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What’s up everyone, today we have the pleasure of sitting down with Simon Lejeune, VP of Growth at Wealthsimple.

Summary: Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains by deleting work that doesn’t matter, cutting projects when the lift is negligible, and measuring true incrementality with one simple question: “What would have happened if we didn’t do this?” He believes AI is accelerating this shift, turning deep channel expertise into a commodity and making curiosity, speed, and ruthless prioritization the real competitive advantages. Growth, in his view, belongs to teams who can abandon the comfort of optimization and pursue experiments big enough to change the trajectory.

In this Episode…

• How to Escape Local Maximum Traps in Growth Marketing
• Productive Laziness Mindsets
• Balancing Clean Experiments with Bold Bets
• How to Approach Incrementality Without Large Data Sets
• The Best Use Cases for Incrementality Tests
• How to Handle ROI Conversations Without Slowing Down Growth
• Why Most A/B Testing Is a Waste of Time
• When Natural Language Becomes the Interface, Channel Expertise Stops Being a Moat

About Simon

Simon Lejeune is a seasoned growth leader with over a decade of experience scaling some of North America’s most recognized tech brands. Currently VP of Growth at Wealthsimple, he drives client and asset growth across products like Trade, Crypto, Cash, Invest, and Tax.

Before that, Simon founded Mile End Growth, a boutique agency delivering strategy, creative, and media buying for startups, and led user acquisition at Hopper, where he managed multimillion‑dollar budgets and built one of the most sophisticated in‑house ad automation engines in travel tech. His career began at Busbud and Nomad Logic, where he directed growth marketing and developed new revenue‑generating spin‑offs.

Local Maximum vs Global Maximum

How to Escape Local Maximum Traps in Growth Marketing

A local maximum trap happens when teams keep optimizing small features that look like wins but cap long-term growth. Simon uses the metaphor of being blindfolded on uneven terrain. You walk in every direction until each step feels lower, then assume you have reached the peak. When you take off the blindfold, you see you are standing on a hill while a much larger mountain waits in the distance. Many growth teams spend months, sometimes years, stuck on those hills.

Simon experienced this lesson in an uncomfortable way. During his final interview at Hopper, CEO Fred Lalonde asked him what he would change first to grow revenue in the app. Simon answered with what felt like a logical idea. He suggested testing different price points for the $5 tip option, maybe $4 or $6, to find the best revenue point.

“He looked at me and said, ‘That’s literally a local maximum, and I do not want you doing that,’” Simon recalled.

That feedback forced Simon to change his perspective. He proposed a more radical idea: building a separate app that would use Hopper’s flight data to surface ultra-cheap Ryanair-style deals under five euros. It sounded risky and unconventional, but Lalonde loved it. Simon left that meeting understanding that real growth often comes from bigger, more disruptive ideas that challenge the current model instead of refining it.

Growth teams can apply this lesson by actively questioning whether their experiments drive material change or simply polish what already exists. Regularly evaluate whether you are optimizing features, pricing, or flows when the real opportunity may be entirely new product lines, bold pricing experiments, or acquisition channels that look nothing like what you use today.

Key takeaway: Incremental optimizations create comfort but rarely drive exponential growth. Audit your current priorities and identify one experiment that pushes far beyond incremental gains. Focus on ideas that reimagine your product, acquisition model, or customer experience. That way you can escape local maximum traps and open paths to growth that small experiments will never reach.

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Productive Laziness Mindsets

Simon challenges his team to delete more work than they refine. “The fastest way to do something is not to do it,” he said. He encourages what he calls “productive laziness,” which means questioning why a task exists before sinking hours into improving it. Many growth teams fill their calendars with recurring meetings and busywork that provide comfort but little actual impact. Simon wants his team to hunt down and remove the 80 percent of work that clogs up progress.

“You could probably not do 80 percent of what you’re doing, and you need to take the time to find it.”

He distinguishes between local and global maxima. Local maxima are small, incremental wins that stack up over time. They create efficiency, but they rarely transform outcomes on their own. Simon shared a story from Wealthsimple where his team used a simple AI-driven prompt to quickly generate FAQs for new promotions. It only improved one process by a few percentage points. Combined with other similar fixes, it meaningfully reduced the time from campaign idea to launch. These small process wins compound into real operational speed.

