How Macro Forces in 2025 Reshaped My Thinking
Image Details: My Summer Experience in San Francisco, CA
Introduction
As we celebrate the commencement of 2026 and reflect back upon the rollercoaster of a year 2025 was, several major events, trends, and disruptions come to mind - both at a macro scale and personally. When looking at broader geopolitics, 2025 marked a major paradigm shift in both the US and around the world. Geopolitically, the year began with immense market volatility - shaped by American foreign policy, wars, and a stronger than ever AI-arms race between the US and China. From broader inflation fears (most of which was never truly recognized) to initial worries of economic downturn (reflected by US 2025 Q1 GDP growth) to the broader impact of supply chains globally, these shifts not only impacted broader policy but also touched each and everyone of our lives.
Technologically, we witnessed the sharp rise in AI - both in terms of valuation and technological capabilities. ChatGPT, although a first-mover with regards to the technology, now faces broad competition from various domestic and international players (Anthropic, Gemini, Perplexity, Deepseek, etc.). Among new grads like myself, a fear of a future that is fully automated, with limited opportunities, mobility, and a diminishing social construct rings true. Yet, simultaneously, there is a renewed sense of optimism that we are in a time of immense opportunity to leverage technology in making the world more equitable and democratized for all.
2025 was a time of immense paradox - fear vs optimism, efficiency vs automation, protectionism vs innovation, wealth vs poverty, etc. The K-shaped economy is alive and well, as the main question seems to be not whether or not emerging trends will benefit the world, but rather who it will benefit. In the midst of all this uncertainty and pessimism, however, here are some of the key predictions, trends, insights, and lessons I have noticed that I believe will define 2026 and beyond.
2025–2026: The Paradox Meter
How opposing forces simultaneously intensified.
Economic Disruption Will Continue In 2026
As of December 2025, unemployment rates ticked up to 4.6%, marking an increase from the 4.1% earlier this year. While this is not an alarming increase, it goes to show the impacts of a “no hire, slow fire” environment that we seem to be in. A lot of this can be blamed on tariffs, but a significant portion of this disruption can also be put on broader bets by enterprises on automation - which is especially impacting white collar job growth. The US consumer is divided immensely, with older and wealthier individuals experiencing immense prosperity from a booming stock market, stable jobs, and skyrocketing retirement accounts. Meanwhile, young professionals and new grads are facing the brunt.
This trend, in my opinion, is very likely to continue in 2026 with one small exception. This leads me to my second prediction.
US Stock Market to Remain Relatively Stagnant
Unlike the AI-arms race that fueled valuation growth in 2025, my prediction for 2026 remains far more stable. While an outright recession is unlikely, it’s very possible that real-economic growth will stall to near-zero. Unemployment may hover somewhere in the 4.8-5.4% range, with inflation remaining between 2.4-2.8%, and the S&P 500 being between 6700-7300 a year from now. Corporate earnings growth will remain strong, especially in emerging technology and AI-driven sectors, however, the growth will be a noticeable slow down from the 2023-2025 boom period. Meanwhile, the consumer discretionary sector will experience weak and/or declining earnings as the K-shaped economy impacts overall consumer spending, and a stagnant stock market causes high earners to pull back.
2026 will not be a year of a boom or bust, but rather a year where hype stabalizes and expectations revolve around long-term investment into technological growth, rather than short-term optimism into AI hype. That said, while adoption expectations may take a hit with regards to AI, the development of the technology itself will continue to steamroll ahead.
AI Divides The Market
My predictions around AI’s corporate/industrial use cases revolve around three types of players.
Fully Autonomous Enterprises (1% -> 5%): These companies will continue to push, in 2026, for as much automation as possible - both in their internal processes as well as the products they sell to their clients.
Hybrid Enterprises (50%-> 80%): These companies will prioritize AI as a defining technology to aid human-driven efficiency, leading to models built around human trust and scalable architecture.
