competitive internships doesn’t favor everyone equally. Those brand-name summer programs tend to run through a small set of well-resourced schools. If you’re not at one of those schools, or you missed the recruiting window, the front door to the perfect opportunity can feel closed to you.

So find a side door.

Sometimes the smartest move is to offer your time and work for free to get inside an organization or industry you want to be part of. This isn’t about undervaluing yourself. It’s about making a strategic investment in access when the traditional path isn’t readily available.

I did this during grad school when I was trying to find my way into sports business organizations, first by networking my way (through friends who helped make the connections) to an internship that paid barely enough to cover New York City rent, but that ultimately helped me get inside the world of sports and meet people who could help me network with other people in the industry. One of the connections I was fortunate to make during that summer in NYC led to project work for a major consumer footwear and apparel brand during my second year of grad school, and that “free” project turned into the full-time consulting job I started with that same company right after I graduated from my master’s program.

I know for sure the journey I was able to take in my career would have been much different if it hadn’t been for the personal investment I made to land those first two work experiences. And without a doubt those experiences paid me way more in terms of what they taught me than the money they put (or didn’t put) in my pocket.

Stop Waiting and Get Started Somewhere

Before this semester ends, identify one project you can take on, either with a research lab on campus or a company that could take you up on the offer to help them out. It doesn’t need to be at a recognizable name-brand company. It doesn’t need to pay. It just needs to be a real project tackling a real problem. It needs to end with a real deliverable, and be done in collaboration with real people who will give you feedback that you can learn from.

Platforms like Riipen connect students with real employer projects that are short-form, remote-friendly, and accessible regardless of where you go to school. Local nonprofits and small businesses are other great options to consider. The opportunities are out there, so make it a top priority now to find a project that puts real work experience on your resume and that bolsters your confidence with real-world learnings.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

But my take is more stark. Don’t hold your breath waiting for that grand bargain to cover you if (or when) AI replaces your job.

Neither the Government or Higher Ed is Coming Anytime Soon

Politicians are the last to understand the impacts technology imparts on the economy’s key stakeholders, and when they begin to figure it out, they move too slowly. Higher education moves even slower, wedded to the model that’s been in place for centuries. And while companies will certainly be the first to discover which AI tools are replacing roles in their organizations, they’re not exactly rushing to publish a public skills roadmap for you to follow. That’s not cynicism — it’s just the reality of how institutions operate when change moves this fast and competitive advantages are precious.

The workers who got caught in the last big waves of automation — manufacturing in the ‘80s and ‘90s — largely had to figure things out on their own, too. The difference now is that the tools to adapt are actually sitting right in front of you, and they’re more accessible than ever.

What “Working With AI” Actually Looks Like

The phrase “AI will change every industry” is true but not very actionable. What’s more useful is getting specific about your industry and your role. If you haven’t already, sign up for the pro version of Anthropic or Google’s Gemini AI platforms, and just start using them in a way that augments the workflows and tools that are central to how you work today.

Here are a few examples of what this might look like depending on where you work in your organization today, no matter what level you’re at:

• If you’re in a product or technical role, imagine building a lightweight workflow where AI helps define requirements, generates a first draft of code, flags issues or bugs, and does all of this before a human engineer reviews and approves anything that goes to production. You’re not replacing yourself as a product manager, designer, or engineer; you’re utilizing AI to accelerate what your product development organization can build and ship for customers.

• If you’re in sales or marketing, think about using AI to identify and score potential leads, then auto-generate a personalized outreach sequence that enables your team to spend less time on cold list-building and more time on the conversations that actually close business.

• If you work in finance or accounting, consider how you might use AI to connect daily transaction activity to your financial reporting systems in real-time, so instead of waiting for a monthly close, your team always has a current view of your business metrics and the information needed to make decisions at any time.

These examples can be executed by anyone right now. They’re workflows people are building and implementing in their roles today, and they’re doing so with AI tools that cost less per month than a gym membership.

The Most Important Investment You Can Make Right Now

If there’s one tangible step to take this week, it’s this: get access to a capable AI platform and start using it on real work problems. Not to just generate an email or a fun video. Apply it to something you actually do every day and see how it makes you more productive.

You’ll make mistakes and so will the AI, and the two of you will go in circles a bit. That’s the point. Learning how these tools work and where they fall short is a skill in itself – and it’s one that will compound over time whether you’re a college student, a mid-career professional, or a first-time manager.

Hopefully government and higher education leaders will eventually catch up. But don’t wait for that to happen. Those who dive in now to leverage AI in their work will be the ones leading through the upcoming waves — not the ones flailing to stay ahead of them.

