Okay, in all seriousness and given the world swirling around us, I have very little clue what’s really going on, and I increasingly wonder what I’ve actually figured out at this point.

However, to the extent I do know some things it’s because of the people who were willing to “take the call” or “take the meeting” with me over the years. It turns out we learn so much from others, one conversation at a time. The experiences others share about their successful and unsuccessful decisions, and the full story surrounding each, become crucial roadmaps we tap into on our own journey.

Throughout my life I’ve been fortunate to meet many people and have benefited immensely from thousands of conversations, most all of which were granted out of the kindness of someone willing to take the time to help me. A few examples:

• Learning how the “business of sports” works across the complex ecosystem of rights owners, rights holders, sponsors, and audience (thanks Rudy)

• Learning the multiple layers of how venture capital works, and how startup companies evolve (or don’t) based on how well they are able to extend runway with each round (thanks Kevin)

• Learning how to treat people and raise kids (thanks mom, dad, and my four grandparents)

When I receive a request to have a chat with someone I try to say yes as much as I can. I’m not gonna say my hit rate has been 100%, but it’s pretty high. Why? Because a lot of people said yes to me and I absolutely know how much those conversations helped me.

Recently, I met with someone over lunch and listened closely to what they were trying to figure out next in their life. I heard them asking for a conversation with someone closer to the leap they were contemplating. Then I got to do something really fun. I got to reach out to two other folks I’ve had the opportunity to help in the past and ask if they would be willing to chat with my lunch guest.

Not surprisingly, both instantly said “yes!”, and I made text intros and know two more important conversations will happen (or likely have already happened). I don’t need to know more than that. This is the multiplier effect of “taking the call” or “taking the meeting”.

Actually, right after both text threads quickly turned into calls being set up, I realized something. What gets said in any one conversation still matters. But the bigger impact might actually be paying it forward, adding another link to the “take the call” chain. Because the more links that get added, the more real learning gets shared around.

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.

I’m talking about urgency and quality.

“Move fast and break things.” That’s the Silicon Valley mantra.

“Be quick, but don’t hurry.” That’s how legendary UCLA Basketball Coach John Wooden guided his teams.

So what do I say when someone asks whether it’s more important to go fast with urgency, or to move slower and keep the quality bar high?

My answer, without flinching, is one word: Yes.

There it is again, my favorite idea. Two things can be true.

Urgency

You gotta go fast if you’re a startup, there’s only so much time to figure things out before someone else does. And once you’ve taken that first dose of venture funding, the clock only moves faster when it comes to proving out something meaningful. This “success” could be reaching the vaunted state of “cash flow positive” where you’re now controlling your own destiny, or it could mean reaching a “proof point” like product-market fit that can unlock a next round of funding.

On a personal level, urgency matters too, but you get to define it on your own terms. You’re right out of college and still figuring things out, relax. You don’t need to “urgently” have it all mapped out from today until you cash your first social security check. Go slower and try some things out. Take some meetings where you walk out thinking “Well, that was a waste of time.” Actually, it wasn’t. It was another nugget of learning you get to use. It’s just another slice of your personal urgency. Taking the meeting is you acting with urgency. Skipping it is not.

Quality

You know quality when you see it and when you feel it. What’s harder is to assess what meets the quality bar in the eyes of others.

When you’re that fast-moving startup, you have less (some might argue “zero”) brand risk in putting out a crappy product or service in the name of moving fast. This ethos may never change, but that’s okay because it’s not about that first low-quality release, it’s about how quickly you turn feedback into quality. Every company gets a bucket of brand goodwill with its user/customer base, just don’t burn through all of it. And be sure you are achieving enough progress on the quality front to refill that bucket with your target base faster than you use it up. If you burn through it all, could that be the end of the road?