Legacy Model Enterprises (49% -> 15%): These enterprises will heavily exist in highly regulated industries (Healthcare, Biotech, Financial Services, Forensics, Legal, Audit, etc.). Despite massive market incentive to adopt AI technology, these companies will double down in their belief.
Hybrid enterprises will increasingly become the norm, pairing agentic workflows with human-centric decision-making. While fully autonomous products will emerge in low-risk, high yield growth areas, most enterprises will remain skeptical to adopt a fully automated strategy. The impact of this shift is a continuation of the low-hire, low-fire labor market we currently preside in.
On an employee-level scope, AI will evolve from an optional-helping agent to a systemic productivity hyper scaler. In other words, rather than simply being a tool to draft emails, write code, and promote ideation, it will transform into a systemic driver focused on raising onboarding productivity, aligning workflows across product stacks, and proactively enabling humans to shift focus from implementation and ideation to validation.
The winners: People who understand long-term trends, customer needs, and can think cross-functionally in terms of systems, rather than siloes, will reap the benefits of this gradual transition. While domain expertise and specialization will continue to remain necessary to reaffirm emerging needs in specified sectors, the focus shifts away from understanding a specific piece of the puzzle to validating how the puzzle pieces paint the broader picture.
Infrastructure & Policy
With regards to AI and emerging tech, as history has continuously shown, infrastructure and policy will continue to lag technological advancements. 44% of enterprises report IT infrastructure constraints as the top barrier to expanding their AI initiatives (flexential). 70-85% of GenAI deployment initiatives are failing to meet ROI goals, and a large portion of this is attributed to poor data hygiene and lack of IT data organization (nttdata).
Outside enterprises, energy and data center construction will continue to fall behind LLM usage, semiconductor innovation, and domestic/international demand. This “lag” will be a byproduct of a combination of inadequate domestic supply chain efficiency combined with policy lag.
The result: While AI capabilities will continue to exponentially expand, their capacity and industrial use cases will reach a temporary plateau that will temporarily limit their practical efficacy. This may temporarily spook investors and shake valuations, but also will lead to opportunities in the following bottlenecks:
Data: Data hygiene will become the new gold as marketplaces will emerge to improve AI capabilities across all industrial verticles. These marketplaces may take numerous forms: C2C, C2B, B2C, and B2B. Data acquisition will become a crucial industry, transforming from a competitive edge to a competitive necessity.
Infrastructure/Supply Chain Integration: Similar to the internet and cloud, nations, enterprises, and individuals who control the entire AI value chain will succeed. From energy —> data centers —> GPUs/TPUs —> data models/ML algorithms —> applications/services - whoever controls this stack reaps the greatest benefits.
Memory & Context Window: As AI infrastructure matures and enterprises optimize their data hygiene, the last remaining bottleneck will revolve around storage, memory, and context windows. The longer the context window, the stronger AI models will be in deciphering real-world context, applying deep reasoning, and understanding the nuances required to solve real-world problems.
Why Capability Outruns Adoption
A simple view of where bottlenecks accumulate as AI scales.
Key Takeaways
In short, 2026 will look volatile on the surface but stabilize beneath it. Succeeding will depend less on reacting to noise and more on understanding systems, building durable workflows, and learning with intent. Economic volatility, AI adoption, and market expectations are converging toward a slower but more durable equilibrium where long-term execution matters more than short-term hype. Here are the main takeaways of what to expect in the new year:
AI adoption will be shaped by trust and infrastructure, not capability. While model performance continues to improve exponentially, enterprise usage will be constrained by data hygiene, IT readiness, regulatory pressure, and organizational trust.
Hybrid enterprises will become the default operating model. The most successful organizations will pair agentic workflows with human-centered decision-making, avoiding both over-automation and institutional inertia.
Labor market disruption will persist without mass displacement. “Low-hire, low-fire” dynamics reflect productivity gains and automation bets, shifting demand toward systems thinkers and cross-functional operators rather than pure executors.
Competitive advantage will increasingly flow to those who control context. Mastery of data quality, workflow integration, and long-horizon reasoning rather than narrow technical specialization will define both enterprise and individual winners.