So what’s one task in your current role you could hand off to AI this week?

Read the full NYT Opinion piece by Gina Raimondo here.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Relax. Let me give you the TL;DR on the latest AI doomer hits making the rounds: agentic AI drives the marginal cost of labor toward zero, frontier platforms deploy all the agents, rebuild every existing company in the process, and ultimately capture 99% of the wealth. And what does that mean for the rest of us? Well, that part hasn’t been explained particularly well. You’d be forgiven for thinking the phrase of the year so far is “we’re all f***ed.”

It’s not just vibes. A widely-circulated post from Citrini Research sketched out a pretty bleak two-year horizon for the economy. Block’s CEO recently announced he was laying off nearly half the company because AI can efficiently replace so much human labor, and he said he felt late doing it. And finally, every software platform that’s embedded itself inside Fortune 500 companies suddenly finds itself needing to explain why it won’t just be replaced by vibe coders inside those same companies. No doubt about it, the doom is arriving in ear piercing shock waves. And it’s going to get louder.

No wonder it’s never felt harder to be a CEO.

If you’re a CEO right now, you likely feel the walls closing in on you: on one side, your team is quietly (and sometimes not so quietly) wondering whether their jobs will exist in six months. On the other side, your board (mostly capital allocators) wants to know why you’re not doing more with less. Good luck explaining a hiring plan for the back half of 2026.

During World War II, Britain’s populace embraced the phrase “Keep Calm and Carry On” to boost morale in their darkest hours. CEOs need their own morale-boosting phrase right now. I’d suggest: Be Positive and Stay Optimistic.

But before we get to “Be Postive and Stay Optimistic”, let me share a few other “Be” traits CEOs might consider embracing in the weeks, months, and years ahead to help them navigate their unenviable job.

Be Contemplative

Carve out dedicated time every week to process what’s happening and sketch out strategic pathways that your company or organization might consider. Too often in times like this, you can get lost trying to just see your next footstep in the fog. But you have to find a way to get above the clouds and chart a future that may require some dramatic changes. Build this contemplation into your executive team meetings, too. Think in 180-day increments and keep iterating. It’s the only way to navigate when the rate of change and dislocation feel so accelerated.

Be Focused

Come back to the vision, mission, and values that define your company. Clarity about what you deliver for customers is your best north star as AI reshapes everything around what you and your team are trying to accomplish. More than ever, now is the time to perhaps do less, but do it exceptionally well.

Be Vulnerable

What’s happening right now isn’t like the invention of electricity or the fourth industrial revolution. It’s not like anything history can aptly be applied to as a comparable. We are watching the proliferation of intelligence capabilities for which nobody has figured out the ramifications, no matter how eloquently they craft a science fiction description of what 2028 will look like. Practically speaking, it’s okay to say that out loud to your team, and to acknowledge that your organization is still figuring out how human intelligence and AI intelligence will co-exist. And it’s okay to acknowledge that your organization may not grow headcount the way it once did, or may even shrink. They already know this potential outcome is on the table.

Be Accessible

Your people have questions about what AI means for them and for the organization. Show up for those conversations. The void created by silence from leadership is filled in immediately with buckets of anxiety poured in by your team. Each conversation you comes with a multiplier effect given teammates talk and compare notes with each other. So speak with each individual in a way that acknowledges their personal perspective, but that is also consistent as if you were speaking to the entire organization at an all hands meeting.

Be Decisive

Even in uncertainty, execution matters, and making decisions that need to be made will separate the organizations that move forward from those that become laggards or roadkill. Once you’ve built your plan, move on it. But be excellent at identifying the successes (and the failures) that result from your decisions so you and your team can course correct and implement the important decisions that follow. Yes, people prefer stasis and they hate ‘context switching’, but unfortunately those concepts largely disappeared as of November 2022.

And finally, and most importantly, as a CEO today:

Be Positive and Stay Optimistic

I get it, optimism feels really hard to embrace when you’re walking through quicksand with a blindfold on. But here’s the thing: this is how CEOs are actually wired. We sign up for the job to solve important problems, to create solutions, to believe that what we’re building matters, and to fight our way forward. New disruptive, scary, and world-reshaping technologies have historically expanded human capability and productivity, and, in turn, have created more wealth and abundance for people. That’s not naivety, that’s a repeatable pattern well documented in human history. Heck, it’s played out multiple times in just the last 150 years.