On the personal level, quality is your brand. How others judge the quality of your work product, your work ethic, and your teamwork all add up to fill your quality bucket. Doing your projects half-assed, not putting in the effort to get results (yes, a form of urgency), and working for yourself over the success of your company or organization all drain your quality bucket. Like the company that drains its goodwill bucket, if you completely empty your personal quality bucket you may find that your reputation precedes you the next time you’re interviewing for that new gig.

Peanut Butter and Jelly

Urgency and quality go together like peanut butter and jelly (or honey, if that’s your sandwich of choice).

Imagine moving fast, and breaking things, only so far as they help your company reach its ultimate quality destination. Stick to your vision and mission, and use these two statements to guide how well you’re serving your users/customers as you urgently move forward. Don’t take shortcuts, cheat, or over-index on what a competitor is doing or saying. And don’t overtax the goodwill you’ve built with your users/customers.

Likewise, set your personal urgency pace and keep the quality bar sufficiently high so you are recognized in the eyes of others in the way you want to be seen. This might be where you act quickly, but you also know when not to hurry as Coach Wooden advised. Do great, high-quality work. Always be proud of what you create, and leave a margin of error in there for “what can be done to make it better in the next revision.”

Do your best to prioritize and do the things that feel important and meaningful to you right now.

This is the best way to act urgently, not frenetically, and at the same time define quality based on how you help others versus measuring it solely on how it contributes to your personal wealth.

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 while I’m getting better at scanning things that hit my inbox and moving on, I just can’t resist big graphics with compelling visuals:

This is one of several cool-looking graphics in a substack called Lenny’s Newsletter. I don’t know Lenny, but it looks like he and the guest author of this post (Noam) have been doing a lot of surveying to get a read on how tech workers are feeling in 2026. The image above is from this post, and the article by Johannes Sundlo covers a lot of results from their user surveys. (Note: I didn’t read the post, just scanned the graphics.)

I did read a shorter post (on a different site, not Lenny’s) that used the same graphic, this one with a title screaming: “HR is asleep and people are burning”.

Geeze, I really hope the HR department isn’t literally letting people burn?

Let’s spend a minute on human resources (HR) or people operations (PO) as the newer department name goes. Despite the popular tech narrative that it’s an unnecessary function, people operations can absolutely be a force multiplier for an organization, when done right. So, simply put, what does “done right” look like?

PO contributes in three important areas, specifically establishing and managing the:

• Systems that support people and the company. This can include choosing and managing the platform that ensures benefits (e.g. healthcare) are easy to manage at the employee level, and it can also include managing the compliance complexities that any organization needs to comply with at the state and federal level (e.g. parental leave).

• Process through which the organization recruits, hires, and onboards new team members. There’s no silver bullet for how this is done, and the best PO teams work closely with every other functional group to understand how to best source candidates and efficiently architect the interviewing and hiring flow. Think about it. What works within the engineering department is likely to be fairly different than in the sales team. And crucially, great PO teams really focus on how to onboard new team members so these folks are able to productively contribute as close to day one as possible.

• Effort to develop talent by investing in manager development. We all know the data: an employee’s relationship with their manager becomes the single most important factor that drives employee engagement and retention, or conversely, that causes team members to disengage and look for a new job.

But wait, what the post above implies is that it’s HR’s or the PO department’s job to get all this right, otherwise, people will be burning. Let’s be clear. The PO department isn’t solely responsible for ensuring every team member’s personal happiness and engagement. Yes, they are accountable for doing the best job they can in the three areas above, but ultimately the key is how YOU as a team member take control of your experience in the workplace.

Whether you’re at a five-person startup or a 50,000-employee multinational corporation, there are a few clear things YOU need to prioritize and be responsible for when it comes to being happy and engaged in your work:

• Communicate and engage consistently with your manager so you two are aligned on what the organization’s strategic priorities are and how your work connects directly to those priorities. If you work in a privately held company or a nonprofit, know your organization’s financials, operating model, and path to profitability. Pro tip: don’t skip your weekly or bi-weekly 1-1s with your manager, and if they have to cancel your 1-1, insist on rescheduling so communication gaps don’t emerge.