Simon points out the trap many teams fall into when these tweaks become the entire focus. He calls out A/B testing as the classic example. The industry celebrates small lifts in conversion rates, yet the promised gains rarely translate into significant growth. “If you’ve had that many wins,” he said, “your conversion rate should be 300 percent by now.” Experienced operators know that most of these improvements exist only in reports, not in the top-line numbers.

He pushes for a balance between incrementalism and bold redesigns. Teams need to ask hard questions: Does this process deserve to exist? Does this experiment meaningfully impact the business? Would rebuilding this system create more value than optimizing it? Those are global maximum questions, and they require a willingness to break away from the comfort of small wins to pursue something transformative.

Key takeaway: Use incremental process improvements to accelerate execution, but regularly pause to audit whether the work itself creates meaningful business impact. Remove tasks, experiments, or processes that do not clearly connect to growth. That way you can free up the capacity to pursue global maximum opportunities that drive measurable, lasting results.

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The Psychological Trap of A/B Testing

Local maximum thinking happens when teams equate motion with progress. Simon describes this as the pattern of chasing a string of small A/B test wins while nothing meaningful shifts in the business. Every tweak produces a minor lift, enough to justify the next experiment, yet the bigger picture remains stagnant. The illusion of progress feels convincing in the moment, which is why so many teams stay stuck in it.

He experienced this firsthand at Wealthsimple. The team wanted to answer a straightforward question: should they push new users toward onboarding in the mobile app or let them sign up on the web? The data pointed to app users having higher long-term value, so they poured resources into testing everything from large QR codes to “text me the app” prompts to blocking web sign-ups entirely. Each experiment delivered a bump, but the overall impact on growth was negligible. Simon eventually cut the project.

“I just asked myself, why are we doing this? If someone wants the app, let them click. If they want to sign up on the web, let them do that. In the end, it was better to just ignore it.”

Simon has seen how addictive A/B testing can be, especially when dashboards in Mixpanel or Amplitude deliver real-time feedback. That dopamine hit makes small wins feel like major victories. At Hopper, he obsessed over Snapchat growth and app store page optimization, then noticed how giants like Airbnb and Uber barely touched their app store pages. They focused on big levers, not endless micro-iterations. It forced him to confront how easy it is to confuse effort with meaningful progress.

The way out of this trap is to ask sharper questions. Are these experiments moving metrics that actually matter? Are the gains substantial enough to justify the time and resources? Are you choosing comfort over bold bets? These questions expose when your team is stuck tinkering on a hill instead of climbing a mountain.

Key takeaway: A/B testing creates the illusion of momentum. Step back often and evaluate whether your experiments change metrics that truly drive growth. Redirect resources from small, comfortable optimizations to bold, high-impact bets that can meaningfully shift your trajectory.

Balancing Clean Experiments with Bold Bets

Data teams often pursue the cleanest possible experiments. They want one variable at a time, airtight setups, and statistically significant results. That makes sense when the goal is to refine a single feature, but it can hold teams back when the ambition is to unlock major growth. Simon has operated inside those constraints and learned where to push.

“When they tell you that you can’t run two experiments at the same time, my response is usually: yes, you can. I just did it,” he says.

Simon focuses on extracting meaningful signal, even when the experiment design is messy. He describes himself as data driven while relying on logic and conviction to guide his decisions. “Things have to make sense,” he explains. He does not wait for perfect A/B test conditions before shipping changes that improve the client experience and set up long-term upside. This mindset opens the door to ideas that never make it past a rigid testing framework.

“Sometimes you launch an A/B test, and there’s no impact. If it makes sense for the client, I’ll leave it there,” Simon says. “Even if the short-term metrics dip, I have conviction that it creates value over time.”

Simon uses data to challenge his instincts, not to limit his decisions. He pays attention when numbers surprise him because those moments force him to reevaluate his assumptions. This balance allows him to pursue bold ideas while maintaining a healthy respect for evidence.

Growth depends on more than running tidy experiments. It comes from making thoughtful bets, staying open to what the data teaches, and pushing forward with ideas that clearly serve customers even if the dashboards lag behind.

