TL;DR

• Spotting AI-generated content can be particularly straightforward when you know the common patterns to look for.
• AI-generated content often relies on repetitive, formulaic phrases, making it easy to identify.
• Buzzwords and filler language reduce engagement and can make content feel impersonal.
• Using too many transitional and generic statements dilutes authenticity and trust.

Customising content with specific examples and avoiding overused phrases creates stronger connections.

Can You sSpot AI-generated Content?

Artificial intelligence is reshaping content creation, offering speed and scale but occasionally at the cost of authenticity. Recognising common AI language patterns is becoming essential, as formulaic phrases can make text sound generic. In this article, we’ll explore how to spot these patterns and share strategies to keep content fresh and engaging, giving it a truly human touch.

Why Recognising AI-Sounding Language Matters

For professionals in writing, marketing, and strategy, understanding these language patterns can transform how they engage audiences. The issue isn’t with AI itself but with how certain language choices create a “default” AI tone. This often gives readers a sense of being spoken at rather than being spoken to, which can erode connection and reduce engagement.

Identifying AI language Through Recognisable Patterns

AI writing tools often streamline content creation with structured language, yet this leads to certain words, phrases, and sentences that feel familiar—and not always in a good way. Here’s a breakdown of some of the most recognisable phrases and suggestions for making content more genuine.

1. Overused Buzzwords and Phrases

AI-generated content is often littered with impressive-sounding industry buzzwords that lack substance and sound repetitive. These include:

• “Revolutionise,” “Transform,” or “Next-generation”
• “Cutting-edge” or “State-of-the-art”
• “Leverage” and “Optimise”
• “Game-changing”

Such words aim to be impactful but often feel empty. Replacing them with specific, concrete language improves readability and credibility, avoiding the impression of a polished but hollow message.

2. Vague or Redundant Expressions

Some AI phrases aim to create flow but can feel redundant and overly polished, including:

• “Ultimately,” “All in all”
• “It’s important to note”
• “It is worth mentioning”

These expressions often pad out content without adding value, making readers feel as though they’re getting “filler” instead of real insight. Keeping sentences lean and purposeful can significantly improve the reader experience.

3. Overly Polished Transitional Phrases

AI tools often rely on polished transitional phrases, which link ideas but can feel formulaic. Phrases like:

• “Consequently,” “Furthermore,” and “Additionally”

are useful in moderation but can quickly make content sound mechanical. Instead, try using informal links or even questions to guide readers naturally through ideas, enhancing engagement and making content flow more naturally.

4. Generic Sentence Starters

AI-generated content often begins sentences with broad statements that feel detached. Examples include:

• “Many people believe…”
• “There are many ways…”
• “It is widely known that…”

These vague openers risk losing the reader’s attention. Human writers typically offer specific insights or intriguing details from the start, which readers find more engaging.

5. Impersonal General Statements

AI often uses broad phrases to create context but can come off as detached and impersonal. These include:

• “Some would argue…”
• “From a broader perspective…”
• “It has been observed that…”

Personalising content with unique insights or actionable information creates a stronger sense of connection with the audience, keeping readers interested and engaged.

6. Repetitive Explanations

AI tends to repeat phrases to simplify content, but it often feels redundant. Examples include:

• “To put it simply…”
• “This can be broken down into…”
• “What this means is…”

These phrases become repetitive quickly, losing their intended clarifying effect. Instead, using precise language and avoiding unnecessary repetition ensures content stays engaging and valuable.

7. Common AI Phrasing in Descriptions or Analyses

When explaining ideas, AI often sticks to predictable phrases that sound clinical. These include:

• “This has led to an increase in…”
• “The primary benefit of this approach is…”
• “There are several factors to consider”

Human writers can create more engaging analysis by using fresh phrasing or offering new perspectives on familiar topics.

8. Filler Language and Informational Add-Ons

AI-generated text often includes filler language that, while aiming to create interest, tends to dilute the message:

• “An interesting fact is…”
• “Did you know that…”
• “One thing to consider is…”

Readers value conciseness and relevance, so cutting filler phrases helps keep the focus on meaningful content that adds real value.

What Happens When You Use Words and Phrases Like This Already?

Using these patterns can have a noticeable impact on content effectiveness, sometimes negatively influencing reader perception, trust, and engagement.

