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Bitcoin just broke US$81,000: The real reason is not what you think

Bitcoin climbed 5.08 per cent over 24 hours to US$81,106.29, outpacing the broader crypto market’s 4.83 per cent advance to a US$2.72 trillion total capitalisation. The move did not happen in a vacuum. Bitcoin now trades with a 97 per cent correlation to the S&P 500 and an 86 per cent correlation to gold. Those figures reveal a macro-driven repricing rather than a crypto-specific breakout. When digital assets, equities, and bullion rise together, traders are responding to shifting expectations about Federal Reserve policy and global liquidity.

The main catalyst arrived from a dovish turn in rate expectations. Federal Reserve Governor Christopher Waller signalled he would be inclined to support holding interest rates steady if inflation data improved. Before those remarks, markets priced in a 63 per cent chance of a September rate hike. Afterward, that probability fell to around 50 per cent. A pause in US-Iran tensions added further relief by easing oil price fears and removing a recent inflation headwind. The White House also contributed dovish comments, reinforcing the message that the Fed would not tighten aggressively.

With Treasury yields no longer threatening to surge, capital flowed back into equities and high-beta assets like Bitcoin. This repricing pushed Bitcoin into near lockstep with the S&P 500, confirming its current role as a macro-sensitive asset. The immediate trigger for the next move comes from the August Non-Farm Payrolls report due on September 4. Soft jobs data would strengthen the dovish case and could extend gains. A strong report would revive fears of a rate hike and put pressure on the entire complex.

The advance also received a powerful boost from a short squeeze in the derivatives market. Traders liquidated over US$415 million in Bitcoin short positions over 24 hours, with US$164 million of that total vanishing in just four hours. A separate measure showed Bitcoin liquidations hitting US$271 million in 24 hours, with shorts accounting for 95 per cent of the total, a 425 per cent surge.

Forced buying by traders covering bearish bets created a feedback loop that accelerated the price climb. This dynamic explains the speed and intensity of the move. The macro news lit the fuse, but leverage did the heavy lifting afterward. Crowded bearish positions unwound in a cascade, and each wave of forced buying pushed prices higher. That mechanical accelerator is not the same as organic spot demand, and it can reverse quickly if momentum stalls.

Also Read: Bitcoin slipped below US$80,000, so why are traders still betting on US$82,000?

Sentiment indicators reflected the sudden shift. The Fear and Greed Index jumped to 78, signalling greed and overheated conditions. Influential market voices added fuel to the fire by declaring a crypto supercycle, which fed retail fear of missing out. Intense bullish social media narratives reinforced the move. This kind of euphoria often precedes volatility because latecomers chase the upswing after the initial institutional move.

The derivatives market will show whether leveraged speculation is stabilising or building toward another flush, as reflected in funding rates and open interest changes. The squeeze pushed prices higher, but the market now needs sustained spot buying and exchange-traded fund inflows to confirm the move as more than a one-day event.

From a technical standpoint, Bitcoin broke above the US$78,000 resistance with high volume, a bullish signal. The next major hurdle sits in the US$83,000 zone. That level aligns with long-term holder supply and previous rejection points. If Bitcoin holds above US$80,000, especially above the US$81,000 breakout level, the path could extend toward US$83,000 to US$85,000.

A clear break above US$83,000 would open the way toward the Fibonacci extension near US$86,500. If Bitcoin fails to hold above US$78,000, the surge likely came primarily from short covering and could fade. In that scenario, a pullback toward the US$78,000 to US$76,000 support zone would be the next test. The market needs a weekly close above US$81,000 to confirm the breakout and attract longer-term buyers.

Exchange-traded fund flows remain the key institutional signal. On September 2, spot ETFs recorded a net inflow of US$101 million. That is a positive sign, but one day does not establish a trend. If spot ETF inflows re-accelerate after the squeeze, they would provide fundamental support above US$80,000 and reduce the risk of a sharp reversal. Without that follow-through, the rally could lose steam once the forced buying from liquidations ends.

