New AI Tools for Making Software
New AI Tools for Making Software
Introduction
New AI tools help people make their own apps. Big companies are now fighting to sell these tools.
Main Body
Now, people who are not programmers can make apps. They use AI tools from companies like OpenAI and Anthropic. They just tell the AI what they want in simple English. More people are putting apps on the Apple App Store because of this. Big companies are also using AI. Anthropic works with PwC to help businesses. Microsoft is using its own tool called GitHub Copilot. OpenAI and Anthropic give their tools for free to get more business customers. AI is also helping in other areas. OpenAI put coding tools in the ChatGPT app. In law, companies use AI to read long legal papers. This helps them make more money.
Conclusion
Regular people can now make software. At the same time, big companies fight to be the leader in AI.
Learning
⚡️ The 'Action' Connection
Look at how the text describes what people and companies do. To reach A2, you need to connect a Who to a What.
The Pattern:
Person/Company → Action → Thing
Examples from the text:
- People → make → apps
- Companies → use → AI
- OpenAI → put → tools
- AI → read → papers
Quick Rule: When talking about things happening now or generally, keep the action word simple.
Words to remember for A2:
- Make (creating something new)
- Use (utilizing a tool)
- Help (making something easier)
Vocabulary Learning
The Rise of AI Coding Tools and the Growth of Custom Software
Introduction
The use of advanced AI models in software development has made it easier for people to create their own custom applications. At the same time, this has increased the competition between the major AI companies.
Main Body
A new trend called 'vibe coding' has emerged, where people who are not professional developers use tools like Anthropic's Claude Code and OpenAI's Codex to create software using simple language. This allows individuals to build custom apps that meet their specific needs, rather than relying on general software. Consequently, there was a 30% increase in new apps submitted to the Apple App Store in 2025. However, this movement faces challenges, such as a lack of professional security and the difficulty of creating high-quality visual designs without a human expert. Meanwhile, big companies are fighting for control of the market. Anthropic has strengthened its partnership with PwC to integrate AI into business operations, and reports suggest that Anthropic has overtaken OpenAI in business adoption as of April 2026. In contrast, Microsoft has had a mixed experience; although it first allowed staff to use Claude Code, it is now moving its teams toward GitHub Copilot CLI to save costs and keep everything within its own system. This tension shows how intense the 'freebie war' is, as OpenAI and Anthropic offer free versions of their tools to attract large corporate clients. Finally, AI is expanding into other areas like mobile apps and law. OpenAI has added Codex to the ChatGPT mobile app, allowing users to manage coding agents from their phones. In the legal field, companies like Clio and Harvey are using AI to analyze large amounts of contract data, leading to significant profit growth. This suggests that legal technology will follow the same successful path as AI coding, as long as the companies providing the AI and the developers using it can work together effectively.
Conclusion
The current situation is defined by two main trends: the ability for ordinary people to create their own software and a fierce competition between AI giants to dominate the corporate market.
Learning
🚀 From 'Simple' to 'Sophisticated'
An A2 student describes the world using basic words like but, and, or so. To reach B2, you need to use Logical Connectors. These are words that act as bridges, showing exactly how two ideas relate to each other.
🌉 The "Contrast" Bridge
In the text, we see a shift from simple opposition to professional contrast:
- A2 Style: "Microsoft used Claude, but now they use Copilot."
- B2 Style: "In contrast, Microsoft... is now moving its teams toward GitHub Copilot."
Why this matters: In contrast signals to the reader that you are comparing two different strategies, not just stating a random change. Use this at the start of a sentence to sound more academic.
⛓️ The "Result" Bridge
Instead of always using so, look at how the article links a cause to an effect:
- The Trigger: People use simple language to build apps The Bridge: Consequently The Result: A 30% increase in App Store submissions.
Pro Tip: Consequently is the "grown-up" version of so. It suggests a logical, inevitable result.
🛠️ Sophisticated Word Pairing (Collocations)
B2 fluency isn't just about hard words; it's about which words 'like' to hang out together. Notice these pairs from the text:
| A2 Basic Idea | B2 Professional Pair | Context from Text |
|---|---|---|
| Strong help | Strengthened partnership | Anthropic + PwC |
| Big fight | Fierce competition | AI giants fighting for users |
| Use in work | Business adoption | Companies starting to use AI |
The Challenge: Next time you want to say "big competition," try "fierce competition." It changes your tone from a student to a professional.