Key takeaway: Data shouldn’t be a barrier to growth ideas. Run tests that give you enough signal to learn, even when they are imperfect. Ship changes that create clear value for customers, then use analytics to refine and adjust. That way you can act with confidence while avoiding analysis paralysis and still ground your biggest bets in reality.

The Importance of Understanding Incrementality

How to Use Incrementality to Measure Real Campaign Impact

Incrementality means measuring what your campaign truly caused, not just what happened during the same period. Simon Lejeune frames it with a simple, almost instinctive question: “What would have happened if I didn’t do this test?” That question is the difference between parroting performance metrics and understanding real impact.

“What would have happened if I didn’t do this test?”

Simon uses Black Friday as an example. If you drop prices by 10 percent and see sales climb by 25 percent, most marketers report the lift as a campaign win. Yet experienced operators dig deeper. Did non-discounted items also see a bump? Would sales have increased anyway due to seasonal traffic? These questions uncover the actual value of the campaign. Vanity metrics are easy to share. True incrementality forces you to separate noise from impact.

Simon uses this thinking to evaluate people as well as campaigns. When hiring, he asks candidates to explain their results from first principles. Many fail to go beyond surface metrics. The strongest candidates quickly consider market conditions, unrelated drivers, and baseline performance. He says this instinct reveals how someone approaches growth. It distinguishes campaign executors from those who build meaningful, lasting business outcomes.

At Wealthsimple, Simon has turned this into an internal habit. He jokes that it has become a meme: whenever a team shares results without incrementality analysis, he replies in the thread with one question, “Okay, but what’s the impact?” This forces the team to move past participation metrics and discounts that buy temporary spikes. It creates a culture where people challenge assumptions and measure what really matters.

Key takeaway: Always measure impact by asking, “What would have happened if we didn’t do this?” Bake this question into your reviews, reporting, and team culture. That way you can cut through inflated numbers, isolate actual business growth, and make decisions based on what truly drives change.

How to Approach Incrementality Without Large Data Sets

Incrementality delivers huge value when there is scale. A large customer base allows even subtle changes to compound into measurable growth. In smaller environments, that same playbook creates frustration. The data is too thin, the lift too fuzzy, and the measurement frameworks become overcomplicated distractions.

Simon Lejeune argues that growth in these conditions depends on motion, not models. “You’re looking for bigger swings or sometimes just trying to get by,” he said. When you are still proving out your market or building an early pipeline, hitting the numbers matters more than running pristine holdout tests. He described those moments as a scramble to build momentum, one experiment at a time.

“I’d rather ship it with no data than spend days debating the framework,” Simon said.

He has watched teams grind to a halt while chasing perfect attribution structures. His bias is toward shipping quickly, then tracking obvious directional changes. That means launching the product update, running the campaign, or rolling out the offer before spending hours debating whether the experiment meets a textbook definition of incrementality.

Larger budgets make traditional methods more practical. Simon points to owned channels like email or in-app notifications as easy places to run clean holdout tests. Paid campaigns with meaningful spend can justify geo-holdouts. Until then, he suggests keeping measurement simple: pre-post comparisons, visible chart movements, and straightforward assessments of impact.

Key takeaway: Incrementality for small teams should focus on action and directional clarity. Ship fast, check whether performance visibly improves, and save the complex frameworks for when you actually have the volume to use them effectively. That way you can maintain momentum, hit your immediate goals, and build the foundation for more advanced measurement later.

The Best Use Cases for Incrementality Tests

Incrementality testing often feels like a badge of sophistication for growth teams. You design an elaborate experiment, hold back spend, and hope the numbers finally tell you the truth about a channel. Simon Lejeune challenges that mindset. He says the biggest cost of these tests is time.

“It’s a race against default death,” he explains. Startups have a limited runway to prove their model, and long, resource-heavy tests can quietly consume months that could have been used to build, ship, or scale.

Simon treats incrementality as a tool for specific moments rather than an ongoing ritual. He runs tests when they answer critical questions, such as:

• Are we pouring millions into a channel that might not be working?
• Is a surprising performance trend worth verifying before doubling down?

“If we’re spending millions on a channel and I have doubts about its contribution, that’s when it’s worth running a one‑time test,” Simon says.