The market has seen this pattern before. Macro news triggers a spike, shorts get squeezed, and then the price drifts without new organic demand. The difference this time is the broader macro backdrop. If the Federal Reserve truly pivots to a dovish stance, the liquidity environment could support a longer rally. If the dovish signals prove temporary, the squeeze alone will not hold Bitcoin above US$80,000.

Also Read: Can Bitcoin defend the critical US$76,500 foundation zone before the September 11 inflation data triggers another massive liquidation cascade?

The near-term outlook is bullish but fragile. Bitcoin’s surge rests on two pillars: a macro reprieve and a violent short squeeze. The macro reprieve is real but depends on upcoming data. The August jobs report on September 4 will be the first test. Inflation data and Fed communications will follow.

Any hawkish surprise could unwind the rate cut expectations that underpinned this move. The second pillar, the short squeeze, has already spent much of its force. More than US$415 million in bearish positions are gone, and the Fear and Greed Index at 78 suggests the easy gains from sentiment reversal are behind us. The next leg higher requires spot buyers to step in with conviction.

From my perspective, Bitcoin’s 5.08 per cent move is a legitimate macro-driven breakout, but it is not yet a confirmed trend change. The 97 per cent correlation with the S&P 500 suggests this is a liquidity trade rather than a crypto-specific narrative. The US$101 million ETF inflow on September 2 is encouraging but insufficient on its own.

The real test is the US$83,000 resistance. If Bitcoin clears that level with sustained volume and ETF inflows accelerate, the path to US$86,500 becomes realistic. If Bitcoin rejects US$83,000 and falls back below US$78,000, the rally will look like a short-covering episode that ran out of fuel.

As I said, the next few days will reveal whether this is the start of a new leg or just a powerful bounce within a range. Watch the August jobs report, watch ETF flows, and watch how Bitcoin behaves around US$80,000 to US$81,000. Those signals will tell you more than any single price print.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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AI and human creativity: How ChatGPT Canvas bridges the gap

In the world of AI-powered content creation, ChatGPT Canvas is a game-changer. Unlike traditional ChatGPT interactions, where multiple prompts and re-prompts are necessary to fine-tune content, ChatGPT Canvas introduces a new, interactive interface that allows direct text manipulation, structured editing, and enhanced formatting options. This guide explores the benefits, key features, and practical applications of ChatGPT Canvas for content creators, marketers, and business professionals.

The challenges of traditional ChatGPT editing

One of the biggest challenges with using standard ChatGPT or other AI tools for content writing is the difficulty in editing specific sections of generated text. Users often find themselves repeatedly prompting and refining responses to achieve a satisfactory output. This process can be time-consuming and inefficient, particularly when working on long-form content such as articles, blog posts, or marketing copy. Since the AI generates text as a complete block, adjusting a single section often requires rewriting the entire response or editing the entire block part by part manually or via prompts. That may however introduce issues such as with keeping a consistent tone, style, or format throughout the content.

What is ChatGPT Canvas?

ChatGPT Canvas is an advanced interface that simplifies content generation, editing, and refinement. It allows users to:

  • Edit text directly within the AI-generated response
  • Adjust content length for different formats
  • Modify reading levels to suit various audiences
  • Format content using markdown for better readability
  • Suggest edits and improvements with built-in recommendations

Also Read: The scarcity mindset is killing creativity, not AI

Key features of ChatGPT Canvas

  • Interactive editing

Users can click directly into the generated text to make edits, eliminating the need to regenerate the entire response. This feature is particularly useful when refining specific sections of an article or making minor adjustments to wording and tone. Unlike traditional ChatGPT, where users must copy the generated text, paste it into another document, and manually edit, ChatGPT Canvas allows seamless in-place modifications without disrupting workflow.

