We may not all agree on how fast things will change or who it will benefit first, but we had one prominent alignment this year, consistent for everyone on the team:
Direction matters more than speed.
In 2025, we saw Los Angeles on fire, we confirmed 2024 as the world’s hottest year yet, Pope Francis died the day after a visit from JD Vance, Los Angeles went from on fire to on protest over the topic of mass deportation, the first fatal crash of Boeing's flagship Dreamliner occured 40 seconds after takeoff, Central Texas experienced one of the most horrifying floods, the Russian coast saw the sixth-largest earthquake ever recorded, Charlie Kirk was assassinated, and the word TARIFF was the most prolific word in US media, having been used a grand total of 621,118 times. (We’re not sure if that last one is actually true)
Is this the end of the year or the end of the world?
It wasn’t all doom and gloom though, because we humans flawlessly landed the first commercial spacecraft on the moon, and a two-time Olympic gold medalist from Zimbabwe was elected as the 10th President of the International Olympic Committee, becoming the first woman and the first African to lead the organization in its 130-year history.
We’ll take any wins we can get.
One of the clearest patterns this year was the conflict of progress vs. stagnation. Driven by a year of economic uncertainty, the momentum of existing and emerging businesses is becoming uneven, and the dividing line increasingly runs between those who can work fluently with AI and those who cannot or choose not to.
The result is a K-shaped economy, where one segment continues to accelerate while another struggles to keep pace. This divide is already visible in hiring, wage pressure, and career mobility, and it’s unlikely to smooth itself out anytime soon.
At the same time, attention-based industries are under strain with 41% percent of 2025 consumer sentiment claiming streaming subscriptions aren’t worth the price, and almost half (47%) saying they pay too much right now.
That’s a considerable amount of fluctuation in questionable revenue for every provider.
We see creators of all ages who once relied on revenue from content platforms like YouTube and social media now fragmenting across more channels. From brand sponsors on Instagram and Vine reviews on Amazon, to being paid one-to-one by fans via Patreon, Beehiiv, and Substack.
It all shows enough interest to pay for more personalized subscriptions in exchange for entertainment, knowledge, and content beyond those platforms.
Prediction: As knowledge gaps fragment and monetization becomes harder for businesses and creators to sustain, the broader economy will continue to divide in the near term.
And the creator economy may be heading toward a period of consolidation that could include new platforms or updates to existing ones that consolidate subscriptions by providing access to multiple creators, much as Spotify, MasterClass, Netflix, and other predecessors are already doing at scale.
The question is: How will we meet in the middle where everybody wins?
No longer just a soft skill, adaptability is the unspoken hiring trait now critical to operations.
New tools are arriving faster than most teams can evaluate them (nothing new), which continues increasing tension and initial resistance. If resistance isn’t quickly followed by inevitable adoption, employees will be left in the lower half of an expanding K-shaped economy.
Once new tools shift from novelty to infrastructure, they compound value. As AI adoption becomes the baseline expectation for nearly every industry, those who remain on top of the tech while integrating it into everyday workflows will be better positioned to respond to change. But that learning must be allowed to take place.
Prediction: Companies that focus on Career Development will outpace competitors in the long term. Hiring will become even more competitive in the short term.
For individuals, the interview process will rely more and more on methods to validate a person’s adaptability before hiring takes place. And for business, proposal processes may rely on similar validation means. This could lead to longer hiring periods and stricter guidelines that (hopefully) decrease churn and undereducated hires.
As toolsets grow more powerful, outcomes depend less on raw talent and more on execution systems. New platforms (and automations) often come with learning curves that offer durable advantages once understood and implemented. In other words, teaching people how to fish should be a constant reminder.
Structured workflows, planning buffers, and repeatable production processes are becoming foundational for teams managing multiple projects. And how many people or teams do you know that are working on just a single project these days?
The operational AI solutions market is on a tear and only continuing to grow. Which is no surprise when speed without structure tends to collapse under its own weight. This is where we see a lot of startups stall unless they can manage operations as well as product development.
Whether B2B, D2C, or otherwise, we’re all in the same boat here, and this boat can’t steer itself (yet). We need structure, regardless of the tools, to achieve scalable growth at a faster pace.
Prediction: In the nearterm, operational infrastructure will rise in focus and cost, while creating a larger and more segmented ops software ecosystem. That means more subscriptions or custom app development that solves specific combinations of operational efficiencies for every type of business. Software engineers will continue to be highly sought after in the near term.