1. Reduced Reader Engagement

Buzzwords and vague phrases may catch initial interest but can lead to disengagement. If content seems to lack depth, readers may stop reading before reaching the main message.

2. Loss of Trust and Authenticity

Readers value authenticity, and over-relying on generic phrases can make content feel detached or even inauthentic. This perceived lack of connection can lower reader trust and lessen the impact of your message.

3. Diluted Brand Voice

Every brand has a unique voice, and AI-sounding language can drown it out, creating a message that feels like everyone else’s. Readers connect more deeply with distinctive, authentic voices that are not simply repeating industry-standard language.

4. Reduced SEO and Long-Term Impact

As search engines evolve, they prioritise content demonstrating “expertise, authoritativeness, and trustworthiness.” Formulaic language risks sounding less credible, which can reduce ranking effectiveness over time. Search engines reward high-quality, engaging content, and AI-sounding text can struggle to meet these standards.

Crafting Authentic, Human-Centred Content

Identifying and avoiding these common phrases lets brands and professionals focus on what matters—connecting with their audience through authenticity, relevance, and value. Here’s how to avoid the pitfalls of AI-sounding content:

Prioritise Specificity

Replacing generalities with examples or data points boosts credibility. Instead of “Data-driven insights drive growth,” say, “Brands using consumer-focused insights have seen a 30% boost in engagement.”

Vary Sentence Structure

AI often produces repetitive structures, making content feel monotonous. Varying sentence length and style keeps readers interested, creating a rhythm that feels human.

Limit Transitional Phrases

Instead of stock transitions, experiment with questions or informal links to create natural flow, allowing ideas to connect without sounding forced.

Add Personal or Unique Insights

Adding original insights can elevate writing, making it relatable and distinct. Readers value authenticity, so expressing a unique perspective or anecdote adds value and fosters connection.

The Role of SEO in Human-Centred Writing

While AI-generated content may rely on keywords for SEO, a balanced approach keeps content engaging without compromising readability:

• Relevance: Focus keywords on the reader’s search intent and integrate them naturally into the content flow.
• Keyword Variation: Human writers can use keyword variations to avoid repetition, maintaining relevance while keeping the text fresh.
• SEO in Headings: Using keywords naturally in descriptive headings improves readability and search ranking.

Final Thoughts

As AI technology advances, understanding language patterns helps professionals humanise content, avoid formulaic language, and keep audiences engaged. Recognising these patterns can guide content creators in connecting with readers in a memorable, relatable way.

Join the Conversation

Can you spot when a piece of content was generated by AI? What phrases make you immediately suspicious? Share your thoughts and join the discussion on how we can make content more human! And don’t forget to subscribe for updates on AI and AGI developments!

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TL;DR:

• Chinese AI companies are reducing costs by optimising hardware and using smaller data sets.
• Strategies like the “model-of-expert” approach help achieve competitive models with less computing power.
• Companies like 01.ai and ByteDance are making significant strides despite US chip restrictions.

In the rapidly evolving world of artificial intelligence (AI), Chinese companies are making waves with innovative strategies to drive down costs and create competitive models. Despite facing challenges like US chip restrictions and smaller budgets, these companies are proving that creativity and efficiency can overcome significant hurdles.

The Cost-Cutting Revolution

Chinese AI start-ups such as 01.ai and DeepSeek are leading the charge in cost reduction. They achieve this by focusing on smaller data sets to train AI models and hiring skilled but affordable computer engineers. Larger technology groups like Alibaba, Baidu, and ByteDance are also engaged in a pricing war, cutting “inference” costs by over 90% compared to their US counterparts.

Optimising Hardware and Data

Beijing-based 01.ai, led by Lee Kai-Fu, the former head of Google China, has successfully reduced inference costs by building models that require less computing power and optimising their hardware. Lee emphasises that China’s strength lies in creating affordable inference engines, allowing applications to proliferate.

“China’s strength is to make really affordable inference engines and then to let applications proliferate.” – Lee Kai-Fu, former head of Google China

The Model-of-Expert Approach

Many Chinese AI groups, including 01.ai, DeepSeek, MiniMax, and Stepfun, have adopted the “model-of-expert” approach. This strategy combines multiple neural networks trained on industry-specific data, achieving the same level of intelligence as a dense model but with less computing power. Although this approach can be more prone to failure, it offers a cost-effective alternative.