• Know your responsibilities and what you’re accountable for each quarter, then translate that into how you focus your effort each month and week. For individual contributors (IC), give thought to if you want to be a people manager and what that path looks like. For managers, you have the challenge of ensuring you know your own responsibilities via conversations with your manager, while also being adept at translating to your direct reports what their responsibilities look like. Pro tip: don’t assume AI is going to replace managers, act like no role will be more critical than a manager.

• Commit to work hard and deliver high-quality work output, depending on what this looks like for your functional area. For example, what do the best engineers in your organization do and how do you describe their work ethic? Likewise, what does this look like for the top people in marketing, finance, revenue operations, and every other role across your organization? Pro tip: don’t be outworked, and if this feels hard for you to achieve, then maybe you need to find a new organizational mission.

• Lift others up and be seen as the ultimate “team first” employee. Feelings of connectedness, engagement, happiness, and regeneration all flow from being in service of others. In today’s world where it might appear that it’s “me first” and about “building my personal brand”, the reality is what really matters is how the organization and team you’re a part of does. That’s what energizes you in the moment and what you recall and remember long after you’ve moved on.

I do appreciate the sentiment shared in Sundlo’s post. It’s admirable when any function raises their hand and says “we need to do better”, but in this case I’d suggest that the “we” starts with each team member ticking through the themes above. If you aren’t bought in on those and living them, not even the greatest PO department that’s providing the greatest set of HR systems, platforms, and tools will be able to make you happy and engaged. That’s on you.

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 great to chat with young people as they are about to take big steps in their lives. I vaguely remember that summer between high school and heading off to college. I take for granted now what a leap it was, that drive with my parents from my rural Oregon hometown to Pullman, WA, where I began college as an unrecruited, unknown walk-on at Washington State University. A nobody who ended up red-shirting that first year at WSU, and then briefly deciding to give up baseball, before re-routing to play at the level I should have started at all along at Pacific University in Forest Grove, OR. Whoa! That was a while ago.

During my lunch with my two former players, I imagined the journey ahead for them. For some reason the classic Yogi Berra quote popped into my head:

“When you come to a fork in the road, take it.”

For sure, these two young men will encounter several forks in their respective roads over the years to come. We all do. There’s one fork in the road I recall very clearly, one that probably had the biggest impact on how my life has unfolded over the ensuing decades.

The Fork

I’m committed to keeping this story concise, so I’ll skip the prelude and much of the run up to the ultimate fork in the road. By early 1994 I had come to the conclusion that I needed to pursue an MBA in order to get to where I wanted to be career-wise. Yes, a second graduate degree following an MA in Economics that I had finished in 1991 the year after completing my Bachelor’s degree at Pacific.

Feeling as though I was pigeon-holed into “economics” related jobs, I looked at the MBA as my opportunity to pivot. A way to rebrand myself as something more than an economist. So I set my sights on prepping to apply to business school and start on this two-year journey in the fall of 1995. However, beginning also in 1994, I had signed on as a part-time scout for the Chicago White Sox, roaming the greater bay area and evaluating high school and college players for the MLB amateur draft.

As 1994 played out, the fork was slowly forming. One branch was the MBA path. Evaluating programs, studying for the GMAT, and then ultimately completing just a few applications to schools, all in California. (East coast schools were disqualified after spending an early December week freezing my butt off in New York and Connecticut).

The other branch was the path to a potential full-time scouting role and the journey from there to front office job with an MLB team. This included spending time with the full-time area scout I worked with, and also getting to meet the guys who held the big decision making jobs like national crosschecker and director of scouting.