AI will change how your company works. It will change some of the roles on your team, and may even reduce the need for everyone on your current team. But the organizations who thrive through the AI-driven transformation will be the ones led by CEOs and senior executive teams who lead with a positive view of the future and an optimistic belief that their organization’s vision and mission must persist.

So that’s your most important job right now: Be Positive and Stay Optimistic. Maintaining this perspective and personality trait will become increasingly paramount for you as CEO given that it’s a safe bet the AI doomers will continue to beat their drums louder and louder.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

“Which way should I go?” is the question we’re constantly asking. In the career context, people express frustration about their current job situation or complain about their inability to find an opportunity that might energize them. And when I ask what organizations they’re targeting, most don’t have a clear answer. They’re applying broadly, scrolling job boards reactively, and hoping something sticks. In a transforming job market, hoping that this will magically land you in your dream role is a false hope.

So what’s the real issue? People don’t spend time really figuring out who they are, what they want to be doing, and then mapping those insights into a specific target list of organizations where they could thrive.

When advising student-athletes through their college recruiting process, I use a simple framework that applies directly here. First, we work to help them define their “best fit” — combining their academic and athletic profile, college preferences like location and size, and what works financially. Once those variables are clear, I tell them they’re ready to “recruit yourself.” That means building a target list of 10-30 schools, developing a strong online recruiting profile, and then consistently reaching out to coaches at their target programs. This process is critical because here’s the reality: 99% of student-athletes need to invest the time and effort to recruit themselves. No one is coming to find them.

The same is true for your career.

Define Your Best Fit

Ok, I get it — when just starting out the “just trying to find any job I can get” approach makes sense, and given the uncertainty AI is creating, this may be the vibe more than ever. But whether you’re in your first role or your fifth, you should be using the job you’re in now to sharpen what “best fit” means for you.

Current roles help a lot because many of our ideas about work–and what works for us–get shaped by the jobs we’ve experienced. But even as you move through subsequent roles, keep asking yourself:

• What problems do I want to help solve?

• What users or customers am I most drawn to serve?

• What kinds of things do I love doing in my work — and what do I hate doing?

• What kind of environment do I thrive in — working with a lot of people, or performing more often in solo-player mode?

There are many more questions you can come up with to shape what matters to you. The key is being honest about what you’ve learned from the roles you’ve already had, not just what sounds good on paper.

Build Your Target List

This self-inventory sets you up for the next critical step to create a target list of organizations based on what you’ve identified. Focus on the problems you want to solve and the users or customers you want to serve, because these are the variables that let you research organizations from the outside and evaluate whether they map to your priorities.

See how many organizations should be added to your list. It might be two or three, or it could be ten or more. The number matters less than the specificity so make sure each organization is on your list because you can connect it directly to your best fit questions.

Once your target list takes shape, make sure your “online recruiting profile” is fully built out. For the career context, this is your LinkedIn profile. Make sure it highlights who you are as a person and potential team member. Imagine someone from an organization on your target list lands on your profile, and ask yourself, “Will they see something that makes me stand out?”

Start the Outreach

The real work begins once your target list and profile are in place. Start researching who works at the organizations on your list in the types of roles that interest you — marketing, product, business operations, whatever your lane is. Keep track of these people and look for connections. Are you one or two degrees of separation away? Who can make an introduction?

Then reach out. Communicate clearly your interest in learning more about their organization and the role they play. Propose a 15-20 minute Zoom conversation, as that’s a time commitment most people can fit into their schedule. And if you’re in the same city, offer to meet for coffee near their workplace. Meeting face to face is always the best option when it can work.

Use these conversations to learn if this organization is truly a best fit. Ask what they look for in new hires, what attributes they prioritize. Ask about the hiring process. Don’t be afraid to ask if roles are open that aren’t posted on the careers page. And ask the person you’re meeting with if they can introduce you to another colleague at the organization. Keep building your network inside the places that rise to the top of your list.

This is the part of the process where the real learning happens. The more conversations you have, the more you learn about how different organizations work, and you keep refining what “best fit” really means for you.

Final Note

Your target list becomes your north star. It’s a mirror reflecting what organizations and roles within them represent the best fits for you next. And the more time you spend engaging with people at these organizations, the sharper the reflection that mirror provides.

So here’s your move this week: sit down for 20 minutes and write out your answers to the best fit questions above. Be honest with yourself about what you’ve learned from the roles you’ve been in. If you already have a target list, revisit it to confirm it still reflects where you want to go. And if you don’t have one yet, that may be why you’ve just been aimlessly following roads that have been taking you anywhere.