Navigating US Chip Restrictions

Despite Washington’s ban on exports of high-end Nvidia AI chips, Chinese companies are finding ways to thrive. They are competing to develop high-quality data sets to train these “experts,” setting themselves apart from the competition. Lee Kai-Fu highlights the importance of data collection methods beyond traditional internet scraping, such as scanning books and crawling articles on WeChat.

“There is a lot of thankless gruntwork for engineers to label and rank data, but China — with its vast pool of cheap engineering talent — is better placed to do that than the US.” – Lee Kai-Fu

Achievements and Rankings

This week, 01.ai’s Yi-Lightning model ranked joint third among large language model (LLM) companies, alongside x.AI’s Grok-2, but behind OpenAI and Google. Other Chinese players, including ByteDance, Alibaba, and DeepSeek, have also made significant strides in the rankings.

Cost Comparisons

The cost for inference at 01.ai’s Yi-Lightning is 14 cents per million tokens, compared to 26 cents for OpenAI’s smaller model GPT o1-mini. Meanwhile, inference costs for OpenAI’s much larger GPT 4o are $4.40 per million tokens. Lee Kai-Fu notes that the aim is not to have the “best model” but a competitive one that is “five to 10 times less expensive” for developers to use.

The Future of Chinese AI

China’s AI industry is not about breaking new ground with unlimited budgets but about building well, fast, reliably, and cheaply. This approach is not only cost-effective but also fosters a competitive environment that encourages innovation and efficiency.

Comment and Share:

What innovative strategies do you think will shape the future of AI in Asia? Share your thoughts and experiences with AI and AGI technologies in the comments below. Don’t forget to subscribe for updates on AI and AGI developments.

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TL;DR:

• Only 50% of banks have made sufficient progress in AI and digitalisation.
• DBS Bank expects to gain over S$1 billion from AI by 2025.
• Cultural shifts and strategic planning are crucial for successful AI integration.

Artificial Intelligence (AI) is transforming the world, and Asia is at the forefront of this revolution. Banks, in particular, are feeling the heat. According to Piyush Gupta, the outgoing CEO of DBS Group Holdings, only about half of the banking industry has made enough progress in embracing digitalisation and AI. So, what’s holding the other half back? Let’s dive in.

The Race to Digitalise

Gupta, who has been widely credited for transforming South-east Asia’s biggest bank, believes that many banks have been going about digitalisation the wrong way. “A lot of people have tried to digitise before they change the fundamentals,” he told Bloomberg News. “I call that putting lipstick on a pig.”

The DBS Transformation

Under Gupta’s leadership, DBS has become a global leader in digital banking. The bank’s market value has soared to S$112 billion, and it’s expected to gain over S$1 billion from AI by 2025. But it wasn’t always smooth sailing. Gupta admits that DBS had its share of setbacks, including technology glitches and penalties from the Monetary Authority of Singapore.

The Role of Culture

Gupta believes that changing the culture at DBS was his biggest achievement. The bank is now “a little more entrepreneurial, a little bit more risk-taking, but most of all, it has got a little bit more confidence about what can be achieved.” This cultural shift has been crucial to DBS’s digital transformation.

Common Pitfalls in AI Integration

Gupta points out that common failures at many banks result from technology mistakes and corporate culture. So, what can banks do to avoid these pitfalls?

Strategy Before Technology

Banks need to have a clear strategy before investing in technology. It’s not about having the shiniest new tech; it’s about using tech to achieve your strategic goals.

Culture Matters

A risk-averse culture can hinder innovation. Banks need to foster a culture that encourages experimentation and accepts failure as a part of the learning process.

The Future of AI in Banking

As Gupta prepares to step down, he leaves behind a bank that’s ready for the future. But what about the rest of the industry?

The Rise of Fintech

Traditional banks are facing stiff competition from fintech rivals like Grab Holdings. To stay relevant, banks need to embrace AI and digitalisation.

Regulatory Challenges

Banks also face regulatory challenges. They need to work closely with regulators to ensure that AI is used ethically and responsibly.

Comment and Share:

How do you think banks can accelerate their AI journey? Share your thoughts and experiences below. And don’t forget to subscribe for more updates on AI and AGI developments!

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