  • Content length adjustment

ChatGPT Canvas allows users to modify the length of their content effortlessly. Whether expanding a script for a longer video or condensing a post for social media, users can fine-tune the content to meet their needs. This feature eliminates the frustration of starting over due to content being too long or too short, making it ideal for those who need flexibility in their writing.

See for example before and after:

  • Reading level adaptation

Different audiences require different levels of complexity in writing. With ChatGPT Canvas, users can adjust the reading level to cater to:

  • Middle school students
  • General web readers
  • Academic or professional audiences

This feature ensures that content is accessible and appropriate for its target readers. By simplifying or elevating the language, users can tailor their messaging to resonate with their intended audience without needing to rewrite the entire content manually.

  • Emoji and formatting support

For users who want to enhance engagement, ChatGPT Canvas supports:

  • Automatic emoji integration
  • Bold and italic text formatting
  • Headings and subheadings
  • Markdown support for web publishing

These features are particularly useful for social media posts, blog formatting, and digital marketing content. Formatting is essential for readability, and with ChatGPT Canvas, users can quickly transform plain text into a visually appealing format with structured elements.

  • Version control and undo feature

One of the standout features of ChatGPT Canvas is version control. Users can:

  • Restore previous drafts
  • Undo changes without affecting the entire document
  • Modify individual sections instead of re-prompting the AI

This makes content revision more efficient and user-friendly. Having the ability to track changes and revert to earlier versions ensures that no valuable content is lost during the editing process.

  • AI-generated images

ChatGPT Canvas allows users to insert AI-generated images into their content. While the initial image quality may vary, refining prompts can significantly improve the output. This is useful for enhancing blog posts, articles, and marketing materials.

Pro tips: At times, instead of asking it to generate an image, I ask it to propose a few image and explain why.

Also Read: How creativity, commerce and AI collide in mid-2026 marketing mix

Areas and applications of ChatGPT Canvas

  • Content marketing and social media

ChatGPT Canvas is ideal for creating structured marketing copy, including:

  • Facebook and Instagram ads
  • Email campaign templates
  • Social media captions and descriptions
  • Product descriptions and landing page copy

The ability to quickly generate multiple versions of ad copy allows marketers to test different messaging approaches efficiently.

  • Blog and article writing

For writers and bloggers, ChatGPT Canvas streamlines the content creation process by:

  • Generating structured outlines
  • Allowing easy edits within the interface
  • Improving formatting for web readability
  • Supporting markdown for seamless publishing

These features make it an excellent tool for drafting, refining, and finalising long-form content.

  • Scriptwriting and video content

YouTubers and video content creators can use ChatGPT Canvas to:

  • Generate structured video scripts
  • Adjust script length for different formats
  • Enhance clarity and readability with formatting tools

This feature simplifies the scriptwriting process and allows for easy collaboration with editors and co-creators.

The takeaway

ChatGPT Canvas is a powerful tool that enhances AI-generated content creation. By allowing direct editing, structured formatting, and content customisation, it significantly improves efficiency and quality. However, to maximise its potential, users should combine AI-generated content with personal insights, real-world examples, and authentic storytelling, seeing it as an aid rather than a replacement and engage in the mass generating of content that lacks depth, originality, and personal touch, ensuring that the final output resonates with audiences and maintains credibility. This ensures the content remains engaging, credible, and unique.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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The decision discipline: How to turn insights into action

Here’s a test you can run on your own organisation. Think of the last genuinely important decision your team made. Now ask: did the data shape that decision, or did someone make the call and then reach for the data to justify it?

If you’re honest, it’s usually the second one. And that tells you something most “data-driven” organisations don’t want to hear: they’re not data-driven at all. They’re report-driven. They’ve gotten extraordinarily good at producing and consuming data, and they’ve never actually learned to decide with it.

Seeing is not deciding

We’ve spent a decade conflating two completely different things. One is seeing data, building the dashboard, running the report, watching the metric. The other is deciding with data, letting what you see change what you do, and then committing to that change.

