WHAT’S INSIDE THIS SPECIAL EDITION

This year in tech was one of the wildest we have seen. 2025’s defining shift was that generative and “agentic” AI moved from pilots into production, embedded in productivity suites, developer tools, customer service and operations. AI, industrializing machine learning, and connected ecosystems were the top forces re-wiring business models. However trade, AI sovereignty and shifting market dynamics were also key factors leading to a complex picture.

Looking back on our newsletter editorials over this year, the story of 2025 is a fascinating one. This summary attempted to identify how the trends in Tech, AI, markets, platforms, and automation evolved over the year.

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Q1 Begins: January and February’s Mixed Signals

The year began with mixed signals as the Ai enthusiams collided with the political reality of tariffs, trade wars and geopolitics.

In “Shifts in AI Agents and Infrastructure, Trade + More” we focused on how fast AI agents were moving from experiments into early products. The important point was not polish. It was direction. Teams were trying to automate tasks that used to need human attention.

Around the same time, “Trump 2025 and Tech: Navigating Uncertainty and Opportunity” connected tech decisions to policy risk. Investors and founders were already thinking about how regulation, trade, and elections could shape AI investment.

February reinforced this uneven landscape.

In “US Tech Billions Respond to China AI Threat” the focus was on competition. The Chinese and US Governments and companies were reacting to each other as the trade war kicked off. It was already clear that AI in 2025 was going to be intricately tied to national strategy and capital flow.

Another important piece was “The Humanoid Robotics Arms-Race” discussing how Robotics was no longer a side topic. This explored how training costs, hardware supply, and talent access were becoming part of mainstream tech planning.

At this stage of the year, AI progress felt fragmented. Many ideas were active at once. Few had settled into clear winners. However looking at robotics from December it is pretty remarkable how far things have come in under a year.

March: Infrastructure Numbers Get Big

March brought the massive scale of the AI boom into sharp focus.

In “Nvidia & xAI Join $100B AI Infrastructure Partnership”, the headline itself carried the message. $100B commitments do not happen for short term experiments. Companies were locking in long term capacity.

Earlier in the month, “Mega-AI Infra Spend, Markets & Trade Jitters” linked infrastructure spending to market reaction. Chips, energy demand, and global supply chains started showing stress.

We also looked closely at platform power in “Can Nvidia Retain Its AI Chip Dominance?”. We discussed how Nvidia’s position mattered because many AI systems depended on its hardware. Control over chips shaped who could scale and who could not.

By the end of Q1 it was already clear that AI was expensive, and the companies that could afford to build early were gaining an advantage. 

May: Agents, Commerce, and Productivity Gaps

April shifted attention toward use cases emerging from the AI boom.

In “The Agentic Commerce Age”, we explored how AI systems were being designed to take actions inside shopping and transactions. This included searching, comparing, and guiding purchases.

Another key article was “Frontier Firms vs. the Productivity Gap”. Some companies were getting faster month after month. Others were falling behind. The difference often came down to tooling and process rather than talent alone.

Q1 ultimately showed us how AI affected outcomes, not just demos. The trend became clear that teams that were adopting this tech faster in their workflows were shipping more and testing more ideas. However whether the ROI was justifying this increased spend was still an open question.

Q2 Begins: Robots, Mobility, and Physical Systems

May saw us shift focus onto the emerging trend of AI integrating into physical environments.

In “Nvidia Robots Can Dream, Rapidly Accelerating Robotics Training”, we covered how simulation and training tools were lowering the cost of building robots. Faster training meant more companies could try robotics.

Later, “Tesla's Robotaxis Are The Gateway Drug to Real World Spatial AI” focused on driving as a difficult AI problem. Roads are unpredictable. Solving that problem had implications for logistics, mapping, and autonomy. We finally saw the robotaxis go life without safety rails in December with good results for TSLA stock, so this long tail play is starting to pay off for Tesla.

We also looked at platform competition in “Apple's 1-2 Combo Glasses + AI Search Shakes Google Pre I/O”. AI was becoming part of how companies protected user attention and search traffic.

AI was no longer limited to screens. It was moving into streets, warehouses, and hardware budgets. This was a trend that would only accelerate over the rest of the year with release after release.

June: Platform Stacking Becomes Obvious

June clarified how large companies were building layered advantages.

In “Is Apple’s AI Strategy Defiance or Delay?”, the focus was on timing. Apple did not rush AI  features like many of its rivals this year. It explored how Apple’s focus was on protecting distribution and margins and user privacy. We explored how Apple’s long-play could actually be a wise move.

In “Amazon's Bots & Drones Coming For The Last Mile Delivery Market”, we examined how Amazon is integrating physical AI into its e-commerce and delivery ecosystem. Automation in this area matters because it affects prices and speed. Amazon has the market access and capital to become a giant in this segment of the AI market.

Later in the month, “Meta's $65B Superintelligence Bet & AI Talent Poaching Spree”showed how competitive the talent market had become. The $65B superintelligence lab spend signaled Meta’s long term commitment to AGI.

June showed how AI, capital expenditure on the AI war, and hiring were linked. The biggest players were increasingly willing to spend early to protect future position.

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July, August: Capital and Policy Shifts

In July, the focus shifted from product launches to capital and policy. In “Trump’s AI Action Plan: Markets Surge, VCs Bet Bigger” we showed how quickly investors reacted to government signals around domestic AI investment. Venture funding and market sentiment moved before any rules were finalized. The article made clear that confidence and direction mattered as much as technical progress. AI infrastructure was expensive, and capital followed narratives that suggested long term support.

