Raising Risk-Takers

Posted on by Brendan

Every parent-teacher conference season, I walk away with the same question: what can I adjust as a dad to help my kids develop the habits and mindsets that truly serve them?

Lately, that question has centered on risk-taking. Life is fundamentally a use-it-or-lose-it proposition. If we don’t get comfortable with uncertainty early, the world quickly shrinks. Kids need calculated risks to discover what they actually enjoy, develop purpose, and build the kind of self-trust that carries them into adulthood.

The developmental research backs this up in three distinct ways:

  • Thrilling play is nature’s exposure therapy. Work from Aarhus University’s Recreational Fear Lab and developmental psychologist Ellen Sandseter’s framework on risky play show that when children engage with heights, speed, and uncertainty, they experience real physiological fear and discover they can navigate it. Surviving those thrilling moments teaches the nervous system that discomfort is temporary and manageable, acting as an evolutionary buffer against chronic anxiety.
  • Hazards cause injuries; risks build competence. A comprehensive systematic review on risky outdoor play highlights a critical distinction between a hazard and a risk. A hazard is an unseen danger a child cannot evaluate (like a rusted playground bolt). A risk is an observable challenge they can size up and choose to tackle (like a tall climbing wall). Eliminating hazards is good parenting; eliminating risks robs kids of spatial awareness and body calibration.
  • Praise dictates whether kids lean into challenges. Research highlighted by Stanford’s Bing Nursery School on Carol Dweck’s work revealed that well-intentioned “person-praise” (“You’re so smart”) actually creates risk aversion—kids start avoiding difficult tasks to protect that label. In contrast, “process-praise” (“You worked hard to figure that step out”) gives them permission to embrace harder, uncertain tasks because failure is treated as useful feedback rather than a verdict on their identity.

Knowing science is one thing; putting it into practice at the playground and around the dinner table is another. Here is what I am actively doing:

  • Ditch “Be careful.” A few weeks ago, my three-year-old was scaling a high climbing structure. My immediate dad impulse was to tell her to climb down. Instead, I walked over and stayed within arm’s reach, giving her an invisible safety net while letting her explore new heights on her own terms. Shifting away from alarmist warnings toward calm presence or body-awareness questions keeps kids in active problem-solving mode rather than panic.
  • Praise the courage of the attempt, not the outcome. When we only celebrate a clean win, kids learn that an uncertain attempt is a bad bet. I’m focusing on recognizing the grit, the attempt, and the curiosity to try something hard, regardless of whether it worked on the first try.
  • Model uncertainty out loud. Kids assume adults have everything figured out. I want my kids to see that embracing the unknown doesn’t stop when you grow up. Both personally and professionally, that means visibly leaning into things I haven’t mastered yet and openly saying: “I don’t know how to do this yet, but let’s figure it out.”

Raising confident kids doesn’t mean paving a smooth path in front of them, it means making sure they know they can handle the bumps along the way.

Stop Overlooking the Human Touch in an AI World

Posted on by Brendan

There is an unspoken tension inside fast-moving teams right now: the race for efficiency versus the need for strategic conviction.

We should be pursuing both, but I believe for most efficiency is winning at the expense of clear thinking. And that’s a problem.

If team members generate three pages of plausible-sounding output from Claude or ChatGPT and forward it directly into Slack, an email, or a shared doc. No synthesis. No curation. Just raw, frictionless generation. That’s biasing toward efficiency over clear thinkin

The problem with doing this is when you pass along raw AI output without filtering it through your own analytical rigor, you aren’t actually saving time. You’re just offloading the cognitive burden onto the person on the other end. You’re making colleagues & partners do the work of finding the signal in the noise.

I’ve been guilty of it, and I’ve been on the receiving end of it. It’s what everyone is starting to recognize as AI slop.

Using AI to draft, stress-test ideas, or break through a blank page is smart. But the work isn’t finished when the model stops generating; that’s where the work begins. The delta between the raw output you received and the refined message you actually ship is where your value lives. The human editing is where real taste, analysis, and conviction happen.

If your team is moving fast with AI, that’s great. But if you aren’t calling out the habit of forwarding unedited output as finished communication, start now.

Use the tools to accelerate your thinking but not to give up your strategic point of view reached through your analysis.

Employee Onboarding in the Age of AI

Posted on by Brendan

I am four weeks into a new job where I actually boomeranged back to the company I was at 12+ years ago. This is my first time onboarding to a new company in the age of AI since I hadn’t onboarded somewhere since 2022. 

I decided to boomerang back to Seer Interactive for two reasons. First, for its growth and learning culture. It aligns deeply with who I am to my core. And also given the time we’re in with search and broader AI technology, I wanted to prioritize learning over anything else. I knew I’d get it here. 

Second is I wanted variety. What does that mean? To me, it means the variety of what I see (i.e. challenges, problems, opportunities), who I work with, and the pace in which we’re moving. After 11+ years driving organic search strategy in-house, I knew that beat so well it felt like it wouldn’t be much variety. 

It’s been great so far for a lot of reasons but one I want to share today is because of AI. 

Onboarding to a new company that has proper AI infrastructure in place (i.e. Seer) has been amazing. The point I am making is AI clearly drives ROI for employee onboarding. Assuming the company has the infrastructure in place. 

