The AI App Landscape in 2026: The Paradigm Shift from “Making Tools” to “Thinking Partners”

Having delved into the insightful notes on AI applications for 2026, grounded in observations from 2025, a clear and compelling picture of the near future emerges. The current AI application ecosystem is maturing in ways both expected and surprising. We have cracked the code on making software development cheap, yet this reality hasn’t permeated enterprises or the world to the extent its low cost implies. We’ve likely realized less than 10% of its potential impact on how companies are built and what software will exist. Meanwhile, fundamental tooling problems persist—most notably, that all our tools are for making, not for thinking.

This article breaks down these core trends, exploring what they mean for our work, entrepreneurship, and corporate strategy.

Summary

Based on industry analysis, this article examines key trends for AI applications in 2026: tools will evolve from “execution and making” to “assistance and thinking”; all enterprise functions will move towards being “software-first,” triggering deep organizational and cultural shifts; and composite AI apps will continue to diverge from foundation models, building moats through multi-model orchestration and deep specialization. The argument is made that the technology already exists to support vastly more ambitious enterprise goals, with the key challenge being how we adapt our thinking and management models to seize this opportunity.

Core Shift #1: From “Making Tools” to “Thinking Tools”

The tools we currently rely on for knowledge work are almost exclusively execution-oriented.

  • IDEs are for writing code.
  • Figma is for completing designs.
  • Spreadsheets are for building analytical models.

These are excellent “making tools.” However, when the core problem shifts from “how to build it” to “what to build,” we lack powerful modern tools to aid exploration and thinking. Large Language Models (LLMs) themselves have emerged as our primary “thinking partners,” but this is insufficient.

As coding agents gain capability—with increasing accuracy and longer planning horizons—the hard problem moves. Imagine a product manager who sets broad objectives, and their AI partner brainstorms, executes, and A/B tests 2-3 new features overnight, ready for review the next morning.

Yet, based on direct experience, current models are still not proficient at “deciding what to build next.” The ideas generated are often bland, derivative, and lack the creative spark found in truly innovative product thinking.

Therefore, I believe the spiritual successors to today’s coding, design, and productivity tools will be intensely focused on exploration rather than execution.

The Current Vanguard:
Coding tools are leading this charge. Cursor is the most advanced example today. Another interesting case is Antigravity, which explicitly designs its product as “agent-first,” which is fundamentally “exploration-first,” showing us one potential path for tool evolution.

Core Shift #2: Software “Consumes” All Service Functions Within the Organization

Within software companies, functions have traditionally been split between “power” functions and “service” functions.

  • Power Functions: Engineering, product, performance marketing—roles typically closer to the software itself.
  • Service Functions: Legal, finance, human resources—traditionally further from software, more leveraged by human capital.

The maturation of coding agents has two critical implications:

1. Every Team Must Become a Software Team
In the future, every team and every task (marketing, legal, procurement, finance) must adopt a “software-first” mindset. Leaders in these functions must learn to reach for a software toolbox before the traditional processes or human systems they’ve historically relied upon.

The path will diverge into two main routes:

  • Adopting domain-specific products, like Harvey for legal work.
  • Using “bare metal” general coding agents, such as Codex or Claude Code.

Regardless of the path, the destination is the same: Every team, ultimately, should function as a software team.

2. Enterprise Ambition for Software Can (and Must) Expand Dramatically
For an enterprise—particularly a software producer—the ambition regarding “what software we should produce” can become extremely bold. The entire ideation and prioritization pipeline needs to be rebooted to accommodate this new reality.

A direct corollary is: Every feature that can be built, will be built. Most enterprises are simply not prepared for this.

The Deeper Challenge:
I believe the accompanying culture change problem will be as difficult as the organizational change problem. Getting non-technical functions to embrace models is key to gaining broad operational leverage, but this requires a fundamental shift in mindset and work habits.

Core Shift #3: The Rise and Continued Divergence of Compound AI Apps

Entering the second year of the “reasoning model” era, I expect the divergence between AI-native applications and foundation AI models to accelerate.

Compound AI apps combine the orchestration of cutting-edge models, domain-specific user interfaces, and the extensive feature surface area that is now extremely cheap to build. This is the natural outcome of the “hyper-specialization” era—extreme specialization is now possible and forms a strong argument for AI apps as distinct and increasingly divergent from the underlying models.

A common misassumption is that the application layer will be subsumed by the model layer.

The opposite may be true. Even in a core area like coding, central to model progress, we see a thriving startup ecosystem. In 2025 alone, this sector generated over $1 billion in new revenue. The capabilities of large labs and tech giants are “jagged,” like the models they produce—formidable in focus areas but constrained by complex commitments and difficult prioritization trade-offs.