By the time spring of 1995 rolled around, the fork that Yogi would have me “take” was pretty fully formed, though some uncertainty remained:

• Fork 1: I had been accepted into the full-time MBA program at UCLA’s Anderson School starting in September

• Fork 2: I had interviewed with an MLB team for two different full-time area scout roles, one covering multiple states in the south and the other covering multiple states in the midwest

The Decision

This is where the details and my memory get a little fuzzy, especially when it comes to exact dates. But sometime in later spring of 1995, say mid to late June after the MLB amateur draft, I needed to make a decision. I had already accepted my spot at UCLA and paid the deposit, but I had recently completed the MLB team interview with a senior-level scout who would oversee both of the open full-time area scout positions they were looking to fill.

I needed to make a decision soon. The timeline to find a place to live in Westwood, and then pack up and move, was fast approaching. The MLB scouting opportunity wasn’t a firm offer, but it felt like it might be there if I wanted to pursue it. I had to do some “futurecasting” and think about “optionality” in a way I hadn’t before. And to be clear, this was well before the term “optionality” had been crafted as a term of business art by Silicon Valley’s venture capital community.

Here’s roughly how I compared the two options, as best I can recall. Spoiler alert: it won’t be hard to determine as you read through the bullet points below which fork in the road I ultimately “took”:

• The MLB scouting job was still in the “interviewing” phase, so the probability of even being offered either of the positions certainly wasn’t 100%.

• Getting an MBA would allow me to pivot into any type of business role, including perhaps working for an MLB if that’s what I wanted to do, or instead heading to New York to be an obscenely rich investment banker.

• Even if I was offered an area scouting role, there would be no guarantee I’d be able to transition into a full-time front office role. There was always the spectre that as a non-former MLB draftee I’d be on the outside looking in for senior level roles in professional baseball.

• An MBA had a clearly defined net-present-value in terms of what it likely would be worth for me financially, and what that would mean for the life I hoped to eventually build with a family and home in California.

The Results

Yes, I went to UCLA and finished my MBA in 1997. I spent the summer of 1996 interning at MLB International, meeting and networking with people whom I both learned from and would later work with as part of my career journey.

I’ve never looked back with regret, wondering if I should have held out for a full-time scouting role. Not only had I not been a professional player, I hadn’t even played NCAA D1 baseball, so I’m pretty sure I would have been playing from behind as a scout in professional baseball. Remember, this was several years before Billy Beane’s “Moneyball” and the rush of Ivy League data analytics quants that poured into front offices across MLB.

Perhaps, in hindsight, my fork in the road moment seems like less of an agonizing decision today. It’s always easier to look back and add up all the reasons why a big life decision looks like a no-brainer many years after the fact. This is how our brains work, how we rationalize and come to terms with the decisions we make.

For me it turned out to be a great decision, but I also feel that taking an area scout job would have ended up being a great decision. And that’s the point. When Yogi Berra said “take it” in reference to a fork in the road, people thought it was just another funny Yogi-ism. Actually, he knew exactly what he was saying, which is basically, just pick a path based on whatever analysis and/or gut feeling algorithm you use, and race ahead.

Ultimately, it’s not the “fork” that matters so much as what you do once on the path that stretches forward from the fork. This matters most because it’s all about how you’ll proceed each time you encounter a fork in the road, and you’ll encounter many. Just remember Yogi’s sage advice each time you reach a fork in the road: “just take 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.

What’s this one about?

The aforementioned NYT article features excerpts from a panel discussion (it’s not even a real article!) that runs this same “what’s AI gonna do to us?” script, and purports to answer one critical question. How should a college student today, or really any worker staring down all this uncertainty, prepare for the AI future of work?

As expected, the panelists bounce around from future view to future view. AI eats everyone’s work, wealth accrues to a few platforms and like 0.001% of humans, and the rest of us either coast through an AI-served life or end up in a gigantic Amazon warehouse on Mars. The theme that keeps appearing is the general advice that you should supervise agents while they work for you. But nobody gets specific, there’s no detailed takeaway that a 22-year old college grad or a 37-year old manager can execute on tomorrow.

Why is this important?