Now, what organizations are on your target list?

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

It’s a bold claim that’s become part of the growing chorus of AI predictions, right alongside “AI will replace all entry-level jobs” and “AI will replace all knowledge workers.”

But what if the more practical opportunity isn’t the solo genius building a billion-dollar empire from their bedroom, and instead it’s the 3-person team that can accomplish what used to require 30, 100, or even thousands of people? Unlike the mythical 1-person unicorn company AI CEOs are predicting, my guess is that this 3-person version is spinning up all over the U.S. right now, and what’s even more exciting is that it’s a version you could be a part of.

Why Three People, Not One?

I’ve been more or less testing this question with my nonprofit MyNextPlay.org, and here’s what I’ve learned: trying to do it all solo, even with AI, is complex and exhausting. The cognitive load of switching between product development, business operations, and go-to-market execution is mind numbing. In fact, I question whether the human brain is even capable biologically of toggling across these three domains, let alone being able to expertly process all of the sub-topics that live within each area.

Anyone spending meaningful time with AI today knows how fatiguing it can be — all that prompting and re-prompting, instructing and correcting, just to get the work done the way you need it.

AI extends what one person can do, no doubt. But there’s real value in having actual humans in complementary roles. Three people who are each expert in one domain, using AI to 10x their individual capacity, will outperform one person trying to horizontally cover the entire org chart even with an army of AI agents at work.

At least right now (and maybe this is the overly optimistic belief I hold), expertise still matters. AI agents can do a lot, but they too often make assumptions that are wrong, drift into excessive complexity, can’t hold onto memory that is needed for future context, pursue strategies that fail, iterate and repeat attempts that don’t work the first time, and are apt to make recommendations that if actually implemented could sink what you’re building. Human judgment, built from real experience and domain knowledge is required to catch those mistakes before they become problems.

Basically, AI keeps you active, moving forward, and expanding the scope of what you can work on, but it doesn’t efficiently and consistently yield the best results.

What the 3-Person Company Actually Looks Like

Here’s a potential model: three core functions, each led by one person with domain expertise who uses AI to expand their scope and therefore the organization’s capabilities:

• Product Development Lead — This person owns the product vision, writes the product brief and requirements with an AI agent’s assistance, manages the development, testing, and deployment process in GitHub, and builds releases from MVP to production-ready using AI coding agents like Claude Code, Copilot, Cursor, or the like. They’re shipping and iterating with users, using AI to do work that used to require multiple engineers and designers.

• Business Operations Lead — Your Swiss Army knife for everything that keeps a company running. They build the operating model, manage finances and fundraising, handle incorporation and legal agreements, set up people systems. With AI helping them automate financial reporting, draft contracts, and manage compliance, one person can genuinely handle what used to be a full finance, people, and business ops team.

• Go-to-Market Lead — This person builds your customer acquisition channels, runs marketing campaigns, handles sales conversations, and manages customer success across your existing customers. AI helps them create content, analyze campaign performance, personalize outreach at scale, and track customer health metrics. They’re your entire revenue engine and they employ agents to ensure key metrics like customer acquisition cost and lifetime customer value are optimized.

Each “lead” focuses on deploying agentic AI to fully develop the breadth of what their functional area must execute, and through collaboration with AI they continue to leverage their specific expertise to scale what the organization can achieve. And most important, they collaborate with each other to ensure that AI agents fully collaborate across the organization and to bring human judgment to the strategic decisions that AI can’t fully make.

What This Means for Your Career Right Now

If this is where things are heading (and I think it is) then one distinct piece of career advice becomes pretty obvious.

Don’t try to be an expert at everything. Pick one of these three domains and become excellent at it. Are you the person who loves building products and can translate user needs into features that ship? Are you the systems thinker who can architect how a business operates, generates revenue, and should allocate capital? Are you part storyteller, part analyst, i.e. someone who can both engage audiences and convert them into growing customer bases, then turn your most avid customers into advocates?

The opportunity isn’t to shoot for the glory of becoming a solo founder who does it all. It’s becoming so good at one critical function that you’re the person someone wants as their product lead, their business ops lead, or their GTM lead when they’re ready to build something with you.

So here’s your next challenge:

1. Identify the functional area that you would take the “lead” role in

2. Find two other people who are each expert in one of the other two functional areas

3. Go build a company or organization with these other two people that solves an important problem

And hey, if the above sounds like too much to take on right now, then just keep working on #1 above, and keep your eyes open to find people who check the box for #2. Once these two steps are in place you’re on your way to step #3 before you know it.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

The New Stack piece interviews a dozen engineering leaders (each with over a decade of experience) asking them what advice they’d give people entering the tech job market in 2026. These expert recommendations apply whether you’re heading into software engineering, data science, or honestly any field, given the reality that everyone’s trying to navigate a rapidly changing work and career landscape.