He also challenges the fantasy of a perfect attribution system that provides real-time clarity. Growth teams do not operate with fully harmonized dashboards and flawless media mix models. They work with incomplete data, changing conditions, and human judgment. Incrementality testing builds conviction for high-stakes decisions, but Simon refuses to treat it as a permanent fixture in the playbook.

His strategy is straightforward. Run incrementality tests when there is meaningful doubt or exposure. Use the results to make decisive budget calls. Move on and avoid retesting unless market conditions shift or strategy evolves. That way you can protect runway, keep momentum, and reserve experimentation for moments that truly matter.

Key takeaway: Run incrementality tests only when they answer questions that could materially change your strategy. Use them to validate high-spend channels or suspicious performance trends, make clear decisions based on the results, and keep testing rare enough that it never slows your path to growth.

How to Handle ROI Conversations Without Slowing Down Growth

Speed decides whether your growth team makes progress or gets stuck in its own processes. Simon has seen this play out in every stage of a company’s growth. When leadership prioritizes shipping and customer feedback, teams can act with urgency and deliver results that compound over time.

“The real head of growth at a company is the founder,” Simon said. “Growth has to be driven by the founders, and usually it is because they got this thing off the ground against all odds.”

When founders create an environment where speed is celebrated, growth teams can focus on execution instead of building decks for the board.

The tension comes from executives and finance teams who want predictable answers. They ask for clean payback periods, precise ROI models, and quarterly contributions. Simon used to fight those requests and learned it drags conversations out for weeks. Now he frames it differently. “You can’t just be the knucklehead that doesn’t care about any other team,” he explained. They need those models to keep the business moving in their world, just as you need momentum in yours.

“If I hear at Wealthsimple, ‘Sorry we didn’t have time to measure this because we wanted to ship it to the client,’ the response is, ‘Amazing, great.’ You want leadership who reacts like that.”

Simon’s answer is to meet them halfway. He gives executives simplified models that provide directional clarity without consuming weeks of analysis. He uses lightweight post-analysis instead of heavy incrementality studies when decisions need to move forward. He negotiates expectations upfront with stakeholders so everyone understands the level of detail required. These small adjustments keep teams aligned without turning reporting into a full-time job.

Healthy growth cultures celebrate shipping and reward teams for delivering value to customers quickly. Reports and models serve the work rather than dominate it. Simon believes growth teams thrive when founders champion this mindset and executives trust directional data as enough to keep decisions moving.

Key takeaway: Create lightweight, directional models that satisfy executive reporting needs without slowing execution. Align on expectations early, keep your reporting as simple as possible, and return to shipping quickly. Build a culture where leadership rewards speed and customer impact so you can balance accountability with real momentum.

Why Most A/B Testing Is a Waste of Time

A/B testing dominates growth discussions, yet Simon Lejeune questions how most companies use it. He sees endless experiments on low-stakes details that fail to create meaningful impact.

“Probably 80 percent of the A/B tests out there make me wonder, why are you even testing that?” Simon says.

For a company aiming to become the largest financial institution in Canada, small copy tweaks and button color changes are distractions from work that actually drives growth.

Simon applies A/B testing when the stakes are high. He looks for experiments that change user behavior in measurable ways. For onboarding, this means challenging the entire flow instead of trimming screens. He imagines onboarding that requires clients to speak with a real person, or stretches into 60 questions to deepen engagement, similar to what Noom did when it surprised the industry with its longer, more personal sign-up process. These ideas sound unconventional because they are, yet they create the potential for breakthroughs that tiny optimizations never reach.

This thinking also shapes his paid acquisition strategy. Simon questions why teams keep pouring millions into the same platforms instead of creating entirely new channels. He has tested incentive campaigns like offering Apple products and sees even bigger opportunities.

“What’s better than a free iPhone? Taylor Swift tickets,” Simon says.

He imagines replacing cash referral bonuses with access to cultural moments, like Revolut did with its Charli XCX concert in London. These kinds of initiatives make people talk, build deeper brand connections, and can outperform traditional ad spend when done right.

Simon uses a simple filter for deciding which big ideas are worth pursuing. He shares them casually with colleagues to see how fast they spread. When finance, CX, and product teams reach out wanting details, he knows the idea has momentum. That signal tells him which concepts deserve deeper modeling and prioritization, and which ones belong in the notes app for later.