In August, the tone stayed focused on positioning rather than features. Coverage during this period examined who already controlled compute, data, and distribution as costs continued to rise. Companies with early access were able to keep building, while late movers faced tighter constraints. August helped explain why later stories about sovereign AI, large infrastructure bets, and automation felt expected rather than sudden. By the end of summer, much of the leverage that shaped the rest of 2025 was already in place.

September: Sovereign AI and Mega Deals

September continued the recurring trend of AI as a central aspect of national strategy.

In “Why Nations Are Investing In Sovereign AI”, we explored why countries wanted local control over models and compute. Dependence on foreign systems was increasingly being seen as creating risk. This is also conversely a risk for the US industry as companies reduce reliance on US tech and spend. Especially with China’s fast growing semiconductor industry this is still a major concern.

Later, “Nvidia’s Record $105B Bet on OpenAI and Intel” put scale into perspective. This $105B infrastructure play reinforced how concentrated the AI ecosystem was becoming.

September made it clear that AI was still tied to national competitiveness and long term planning in the minds of both politicians and tech executives. And the AI companies and Venture Capital were more than willing to make deals to ensure the US would win the battle.

Q3 Begins: October - Automation Reaches Operations

In “Amazon’s Automated Future Is Emerging”, we looked again at Amazon’s warehouse and logistics automation. The article explored how these changes targeted cost, speed, and reliability with the use of AI tech. Automation at this level influenced prices, delivery times, and labor needs. It was quieter than consumer launches but more durable.

November: Jobs and Commerce Pressure

November brought the human impact of AI increasingly into focus.

In “Winners And Losers In The AI Jobs Data” on Nov 19, we reviewed employment trends in light of the latest jobs data. Some roles were shrinking - primarily junior ones, while others gained leverage through AI tools. This explored how senior roles are set to fare better under AI than entry level ones, creating some interesting social dilemmas

Then “OpenAI Device Reveal & AI Shopping vs Amazon’s AI Investments” highlighted how it matters who controls the moment when people decide what to buy. Amazon makes money when people start their shopping journey on Amazon. Search, recommendations, reviews, and checkout all happen inside its system. That control drives a large share of its revenue. However, OpenAI’s device work and AI assistants pointed in a different direction. This matters because shopping is one of the largest and most reliable sources of online revenue. Whoever guides buying decisions gains leverage over brands, pricing, and advertising spend.

In earlier features on Google’s A2P commerce and payments work and OpenAI’s payments standard, we also explored how the focus was  on controlling this financial plumbing. Our coverage showed how this approach favors companies that already control identity, messaging, and authentication. Taken together, these features showed a deeper shift than shopping recommendations alone. Commerce was moving away from websites and checkout pages toward automated, permission-based flows. Payments standards became as important a battle as discovery in the era of agentic commerce.

By November, it was clear that AI was starting to sit closer to the decision point in shopping. That shift helped explain why giants like Amazon, OpenAI, and Google were investing so heavily in their own AI systems at the same time. 

What 2025 Taught Us About How Tech Changed

By the end of 2025, a few patterns emerged piece by piece across the year.

Scale matters more than novelty

Early in the year, attention focused on big infrastructure numbers. We showed how concentrated the buildout became. The companies that moved first locked in chips, talent, and supply. Smaller players had to work around those constraints.

By December, scale no longer looked optional. It shaped what could be built and how fast.

Platforms tightened their grip

Throughout the year, platform control kept surfacing. Apple, Amazon, Meta, Tesla, and OpenAI were less competing feature by feature, than they were defending distribution, data access, and user attention. Control over entry points mattered more than individual products.

AI moved into physical systems

By mid-year, AI was no longer confined to software. Robotics and automation became recurring themes. These systems were built to reduce cost, increase speed, and operate at scale. They changed how warehouses, delivery networks, and mobility systems worked.

Work changed unevenly

The impact on jobs was not uniform. We observed how some roles lost leverage while others gained it. People who learned to use AI tools effectively became more productive. Others struggled as tasks were automated or compressed.

This trend developed quietly across the year and became more visible by November.

Governments became active participants

Policy and national strategy mattered more as the year went on. Governments increasingly sought to influence capital flows and infrastructure decisions. AI was being treated as a strategic asset rather than a neutral technology. This added another layer of complexity to the ecosystem.

Developer speed reshaped competition

Faster AI-powered coding changed who could build companies and how quickly products reached users. Developer tools became part of the infrastructure stack. Teams that shipped faster tested more ideas and adapted quicker.

Where We Are Now

At the end of 2025, tech looks different than it did twelve months ago. 

AI is embedded in workflows rather than showcased as a feature. Automation operates inside logistics and operations. Platforms hold more power through distribution and data. Infrastructure spending has a concentrated advantage. Work has shifted unevenly. Governments are directly involved.

None of these changes happened overnight. They built up across the year through real decisions, real spending, and real deployments.

That is the state of tech as we enter the next phase.

What 2026 Is Likely to Bring

Based on what we covered this year, here are some what we can expect next year:

• More automation inside logistics, manufacturing, and operations

• Continued pressure on shopping and recommendation systems

• Robots becoming standard capital investments

• Talent concentration and higher investment on companies that ship fast

• Governments continuing to expand local AI capacity

We look forward to keeping you updated on these stories as they emerge into sharper focus in the new year.

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