What the Experts Say

Below are a few of the most important — and consistently offered — takeaways offered by the experts:

• Get really good at learning. Your ability to absorb new concepts quickly and apply them matters more than what you already know. The tools will keep changing, let’s face it, there’s a new “AI-agentic-blah-blah-blah” announced every day. Your learning velocity is what creates an enduring advantage.

• Don’t compete with AI, collaborate with it. You’re not going to out-code or out-analyze AI. Learn to use it as scaffolding for your work while you focus on the judgment calls that actually need to be made by a human, like knowing how to “prompt” your AI-partners to follow the right path, and solve the right problems.

• Build relationships as well as systems. Technical skills get you in the door. The ability to build trust, develop influence across teams, and understand what challenges other people face determines how far you can go. AI writes code, but it hasn’t demonstrated an ability (at least so far) to build relationships —or communicate effectively—in the authentic way you can.

• Focus on the basics. Don’t skip the fundamentals in pursuit of the latest framework or greenfield solution. Understanding core principles—things like security, system design, and compliance best practices—creates a foundation that doesn’t collapse or need to be refactored when it’s time to scale or deal with the “next wave” of change.

Two More Key Recommendations

The experts interviewed in TheNewStack piece covered many essential points, but two critical takeaways weren’t highlighted by the article’s experts. It’s these two attributes that separate great technical professionals from good ones:

Understand the business you are in and the customers you serve. It’s easy for engineers and technical team members to treat the “know the customer” challenges as someone else’s job. They love to build elegant solutions or create amazing features which may not satisfy customer needs, and therefore fail to power the organization’s business model towards profitability, and more importantly sustainability. Ask yourself a few questions:

• How do we enable our customers to absolutely love what we build?

• How does what we build then create value someone will pay us for?

Connecting your work to actual customer success makes you exponentially more valuable within your team and more broadly to your company / organization.

Focus on defining important problems, not just solving those assigned. The people who advance fastest aren’t just good executors waiting for tickets to close. They extend their ability to “learn rapidly” (see above) into an ability to define which problems, once solved, unlock value for others—customers or colleagues. Pay attention to problems that reveal themselves as bottlenecks, recurring complaints, or opportunities that aren’t being pursued. And once you’ve defined a big problem, take the next step: design and propose a solution.

The future belongs to technical experts who can help deliver amazing experiences for customers and solve real problems for real customers and co-workers, working in collaboration with both AI and other humans. That’s a skill set worth honing because it will play in any job market.

This post draws from “Advice for People (Still) Entering the Tech Industry in 2026“ published in The New Stack.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

So what does “taking ownership” of your career actually look like from where you sit in your current role or when you’re seeking your first one?

Start by considering how you’d prioritize choices across three dimensions that can help shape your career journey.

What Industry and Organizations Do You Want to Work In?

This is the WHY question. Ask yourself ‘Why am I working here?, and ‘Do I believe deeply in the product or service we’re creating and delivering?’ Specifically, do you connect to the user and customer personas you serve?

Some people put this element at the top of their list and make it non-negotiable. For example, they’re drawn to climate tech, education innovation, healthcare breakthroughs, or financial services. The mission matters. They want to wake up feeling like their work connects to something bigger, something that’s really important to them.

Go deeper. Are you drawn to mission-driven nonprofits? Do you want to build a consumer product people need and use every day? Are you interested in working to streamline how a complex and highly regulated industry like healthcare can make people’s lives better? Do you want to help small businesses scale, or would you rather work on infrastructure that most people never see but that ultimately powers everything?

I like to start here because this is the dimension that can gnaw at you if you get it wrong. If you wake up one day and think “why am I building, selling, or doing operations for this company?” you may need to step back and find an industry, and organizations in that industry, that matter more to you.

What Functional Area Do You Want to Become Expert In?

This is the WHAT question. What functional area within the organization do you see yourself developing real expertise?

Think about three broad buckets:

• Product development - building and shipping the product or service

• Go-to-market - selling and supporting the product or service in the market

• Business operations - helping the organization run efficiently and scale

Then get more specific. Within product development, are you drawn to product management, engineering, design, or data science? In go-to-market, is it sales, customer success, marketing, or strategic partnerships? In operations, do you see yourself in finance, people/HR, business operations, or legal?