Within the first four weeks, here’s a few AI things that have helped me ramp quickly: 

  • Daily Briefings: my daily briefing agent helps synthesize context, priorities, and what matters across my portfolio. 
  • Client Pulse: Getting up to speed on client sentiment, nuances, historical goals, and current standing quickly versus sifting through old threads and having tons of internal meetings. 
  • Quick sounding board for acronym checks, team structure and basic questions. 

All the above preserves internal team members time, lets me have more strategy conversations with people, and allows me to add value faster. 

So a faster ramp within a company that’s made the right AI investments, doesn’t just benefit the business; it accelerates an employee’s ability to lean into growth, learning, and impact on day one.

How to measure this for anyone who needs to see the numbers? 

  • Time to First Value: think of something like date of first shipped deliverable, test launch, test read. Things that help drive actual impact. Metric: Date of First Shipped Deliverable – Start Date
  • Time to Full Utilization / Standard Output: Days until the hire reaches the target output expected of a tenured peer in that role. Metric: Date Target Output Sustained for 30 Days – Start Date
  • Time and Cost of Existing Employees Training New Hires: When tenured staff train a new employee, the true cost consists of direct time plus opportunity cost (billable or strategic work not completed).

This is a clear AI ROI case to me.

Thoughts on Dots

Posted on by Brendan

This week, OpenAI announced Dots. What they called, “remarkably capable, always-on agents built to handle everything.” And Dharmesh Shah had this to say about it:

I couldn’t agree more. Whether learning, working, collaborating, or parenting, my default mode is connecting dots. Just last week in a meeting, I referenced a report template that tied directly back to an all-hands release meeting. It took a moment for the person I was meeting with to bridge that gap, but once they did, the path forward was instantly clear. Sometimes I spot those intersections faster than others; other times, teammates help me connect them.

One of my personal goals is communicating those synthesis moments more effectively. That’s why tools like Dots excite me. Not just to automate tasks, but to help scale how we synthesize and share context.

I’m going to keep learning and leveraging AI to map those patterns faster and in ways my own brain can’t match alone. My one prediction? “Dots” won’t achieve the household-name stickiness of ChatGPT, but the utility underneath it is the real deal.

Grow Google. Diversify away from Google.

Posted on by Brendan

That’s the whole strategy in six words. Both for Google and for any business that depends on it.

A month after Google I/O and Google Marketing Live, the instinct inside most companies is to react to every announcement. A ton gets announced at I/O; it’s a conference, and not everything sticks. The part of this job I love is the opposite of reacting: studying, thinking critically, and sifting the noise down to what’s actually going on and what businesses should do about it.

Where I landed is Google’s strategy unfolding now and over the next few years, runs on three moves: Protecting/Defending, Diversifying, and Exploring. I believe enterprises should mirror this in ways unique to them.

Most leaders stop at the obvious points: Google has to protect its cash cow, and it’s caught in an innovator’s dilemma. Both are true. But the I/O story is evidence of more than defense; it’s evidence of mobilizing. Protecting has short-term implications. Exploring is the 1-2 year horizon and beyond. Diversifying spans both.

Protecting / defending. Google is holding onto ad monetization for two search intents as long as it can: navigational and transactional. If and when does that break? I don’t know but it only breaks when another platform offers something good enough to make people switch. On transactional, I’m with Paul Graham’s critique, but an irrelevant ad in image search isn’t enough to move people. On navigational, Kevin Indig’s piece on the brand tax nails it and this certainly can’t keep climbing and going on forever.

Diversifying. Expect Google to push advertisers toward YouTube as a Meta alternative, maybe Discover too. Wherever it can open ad inventory across its surfaces. In my opinion, these are always worth a look, some of these may be well-priced, effective placements.

Exploring. The bets that matter most for anyone with products: Universal Cart, the Universal Commerce Protocol (UCP), AI Mode, the new Conversational Attributes schema in Merchant Center, WebMCP, and information agents. (There’s plenty more but those six are the ones reshaping the path from discovery to checkout.) I call it exploring because nobody knows yet what gains traction or how Google monetizes it (i.e. traditional ads, enterprise contracts, subscriptions). What’s obvious: Google is chasing (1) one thing social platforms have more of – time spent and (2) Share or ecommerce search from Amazon. 

So what does this mean for organic search strategy and where you deploy resources? Four principles:

  1. Differentiation: what you invest in is genuinely different from the field of competition.
  2. Experimentation: or it’s a new bet with first-mover or arbitrage upside, on Google or somewhere else. You want a mix of the two.
  3. Prioritization: you can’t chase everything. Pick the right number of bets, and remember they’re bets. AI lets you take on more at higher quality; apply the learnings, then move to the next one.
  4. Cost-effectiveness: for the channel overall and for each strategic investment. What will it cost? How complex is it? How much upside toward the channel’s total upside? How much time should the team spend?

One more thing: organic search can’t run in a silo. On the strategic bets leaders have to get teams talking, finding shared goals and common ground, to drive 1+1=3 outcomes.

There’s AI-driven revenue to capture, and I haven’t been this excited to chase something since I found SEO on Twitter in 2012. Cheers to that.