Simon laughs at how these kinds of ideas can terrify more traditional marketing ops teams. Going from testing button colors to planning a “Swifty” promo with concert tickets feels chaotic to anyone who prefers predictable playbooks.

He recalls that now infamous iPhone giveaway, their most successful promotion to date, which required shipping tens of thousands of devices and building an entire logistics operation from scratch. There were complaints about missing packages, customers chasing down deliveries, and plenty of operational headaches. Yet over time, the team’s mindset shifted. The people who once dreaded these experiments started to lean in, asking what the next big idea would be. Simon believes those are the people you want around you; the ones who embrace the chaos and find energy in creating something entirely new.

Key takeaway: Use A/B testing for decisions that can reshape user behavior or redefine acquisition, not for cosmetic adjustments. Invest in experiments with the potential to create new growth channels, like cultural events or bold onboarding strategies. Test an idea’s internal traction by sharing it widely and watching how fast it spreads. That way you can focus resources on initiatives with real momentum and measurable upside.

When Natural Language Becomes the Interface, Channel Expertise Stops Being a Moat

AI is Making Channel Expertise Less of a Moat

AI is breaking the old molds of team structure in growth. Simon draws a clear parallel to when Facebook and Google automated large parts of ad management. Before automation, you needed engineers building bidding scripts and specialists running hundreds of campaigns manually. Then automated campaigns arrived, and a single operator with the right creative and inputs could handle budgets that used to require an entire team. That automation moved the competitive advantage from operational complexity to creative strategy.

AI is now extending that transformation across nearly every channel. Tools can generate UGC-style ads with a single click, create dozens of creative variations instantly, and draft copy that performs at a high level. Simon predicts that once everyone can produce this level of work quickly, the real differentiator will be who has the most original ideas and who moves fastest.

To prepare for this, Simon reorganized his team at Wealthsimple. Instead of fixed channel roles, teams now align with measurable outcomes such as growing client numbers, increasing assets, or driving direct deposits. Every team member is expected to adapt across functions. They might write SQL queries one day, launch a paid social campaign the next, and test an in-app experiment the following week. AI removes the steep learning curve for these tasks, giving curious generalists the ability to perform like specialists.

Some team members initially resisted this model because they had spent years developing deep expertise in one channel. Simon reframed their role around a broader mission: growing the business. He encourages high-agency work where people do not wait for approval to try new things, provided they act responsibly within the constraints of a financial services company. This mindset has helped younger team members in particular develop quickly and build a skill set that will make them valuable in any growth environment.

At Wealthsimple, employees now use an internal LMS packed with models, including a private LLM that behaves like ChatGPT without sending data outside the company. This makes experimentation safe and accessible. Simon wants everyone to be able to jump in and try things, whether it’s building automations or running simple campaigns, instead of waiting for a specialist to approve or execute. He has seen how this access creates momentum, where people learn by doing and lean on each other for quick feedback.

“I hate gatekeeping. If someone wants to build an email or run a Facebook ad, they should be able to get in there and try.”

That mindset has reshaped their culture. Simon encourages knowledge-sharing across teams instead of hoarding expertise. If a product marketer needs an email, they do not have to wait for growth to get around to it. They get access, they build, and they ask for help when needed. This kind of environment rewards curiosity, creates little micro-moments of collaboration, and pushes people to get better at using the tools themselves. It is messy at times, but it works. More teams are discovering that giving people real access to tools builds skill and speed in ways that rigid ownership never could.

Key takeaway: AI is eliminating many of the old advantages of deep channel specialization. Build teams around business outcomes rather than functions, and hire adaptable generalists who thrive in multiple disciplines. That way you can create a culture where speed, experimentation, and big ideas matter more than rigid expertise.

How Natural Language Interfaces Are Changing Martech Operations

Natural language interfaces are changing how marketing teams use their tools. Simon Lejeune predicts a future where marketers will not submit detailed tickets for complex workflows or wait on an operations team to translate their ideas into technical execution. Instead, they will log into Braze, MoEngage, or AppsFlyer and write simple, conversational commands like, “Send an email to clients in Toronto who have not used their card in seven days. Include a promotion from this ice cream vendor and generate a preview before it sends.” The tool handles the execution. The marketer focuses on deciding what matters and shaping the campaign’s strategy.