This matters because functional expertise is what travels with you across industries and companies. You can shift from a healthcare startup to a fintech company and still be “a product person”, “revenue centered”, or “someone who builds finance systems.”

You can absolutely shift your functional focus over your career—plenty of people do. But having a functional anchor early on helps you tell a clear story about your career focus and makes it easier for others to understand the value you bring today.

Functional expertise also helps when you are trying to land in a new industry or ecosystem. For example, project management expertise is needed in every sector — use your functional expertise to help you move to the organization that matches your “WHY?”.

What Stage of Company Do You Best Fit In?

This is both a RISK and VIBE question, and it’s the one people can easilty generate a “grass is greener” mentality around.

Early-stage startups (going from zero to one) mean high risk, wearing many hats, failing and fast learning, constant chaos, and not much “manager feedback”. You might have equity that becomes worth something—or nothing. You’ll work on problems that don’t have established solutions, which means lots of experimenting and iterating. And the definition of work-life balance isn’t a 40-hour, Monday through Friday thing.

Growth-stage companies (think Series B or C funding rounds or even later stage) still carry risk, but you’ll find more structure and likely more functional focus. The company is trying to scale what’s working, which creates different challenges. For some people these companies are the Goldilocks “just right” combination. You’ll have more people to collaborate with and learn from, but less ability to shape things from scratch. Your role starts getting more defined as teams specialize, but things are still fluid so you might be pulled in a lot of directions and feel like you report to several people.

Large, mature companies mean more resources, established processes, and (maybe) clearer career paths. You may have more narrowly defined responsibilities, and perhaps find yourself operating in a more predictable decision making environment that doesn’t tug at you from multiple parts of the organization. It’s also likely that you can discretely define your work and non-work schedules.

Your risk tolerance and desired workplace vibe isn’t static—it shifts throughout your life. What feels exciting and manageable when you’re 25 and single might feel terrifying when you’re 36 with a mortgage and two kids. There’s no “right” answer here, what matters is tuning into what works for where you are at your current life stage.

Land and Expand

The goal isn’t perfection. It’s all about establishing a foothold.

Pick the one dimension that matters most to you and explore from there. Maybe you know you want to work in climate tech but you’re not sure if you should go into sales or operations—that’s cool, you have prioritized the industry and can explore the functional areas more once you “land” inside a climate tech company in a role that maps to your current functional identity.

Or maybe you’re clearly a product designer working in a large company, but you’re struggling with FOMO thinking you need to be at a VC backed startup? That’s something you can “solve for” by finding a PD role at a start up, and then paying attention to how that environment feels to you. Do you thrive in ambiguity and constantly needing to “figure it out” with fewer resources, or do you miss the bigger team, more predictable pace and not having to turn something around on a Saturday afternoon?

I could go on and on with examples. The important thing is to find which of the three dimensions you’ll “land” on first and then expand from there. Once you land somewhere, you’ll learn fast and figure out which of the other two you need to dive deeper into next. And document your work somewhere - a google doc, your phone notes app, etc. so you can continually see where you’re at and where you have work to do.

So where are you going to “land” in the next few months: the “why”, the “what”, or the “risk/vibe” dimension?

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Being a CEO isn’t easy at any stage, but the job is vastly different if you’re running a public company (which I’ve never done) versus if you’re leading a venture backed startup that’s somewhere between zero and exit (which I have done).

Typically, the startup CEO profile looks like one of the following personas:

• Founder from a functional area of expertise (i.e. product development, engineering, etc.) who has to learn how to build a company working with functions they aren’t expert in and with investors they have sold on a big vision. This CEO may take the company all the way to exit or may hand off along the way to a CEO who fits the profile below.

• Experienced operator (who may or may not also be a first time CEO) brought in by the board to help a company navigate from some level of product / service usage (and likely some “issues” that need to be navigated) to an exit, which statistically speaking will most likely be an acquisition.

Given the complexity of the CEO job—and the reality that nobody gets a Bachelor’s degree in “Being a CEO”—it’s pretty common these days that folks (i.e. board members) will at some point recommend that the startup CEO work with a “CEO coach” who can help them navigate the twists and turns inherent in leading an early stage company.

So if you’re a startup CEO what questions might you ask to answer the “CEO coach” question?

Here are 5 questions that can help you sort it out:

1) “Do I even want or need a CEO coach?”