We all sort of get the deal. AI is creating a ton of economic value, well at least it’s generating a ton of capital expenditure investment. It’s just not moving the needle for you by making it easier to find purposeful work or become more productive at the job you currently have. Articles that paint our alternate futures in generalities don’t help. What helps is a specific, do-this-now description of how to create value for yourself with AI.

If you’re 22 to 55 years old, the real threat isn’t that AI shows up and makes you more productive. It’s that you don’t figure out how to make it work for you before it’s too late. “Go manage your AI agents” sounds like advice for a company with an engineering team you don’t actually have access to. The do-it-all autonomous agent world is fantasy land right now. What can make you more valuable is building a simple workflow to handle a chunk of your work that consumes a lot of time but barely uses your brain.

How can you do this?

Start from the problem, not the AI tooling. Write the goal you want to achieve this week in a simple, plainly written sentence in your voice before you touch any AI system. Something like “I want Monday’s business development pipeline status report to build itself” beats any clever prompt. That sentence is your northstar.

Next, ask your AI tool to build the simplest version, and then use it several times to decide how to improve it. AI’s shortcoming isn’t that it provides too little information, it’s that it overproduces and overwhelms. Have you ever asked something like “Can you outline a career plan for me to consider for the next 5-10 years” and ended up with a few thousand words that you can barely figure out what to prioritize? Pick one northstar, ship the rough version, and iterate in small steps from there until you have what you need.

Finally, your move depends on where you sit career-wise. If you’ve got years of experience, put it to work. An account manager who’s written quarterly reviews for fifteen years feeds an assistant her four best ones, then lets it draft each new review in her style while she adds the strategic input only she can provide. A half-day of work becomes an hour. If you’re early in your career, build the tool you wish existed. An operations coordinator two years in describes the Monday report he dreads trying to draft, has AI assemble the template, and turns three hours into twenty minutes.

The real unlock is putting these two people together. The veteran knows what “great” looks like, the newcomer moves fast in the tools, and that pairing is how to redefine the apprenticeship ladder that’s at risk of breaking inside of companies and organizations.

Now, go get something done

This week, pick one meaningful outcome and build the smallest version of it. Write a one sentence description of what’s needed, describe or input one example of “great”, and then iterate a couple rounds. Now you’ve built a workflow that proves you can create value with AI instead of just drowning in it. Do it again and again and you’ve got a real edge, the demonstrated ability to make AI work for you instead of the other way around.

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.

This past week the news hitting my inbox described a new proposal from MLB owners added to the latest round of discussions with the Player’s union (MLBPA) as part of negotiations tied to the expiration of the collective bargaining agreement (CBA) after this season.

I’m most interested in tracking this proposal forward in terms of how it impacts opportunities for baseball student-athletes at all levels, not just those fortunate enough to make it to the “Bigs”.

What is the news?

MLB owners proposed a fundamental change to how their amateur draft works. Most notable would be the requirement that players need to be 20 years old, and have been out of high school for at least two years, to be eligible for the MLB draft.

This means no more high school players being drafted by MLB teams and no more U.S.-based 18 and 19 year olds playing minor league baseball. This proposal doesn’t appear to introduce an intentional draft so players outside the U.S. can still sign at age 16 (except in markets like Japan and Korea where local players are governed by requirements tied to professional leagues in those countries).

Why would this change matter?

This new draft structure matters on a few levels. For MLB it’s another step in the ongoing effort to shift player development costs away from the minor league system and to other platforms that don’t require direct investment from MLB teams i.e.college baseball, post-grad teams, and independent pro leagues. Yes, there is a fixed signing bonus peg for each slot in the proposed 12-round draft, but this total sum may be offset by the cost savings of carrying a smaller base of minor league operations and the increased financial cash flow that stems from starting the arbitration eligibility clock two years later for those HS draftees who make it that far in MLB.

How could this play out?

So how might this change to the MLB draft impact student-athletes if these changes are included in the next CBA?