“These tools better get going,” Simon said. “Because the future is me going in and saying exactly what I want in plain language, and the tool builds it for me.”

AI takes this further by giving every idea a polished presentation. A stray thought during a walk can become a fully developed plan in minutes. You can ask an LLM to explore risks, model potential outcomes, and format everything into a clean one-pager that looks investor-ready. These ideas gain an unearned weight because they read like final strategies. In the past, weak concepts would quietly die in a notebook. Now they get promoted to your roadmap unless someone stops them.

Production speed no longer limits execution. Campaigns that once took months now take days. That creates a different constraint: the attention of your audience. You cannot multiply their willingness to engage no matter how quickly you produce campaigns. This means marketing teams need a more deliberate approach to choosing which ideas get built.

Simon frames prioritization as the core skill of future martech operators. Tool-specific expertise expires quickly. Creative problem-solving and disciplined filtering of ideas are what sustain growth. Operators who can sort through dozens of credible, data-backed proposals and identify the few worth pursuing will define the next generation of high-performing teams.

Key takeaway: Use natural language interfaces to speed up execution, but anchor your work in ruthless prioritization. Before building, ask: Does this campaign truly earn a place in front of the customer, or does it only look good in a polished one-pager? That way you can protect your audience’s attention and invest energy in fewer, higher-impact ideas.

How to Use Game Thinking to Stay Energized in Growth Roles

Simon frames growth work as a massive, turn-based strategy game. He imagines his role with resources to manage, a clock ticking, and opponents making moves of their own. This framing changes how he experiences the grind of high-stakes growth roles. Shipping a campaign feels like making a play. Refreshing the dashboard feels like checking the score. That way you can bring curiosity to the work, even when the pressure is relentless.

“I see this as a game. I have resources, I have a timer, I have opponents. I like to play this game. I want to see the results, turn by turn.”

He also builds a mental buffer around the job. He pours energy into ambitious goals, yet he remembers to keep perspective on what the work actually is. Leading growth at a fintech startup can feel like a fight for market dominance, but Simon grounds himself by seeing it as one piece of a much larger life. This mindset gives him the freedom to take the work seriously while protecting his sense of play.

For operators who feel trapped by pressure, this reframing is practical. It helps you:

• Stay curious instead of cynical when projects stall.
• Treat experiments as progress, not punishment.
• Build resilience because every result is just one move in a longer match.

Simon’s framing creates room for experimentation, keeps him engaged through setbacks, and helps him execute with more energy. It turns complex growth challenges into an ongoing match that rewards creativity and persistence.

Key takeaway: Treat your role like a strategy game. Frame every project as a move, see results as immediate feedback, and keep perspective on what matters. That way you can sustain curiosity, reduce burnout, and approach your work with the energy needed for the long haul.

Episode Recap

Simon Lejeune still remembers the moment he learned to stop chasing small wins. In his final Hopper interview, he pitched a price‑point test on their $5 tip option. CEO Fred Lalonde didn’t flinch. “That’s literally a local maximum, and I do not want you doing that.” It hit hard. Simon walked out understanding that real growth doesn’t come from tweaking what exists, it comes from ideas big enough to shift the entire model. At Hopper, that meant proposing a separate app for ultra‑cheap flight deals. At Wealthsimple, it has meant reorganizing teams and pushing for experiments that actually change the trajectory of the business.

He wants his teams to spot when they’re stuck polishing the same hill instead of climbing a new mountain. That means deleting more work than you refine, cutting projects when the lift isn’t meaningful, and using a simple question to measure impact: “What would have happened if we didn’t do this?” That single question turns fluffy campaign reports into real insight, forcing teams to separate noise from actual incrementality.

AI has only sharpened this philosophy. Simon has seen how natural language tools turn once‑specialized tasks into something anyone can do. You no longer need a dedicated ops specialist to build a Braze journey or a media buyer to tweak campaigns when a single line of plain‑language input can do the heavy lifting. The moat isn’t deep channel expertise anymore. It’s curiosity, speed, and the ability to prioritize which of the dozens of plausible ideas are worth your team’s attention.

Simon’s story is a reminder that growth lives in motion and boldness. Stop stacking tiny wins. Start making moves that feel uncomfortable enough to matter.

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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