Even with the rate of accelerating technology, we find moments of real impact still come at least as much from alignment of human craft combined with real direction, and if luck has it, perfect timing. Therein lies the holy trinity.
“Lightning in a bottle” outcomes are difficult to achieve without creative strategy and execution actively shaping the work regardless of the level of AI involvement in the process.
But that also requires time, which means we’re forced to either 1) adapt to faster production or 2) prove value in longer timelines.
Resilience is no longer separate from professional capabilities. With the acceleration of change comes the acceleration of failure, and learning therein. This has become so commonplace in the decades leading up to now that it’s now fully baked into operations, and the ability to “manage under the pressure of condensed timelines” has become an unspoken expectation of every job role.
That creates an obvious emotional and mental strain for everyone, from interns to executive leadership, on how most companies in the public and private sectors make decisions and sustain output. It’s no wonder that over half the country is stressed over job insecurity.
We’re witnessing AI commerce moving quickly from concept to infrastructure. Shopping research and purchasing are increasingly embedding themselves directly into conversational chat. From brand awareness and discovery to customer acquisition, it’s impacting every part of the marketing funnel, all the way to the retention experience.
That requires a lot of trust, another commodity that becomes harder to achieve in an environment where AI-generated content continues to raise questions about authenticity and intent.
So now we face this irony of our desire (and dependency) on AI accuracy, versus our lack of trust in its results (hallucinations and slop) and its ability to produce results faster.
We want to believe AI search results are summarizing correctly. Or deep-research mode is pulling data from accurate sources. Or vibe coding is using the most efficient methods. Or image, video, and sound generation will produce accurate results.
But it all takes extra time to validate the results of a technology still in its infancy.
Provided the act of “AI production + human validation and fine-tuning” helps us achieve faster results than human input alone, it’s still an extra process we rely on with no clear timeline for when it will phase itself out.
Prediction: The first company, AI or otherwise, that can confidently solve the majority of trust barriers, will leapfrog into pole position. And be immediately acquired if not already one of the few key players.
AI clearly expands what individuals and small teams can accomplish, but the K-shaped economy shows how it also accelerates inequality and the fear of forced retirement.
The narrative around “AI employees” and fully automated companies is no longer subtle, and job displacement is already affecting creative, administrative, and support roles. This concern mirrors broader labor projections that show, on average, workers can expect that two-fifths (that’s nearly 40%) of existing skill sets will be transformed or become outdated over the 2025-2030 period.
Unless incentives shift around the practice of replacing humans with AI solutions in big business, that fear will continue to be warranted. And with it will come more and more common (read: bland) results.
As creatives, we see a lack of judgment and originality everywhere, all the time. And yet, we also see how our AI collaborative tension produces stronger outcomes within our industry when we have access to tools that allow us to continue our capitalistic endeavours, whatever they may be.
Prediction: Reducing human involvement may increase short-term efficiency, but it risks unpredictable long-term loss and a predictable, unfortunate lack of originality. There are no clear answers on this one yet.
When we look at the top side of the K-shaped economy, it’s made up of those with the time and resources to spend learning the infinite amount of new tools that are becoming so easily accessible. They can afford to experiment, fail, and learn the tools becoming embedded across industries.
The downside we’re already seeing is a landscape saturated with questionable output, where the origin of the content may gain more value even if it continues to impact displacement. When systems optimize relentlessly for efficiency, people tend to push back.
And we see it everywhere from slow food movements to a backlash on fast-fashion and ongoing appreciation for handmade works from television commercials to museum collections.
Prediction: We’re watching for a cultural counter-movement in which corners of the internet intentionally restrict AI-generated content, and human-made work becomes more desirable and exclusive.
A lot has happened in the past year, and so much more is on the way. We’re currently fighting the gaps between capabilities and trust, while readiness without alignment continues to amplify any existing fractures or imperfections.
All signs point to a very familiar challenge; technology continues to move rapidly, and humans continue to respond unevenly. But the details and the pace of accessibility are what to watch for.
To put it another way, we’re drawing the roadmaps while driving down the side streets at 120mph alongside a growing number of adolescent drivers convinced they know the quickest routes.
Sound scary?
It is.
But in the end, at least for now, direction matters more than speed.
Just don’t be the one who’s afraid to ask for directions.