Vocabulary Learning
The Proliferation of Agentic AI Coding Tools and the Emergence of Bespoke Software Ecosystems
Introduction
The integration of advanced large language models into software development has facilitated a transition toward personal, customized application creation and intensified corporate competition between AI providers.
Main Body
The emergence of 'vibe coding'—a paradigm wherein non-professional developers utilize tools such as Anthropic's Claude Code and OpenAI's Codex to generate functional software via natural language—has catalyzed a shift toward personal software. This trend is characterized by the creation of bespoke applications tailored to individual specifications, thereby bypassing the limitations of mass-market software design. Statistical evidence suggests a correlation between these tools and a 30% increase in new Apple App Store submissions during 2025. However, the scalability of this movement is constrained by the absence of professional security guarantees and the inherent difficulty in generating sophisticated user interfaces without human design intervention. Simultaneously, a strategic rapprochement is evident in the corporate sector. Anthropic has expanded its alliance with PwC to embed AI into enterprise operating models, a move that coincides with reports of Anthropic surpassing OpenAI in business adoption rates as of April 2026. Conversely, Microsoft has demonstrated a volatile relationship with these tools; while it initially permitted the use of Claude Code among its staff, it is currently transitioning its 'Experiences + Devices' team toward the GitHub Copilot CLI to consolidate its internal ecosystem and reduce operating expenses. This institutional friction underscores the high stakes of the current 'freebie war,' where OpenAI and Anthropic are offering complimentary usage tiers to secure high-value corporate accounts. Further technological convergence is observed in the mobile and legal domains. OpenAI has integrated Codex into the ChatGPT mobile application, enabling remote management of desktop coding agents. In the legal sector, the application of LLMs to vast corpuses of contractual data has led to significant revenue growth for firms such as Clio, Harvey, and Legora. This trajectory suggests that legal technology may mirror the early success of AI-driven code generation, provided that the tension between model suppliers and downstream application developers is managed.
Conclusion
The current landscape is defined by a dual trajectory: the democratization of software creation for individual use and a rigorous, resource-intensive competition for dominance in the enterprise AI market.
Learning
⚡ The Architecture of 'High-Register Nominalization' & Conceptual Density
To transition from B2 to C2, a student must move beyond describing actions and begin encoding concepts. The provided text is a masterclass in Nominalization—the process of turning verbs or adjectives into nouns to create a denser, more academic prose style.
🔍 The Linguistic Pivot
Compare these two ways of expressing the same idea:
- B2 (Action-Oriented): AI providers are competing intensely because they want to win over corporations.
- C2 (Concept-Oriented): ...intensified corporate competition between AI providers.
In the C2 version, the "action" (competing) becomes a "thing" (competition). This allows the writer to attach modifiers (corporate, intensified) directly to the concept, increasing the information density per sentence.
🛠️ Deconstructing the 'C2 Power-Phrases'
| The Phrase | The C2 Mechanism | The Semantic Effect |
|---|---|---|
| "Strategic rapprochement" | Abstract Noun Pairing | Transforms a simple 'agreement' into a formal geopolitical/corporate maneuver. |
| "Institutional friction" | Metaphorical Nominalization | Replaces 'they are arguing' with a systemic state of tension. |
| "Technological convergence" | Academic Synthesis | Collapses the idea of 'different techs coming together' into a single, immutable event. |
🖋️ Advanced Stylistic Nuance: The 'Bespoke' Lexis
Note the use of "Bespoke" and "Paradigm." At C2, vocabulary isn't just about 'difficulty' but about precision.
- Bespoke: Not just 'customized,' but implying a high-end, tailored quality (originally from tailoring).
- Paradigm: Not just a 'way' or 'method,' but a fundamental framework of belief or practice.
💡 Mastery Application
To emulate this, avoid starting sentences with people (e.g., "Microsoft decided to..."). Instead, start with the phenomenon (e.g., "Microsoft's transition toward... underscores..."). Shift the focus from the actor to the outcome.