What kinds of AI apps will thrive?
We can look at two dimensions:

  1. Areas of Advantage: Domains that benefit from being multi-model, having cornered data resources, network effects, or requiring a large feature surface area.
  2. Application Architecture: Combining the concept of “thick” AI apps—featuring multi-model orchestration, autonomy sliders, context engineering, etc.—gives us a glimpse of mature AI applications. They are no longer simple chat interfaces but complex, autonomous, customizable work engines.

Letting More People Discover and Create: The Democratization of AI

The command-line-style user interface has long been a barrier, preventing everyday consumers from accessing some of AI’s most powerful capabilities. This is beginning to change.

  • Wabi has been a major catalyst in exposing code generation to consumers.
  • The Images tab in ChatGPT/Grok has done the same for image generation.
  • With the development of App Directories and Skills, MCPs and prompt plugins may also reach a mainstream audience.

Empowering more consumers to “make” things themselves has profound significance. In 2025, the delight of generating a small app was comparable to generating a poem in 2023. Yet, most consumers remain unaware this is possible. This proliferation of creative experience can partially alleviate concerns about the cultural disconnect of technology and makes pessimistic narratives about “who gets to create” seem less absolute.

Practical Notes for (Incumbent) CEOs

For leaders of established companies navigating the AI transition, here are actionable suggestions derived from the above observations:

1. Reimagine Customer-Facing Functions
Study the best examples of how models are collapsing sales, support, collections—all customer-facing roles—into a single function with a broad, unified goal. This is not just about efficiency; it’s a complete redesign of the customer experience.

2. Ruthlessly Implement “Software-First”
Adopt “every function is software-first” as a strategic doctrine. The adoption of models by non-technical functions is the critical path for enterprises to gain broad operational leverage and achieve exponential efficiency gains.

3. Demand More Ambitious Products and Pricing
Our technological capabilities now support greater ambition. If Tesla can deliver Full Self-Driving and Claude Code can write complex software, then for the near-term purposes of most enterprise tasks, we already possess tools with AGI-like capabilities. CEOs must ask: With cost constraints dramatically lowered, where are the new boundaries for our products? Do we have the courage to set more ambitious prices for products that deliver significantly more value?

Finally… Have Fun

No one tells you you’re living in the good old days until they’re over. So, consider this your notice. This product cycle is more decentralized, more software-led, and simply more fun for technologists than any in recent memory.

Exploring these new technologies, discussing their implications, and the sheer act of building new things is a joy. I hope everyone is having as much fun with it as I am.

FAQ: Common Questions on 2026 AI Application Trends

Q1: What exactly are “Thinking Tools”? How are they different from note-taking apps?
A1: “Thinking tools” here refer not to apps for recording known information, but to active tools that assist in exploring the unknown, forming ideas, making decisions, and strategizing. They act more like a “partner” capable of high-level conceptual sparring, posing counter-intuitive questions, and simulating outcomes of different scenarios. Current LLM conversations are a prototype, but future dedicated tools might integrate capabilities like visual causal reasoning, parallel testing of multiple hypotheses, and real-time querying of domain-specific knowledge graphs.

Q2: What does “Every team is a software team” mean in practice? How can Legal or HR do that?
A2: This doesn’t mean the legal counsel starts writing Python. It means their core workflow will be deeply embedded within software-driven (especially AI agent) tools. For example, a legal team uses an AI like Harvey to review contracts, auto-generate clauses, and assess risk; an HR team uses agents for initial candidate screening, personalized employee development path planning, and analyzing organizational effectiveness. Their work will revolve around configuring, overseeing, and optimizing these software tools, with their problem-solving ability massively extended through “software leverage.”

Q3: What does the “divergence” between AI apps and foundation models mean? Can’t I just do everything with ChatGPT?
A3: Divergence means AI applications won’t disappear; instead, they will build moats through integration depth, user experience, and domain specialization. ChatGPT is a general interface. However, a professional “AI Financial Analyst” app might embed a finely-tuned finance model, APIs connected to real-time market data sources, a spreadsheet and chart interface familiar to accountants, and pre-set compliance check workflows. It offers a complete, “out-of-the-box” solution, not a general capability requiring complex prompt engineering. This deep integration and specialization constitute the divergence.

Q4: In the advice for CEOs, what does “demand more ambitious prices” imply?
A4: Traditional software pricing is constrained by development costs and conservative estimates of market acceptance. When the cost of implementing features plummets, companies must re-evaluate the total value their product delivers. For instance, an AI tool that automates 90% of compliance processes could be worth millions, as it saves massive legal costs and mitigates risk. CEOs need the courage to price based on this disruptive value creation, not on the limited feature sets of the past, which were constrained by high costs. This, in itself, is a reset of strategic thinking.