This proposal marginally impacts the flow of high school (HS) players into college baseball. Thus, given the small pool of high school draft picks, the long-term impact on opportunity creation for student-athletes will continue to rest primarily on whether we increase (or decrease) investments in playing opportunities at the HS and post HS level.

For those striving to play after HS, it will come down to how colleges at the 2yr junior college (“JuCo”) and 4yr college (“4yr”) level jointly invest, or not, in college baseball.

First, some numbers and context. In 2021, the MLB draft dropped to 20 rounds and approximately 600 players, from what used to be 50 rounds up through 2011 and then 40 rounds from 2012 through 2019, so roughly 1,200 to 1,500 players. Further, the historical split between high school versus college draftees has shifted from more than 50% high schoolers, to something closer to a third HS players and the remaining two-thirds college players, which implies 550 additional high school players have been heading to college baseball since the days of 50 rounds and a 50-50 split between HS and college draftees.

Today across JUCO and 4yr levels nationally, there are more than 1,600 teams. Using a per team player number of 35 (NCAA D1 and D2 recently moved to 34 man roster limits, but other levels don’t impose roster limits). This means there are approximately 56,000 college baseball roster spots across the U.S.

Since this new proposal means no HS players would be drafted, 200 additional HS players (20 rounds x 30 teams x 33%) need spots in college or elsewhere. But let’s say the D1 roster limits have taken away 2 players per program (it could arguably be more at the D1 level, and then more if we include the D2 impact), given the 300 plus D1 baseball programs, that’s more than 600 roster slots that have been removed. Simply put, the pre-MLB draft development system has already removed 3 times the number of new player roster slots that will be needed.

In light of these numbers, how could this change end up being a good, or not so good, for student athletes?

The good outcomes…

• Perhaps this new draft model serves as a catalyst for states to enable more of their JUCOs to offer baseball teams, and to provide access to more players by adding JV or developmental teams for freshmen.

• Perhaps more NCAA D1 and D2 programs add college baseball, and all of these college baseball programs increase scholarship slots to well more than the historical limits of 11.7 and 9 respectively.

• Perhaps more NCAA D3 and NAIA programs add college baseball, and all of these college programs increase financial support for student-athletes looking to play at these levels.

• Perhaps post-grad programs expand to offer player development opportunities and academic / apprenticeship training so these players can either continue playing at a 4yr college or enter the workforce after two years.

• Perhaps more student-athletes and parents will need access to information and guidance around the college planning and recruiting process like that provided by my nonprofit MyNextPlay.org, and others.

The not so good outcomes…

• Perhaps 4yr colleges continue to cut baseball programs (like Sonoma State and San Francisco State did here in California last year), while college football captures the vast majority of operating budget funding within college sports such that NCAA D1 and D2 get nowhere close to the roster limit numbers in scholarships, and for many, they actually don’t surpass the old limits. The reality today is that most programs aren’t even at the 11.7 and 9 full scholarship levels allowed before roster limits went into effect.

• Perhaps the number of JUCOs offering baseball stays flat, or even declines, as states grapple with funding challenges, or they too decide to limit roster sizes so only a finite number of students get an opportunity to continue developing after HS at the 2yr level – a level that for many young people supports a window for maturing physically and mentally.

• Perhaps post grad leagues expand, but rather than focusing on “access”, their charter instead mirrors that of today’s club/travel sports ecosystem. In this scenario, they too become exclusively focused on maximizing their profit & loss statements, leaving out students below specific financial levels.

The Net Net…

The proposed MLB Draft change is consistent with the trends and models professional sports leagues are moving towards to manage player development, i.e. labor costs. Ultimately it’s about narrowing the focus to the very top of the pyramid of elite players likely to ever make it onto a professional team roster.