• Are there topics or questions you want to talk with someone about in a confidential manner, and do you have a sight line on what those topics and questions might be? I’ve met with many CEOs who felt they didn’t need a CEO coach, or perhaps weren’t “there yet” in understanding what a coach could help them with. Don’t sign up for a coach because someone told you to get one. Only lean into finding a CEO coach when you feel drawn to the idea of being able to talk about “things” with someone who’s 100% there for you, and you alone.

2) “Who can I talk to about anything?”

• This might be the best way to identify not only whether you should work with a coach, but who the right coach might be. Let’s face it, startup CEOs can’t (and literally don’t) talk to their co-founder(s), members of their executive team, their board members, their partner, or even their best friends about everything they are wrestling with at any given moment. (Well, they probably could tell their partner everything, but that’s likely not a great dynamic if you’re hoping to build a healthy long-term relationship). When you need to talk to someone other than that little voice in your own head, you might be ready to find a CEO coach.

3) “Do I really think I can talk to my board members about everything?”

• Part of what makes being a CEO so difficult is the collective opinion everyone around you has about how you’re doing the job. No matter how confident a startup CEO appears to those around them, they’re not actually that confident everything’s going to work out. Here’s the reality: the group you’ll spend the most time managing your confidence in front of is the group whose job it is to hire and fire you…your board. And this dynamic is more pronounced in an early stage company where your board members are primarily (or exclusively) your investors. What you need is an “independent independent”. More on this concept later.

4) “Do I think one of my independent board members can be my coach?”

• Sorry, but even if you add your mom to the board as an independent director, she has a fiduciary responsibility to all shareholders, including the preferred equity holding shareholders like her fellow investor board members. So if you end up telling your independent director (aka your mom) over dinner something like “I don’t know how to [insert one of the many things you don’t know how to do as a startup CEO]”, all that’s going to do is make mom think “Hmmm…maybe my kid isn’t cut out to be the CEO of this company.” See above: what you need is an “independent independent”. Again, more on this concept shortly.

5) “Do I have to pick between a ‘been there, done that’ former CEO as my coach or someone with a graduate degree in psychology who can ‘help me develop my leadership skills’?”

• This is a false choice. First, plenty of former CEOs who have been through what you’re dealing with can—and often do so very effectively—provide great insights around the “soft skills” that some CEOs feel can only come from a “psychologist”-type coach. Second, if you can’t find the right coach who can do both, then select a CEO coach who can address topics you want the most help with right now. That’s either someone who you can ask something like “how did you think about how much of your series B to invest in the core business versus new initiatives?” or it’s someone who you can ask something like “how can I get my product and go-to-market leaders to work together more effectively?” The good news is there are CEO coaches out there who can talk through both types of questions with you in a confidential setting.

So what do I tell a CEO when they ask me whether I think they should work with a CEO coach or not? Here’s how I lay it out for them:

• Every CEO needs someone they can talk to about anything—someone to whom they can ask the ‘How do I…?’ or ‘I don’t know how to …?’ type questions and not worry that they’ll be judged as not being capable of doing the CEO job when they do. I think this advice holds for any CEO, but is particularly helpful for “first time” CEOs who don’t fully realize the ‘multivariate calculus’ nature of what being a startup CEO entails.

• An ideal CEO coach as someone the CEO can think of as an “independent independent”: the person that they’ll get everything out of as if they were an “independent board director”, but who is fully “independent” and therefore in service first and foremost to the CEO. The value of this ‘consiligere’-like leverage that a CEO coach provides is difficult to describe until you tap into it during a company crisis or when you’re stressed the night before a board meeting.

• And a good CEO coach is a litmus test for your board and investors who may have been the folks recommending you work with a coach. Good board members and investors have seen the positive impact a CEO coach can bring to a company, and they don’t feel threatened that the CEO is talking to someone that they might never even meet. It’s the folks around your board who don’t embrace the privacy of this “CEO to CEO coach” relationship that you may need to wonder about.

If you’re in the midst of the startup CEO journey and climbing alone, I’d encourage you to re-read this post and listen to that voice in your head after you ask each of the questions above one more time. If you’re still feeling like “solo player” mode is right for you, great, no need for a CEO coach. But if asking yourself the questions above makes you anxious about walking the tightrope by yourself, make it a priority to find your “independent independent” in the coming weeks or months.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.

Everyone’s telling you to uplevel your skills. But which skills exactly? Good luck finding a short, scalable list of which skills matter most.