My concern here is that changes like this don’t actually translate into creating more opportunity for student-athletes to play at the college level. If we don’t consciously invest in sports like baseball versus continuing to expand Power 4 conference college football, we’ll most certainly end up with the same “top of the pyramid” pro sports model, and the result will be that opportunities for student-athletes in sports like baseball will continue to contract.

And if we impose further roster limits and cut programs at the NCAA D1 mid-major, D2, D3, NAIA, and JUCO levels, we will only be accelerating the rate at which we’ll be reducing opportunities for student-athletes to play from HS through their college degree, therefore narrowing access to one of the most profound pathways for helping young people develop personally and professionally.

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.

How do you decide what to click into and consume? And how do you know what to do (if anything) with the content you do decide to actually spend time with beyond glancing at the headline? One framework I’ve been trying to apply is a quick “What, Why, and How” filter to the information that flows my way.

Priorities First

Before we get to these three filters, it’s important to be clear about your priorities. Specifically, name the areas you care most about and want to learn more about. These could be specific to your work, company, and industry. They can also be personal areas of interest like becoming a better parent, pursuing a healthier lifestyle, or following your favorite NBA team.

Whatever makes your list, write them down. Then work to let all the other stuff hitting your inbox whiz right by, all the “noise” that doesn’t apply to these priorities. Yes, this is easier said than done given the persistent flow of information, much of which you may not have even explicitly signed up to consume.

This prioritization effort creates your “strike zone.” Think of it like a hitter in the batter’s box. A good hitter doesn’t swing at every pitch. They know their zone, they take pitches, and hunt for the pitch they can actually drive. Your inboxes and feeds are throwing you pitches all day long, and most of them you should let go by. Define your strike zone first, and then select the “content” pitches you swing at. Once you’ve done that, you’re ready to apply the “What, Why, and How” filtering.

What?

Read the post or watch the video you clicked on and ask yourself “What is this information telling me?” Too often we mindlessly consume content that might be relevant to an area important to us, but we don’t really process what it’s telling us. We don’t read or watch in a focused way and therefore information doesn’t get imprinted in our brain.

Say you’re a product manager and you’re watching a video about the way AI is potentially changing the software development lifecycle (SDLC). Do you know what your organization’s current SDLC looks like and can you then compare what this video is telling you about how the process may be changing? After the video can you list out what new angle or elements you just learned about?

When you finish a video or an article, stop and answer one question before you move on: “What did I just learn?”

Why?

After the “What”, ask the next question, “Why does this information matter to me?” You’ve just learned something new (hopefully), so now it’s about putting it into a context that lines up with your priorities.

Stay with our product manager example. You just learned about how AI is impacting the SDLC and now it’s about reflecting on why this matters to you in your role as a PM and ultimately to why changes to the SDLC matter to your organization.

This is where you need to zoom out and revisit where the SDLC fits into the big picture for you and your company. “Why does our SDLC matter and how does it impact the long term success of our organization?” Doing this broadens the “why” question in a way that gives you the full scope you need when you then ask the “How” question.

How?

Once you’ve fully processed the “What” and “Why” questions related to that video or article, you’re ready to ask the most important question, “How do I apply this information to one of my work or personal priorities?” This is where the return on investment comes from what you just learned.

Now the product manager’s “How” question might become, “How should we change our SDLC process given the role AI might play in it?” You may even ask a more personal question like, “How does what I do in my role as a PM need to change given what AI allows me to do?”

The “How” questions are the ones that matter most. They’re where the payoff lives. All the work you put into prioritizing what matters, and all the reflection you place on the “What” and “Why,” questions exists to set up your “How” questions.

Try It Out

Give the “What, Why, and How” framework a shot, and tweak it to fit how you work. But stay focused on the key point that the “How” questions are where you earn the big gains. All the time you spend consuming content only pays off when it positively changes something you actually do. If you don’t get all the way through the “How” step you’ve just wasted twenty minutes or more consuming something that doesn’t leave a mark. However, answer the “How” question and that time spent makes you better in your work or life.

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 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.