Before try to offer up some ideas of skills that will help you thrive in an AI-augmented workplace, let’s get clear on the differences between a few terms that get used interchangeably (but are actually quite different), specifically: abilities, knowledge, and skills:

• Abilities – these are the more stable characteristics that can include cognitive, sensory and physical abilities, think of these as your innate abilities, or “talent”, that reflect how you’re hard-wired.

• Knowledge – this represents the body of factual or procedural information that can be applied, think of this as the information and understanding you acquire through education and experience.

• Skills – these are the capabilities required to perform tasks accurately and successfully, think of skills then as the applicable capabilities you develop through training and practice

(Source: Gartner)

For years, we’ve talked about skills as function-specific capabilities. Sales professionals need to fully hear customer needs and persuade them to buy. Product managers need to define customer pain points and translate that into requirements for design and engineering teams to build. These functional skills still matter, but as AI continues to pervade the workplace, “functional” boundaries are disappearing. When salespeople are vibe coding and engineers are selling, what then are the most important skills?

There are three universal skills that every individual should be continuously honing and developing, regardless of their role or industry.

Be a Strategic Thinker

I’ve witnessed firsthand the challenges people have in truly understanding the broader strategic context in which their organization—and therefore their role—exists. Oftentimes people struggle to move beyond the scope of their day-to-day work so they can fully assimilate how everything fits together. It’s like being handed a single puzzle piece and being asked to describe the full picture—you need to zoom out first.

Start by defining the “ecosystem” in which your organization operates. Who are your customers versus your users—are they the same cohort or not? Who are your competitors? Who can be valuable partners to help you grow faster? What existential risks lay under the surface? Then bring this broader perspective inside your company and click down strategically: What should we create for our customers? How do we most effectively get our service or solution to them? How do we generate revenue and become profitable—ultimately building a sustainable organization?

These elements represent a few key layers of strategic thinking, and they matter whether you’re an individual contributor or a senior leader. What’s more, strategic thinking is a skill that’s accessible to anyone willing to invest the time to truly understand the bigger picture.

Be a Problem Definer and Solver

Strategic thinking sets the table for the second critical skill: the ability to identify and define the most important problems your organization needs to solve. What’s keeping your company from breaking through? Is it at the product or service level, or is it how you’re taking the product or service to market? Wait, maybe it’s a flawed business model, poorly architected people plan, or insufficient fundraising effort?

Once you’ve identified and defined a problem clearly, get good at developing potential solutions. This is where your strategic thinking skills get activated. You design the way to tweak your product or service to really light up customer demand, or the way you’ll rethink your go-to-market motions to break into a customer segment your company can really scale.

Remember, these two go together. If you just define a problem, that’s half the battle. Expect your manager or boss to then say something like, “Don’t just bring me problems, bring me solutions!” And they’re right to say that.

Be a Collaborator

The third powerful skill for workplace success is how you show up to collaborate with your colleagues. Specifically, how does your strategic thinking and problem-solving prowess manifest itself to bring those from other parts of your organization along? How do you encourage and inspire teammates to unlock their own skills, knowledge, and abilities to foster your organization’s success? It also means knowing when to step back and let others play point based on their strengths, and being generous with credit when other ideas work. The best collaborators I’ve seen don’t hoard information or control—they share both, trusting that empowering others ultimately multiplies their own impact.

That’s the human-to-human side of collaboration. But moving forward how you collaborate with AI tools and platforms will be critical. For example, how do you provide strategic context and problem definition to AI agents so they can multiply what you and your colleagues achieve—both in breadth and speed? Being able to work effectively with AI isn’t about replacing human collaboration; it’s about amplifying it. This human-to-AI collaboration skill will increasingly separate those who thrive from those who may fall behind in the workplace.

Where to Start

Strategic thinking, problem defining and solving, and collaboration—these three skills are accessible to everyone. They’re not reserved for those with certain degrees or backgrounds. But they each require “intentional” practice. You have to be willing to be wrong, to fail, and to quickly learn because that’s how you truly develop these skills.

A piece of advice: Start with whichever of these three skills feels most comfortable to you right now. Build a strong foundation there in this skill, then work to connect that skill in a tangible way to the other two, and keep iterating. The beauty of focusing on these universal skills is that they compound—each iteration makes you better at all three, and the value they create grows exponentially as you advance in your career. Get going now, stay consistent, and no matter how much AI transforms your specific function or industry, these skills will serve you for decades.

NextPlay>Forward AI Disclaimer: I very actively use artificial intelligence and large language models to generate the content you read here, but I do review it and edit it to make sure it can be generally useful to people who read it. Keep in mind that AI can make mistakes - check important information. Let me know if I make any errors and I will correct them.