Everything in the modern analytics stack optimises for the first. Prettier dashboards, faster pipelines, more metrics, real-time everything. And it’s all beside the point if the thing the data implies never actually gets done. A dashboard that changes no decision is just expensive decoration.

The appetite to close this gap is enormous, by the way. Salesforce’s 2026 research found that 93 per cent of business leaders say they’d perform better if they could just ask questions of their data in plain language. Read that as what it actually is: a near-universal admission that the data is there and people still can’t easily turn it into action. The bottleneck was never seeing. It’s the leap from seeing to deciding, and no amount of dashboard polish closes it.

Also Read: The environmental ethics of AI should be a product decision, not a sustainability footnote

Why we hide in reports

Report-driven cultures aren’t stupid. They’re rational responses to incentives, and the incentives are backwards.

Producing a report is safe. Making a decision is exposed. If I build you a dashboard and the business goes sideways, that’s not on me, I delivered the data. If I make the call the dashboard implied and I’m wrong, that’s very much on me. So smart, self-preserving people accumulate reports as a way of looking rigorous while avoiding the risky act of commitment. The dashboard becomes a place to hide.

Then there’s the certainty trap. People tell themselves they can’t decide yet because the data isn’t complete, so they ask for more. But more data rarely produces more clarity; it usually just produces more delay and a false sense that certainty is coming. It isn’t. Most real decisions have to be made on a strong-enough signal, not a perfect one, and a culture that waits for perfect is a culture that decides late, every time.

And underneath both: nobody’s accountable for the decision. We measure whether the report shipped on time. We almost never measure whether a clear signal in that report actually changed what anyone did. We’ve instrumented the production of insight and left the use of insight completely dark.

The fix is human, not technical

Here’s the part that should be encouraging. Because this is a behavioural problem, not a technology one, it’s fixable, and fixable without buying anything.

Start every analysis from the decision, not the question. “What will I do differently depending on the answer?” If nothing, don’t build the report. That one discipline kills half the dashboards nobody uses and forces every remaining insight to have somewhere to go.

Also Read: How AI and blockchain could make commerce decisions more accountable

Give people explicit permission to act on strong-enough signals. Name the difference between reversible and irreversible decisions, you can move fast and loose on the reversible ones, and most decisions are more reversible than people treat them.

And close the loop, relentlessly. What did we predict? What happened? What did we learn? Organisations that revisit their decisions build judgement that compounds. Organisations that don’t repeat the same hesitation forever, decision after decision, learning nothing.

The reframe

So stop measuring your dashboards and start measuring your decisions. Not how much data you have, every competitor has data. Not how many reports you produce, reports are cheap. Measure whether decisions actually change because of what the data showed, whether they get made fast enough to matter, and whether your organisation is getting better at making them over time.

The companies that win the next decade won’t be the ones with the best dashboards. Plenty of people will have great dashboards. They’ll be the ones whose people can stand in front of the data and decide, quickly, under uncertainty, and honest enough to check whether they were right.

Your dashboards are fine. That was never the problem. The problem is that you’re all looking at them, nodding, and then not deciding anything.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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The AI dashboard gold rush: Beyond the pretty charts

Claude dashboards have become a kind of “AI flex” on Instagram recently, especially among founders, marketers, finance people and operators.

The biggest reason is that dashboards suddenly became very easy to make and very easy to share. Claude Code can now turn data or work from a session into an interactive Artifact: charts, tables, filters, KPI cards, timelines, and so on, and publish it as a webpage. Anthropic specifically promotes dashboards as one of the uses for Claude Code Artifacts.

That’s a meaningful shift. A year ago, a founder wanting something like this had two options: pay a developer to build it, or live with a spreadsheet nobody opens. Now the barrier is closer to zero, which is exactly why it’s spreading so fast. It’s not that the underlying analysis got smarter. It’s that the packaging got trivially easy.

And dashboards are perfect social-media content. Compare these two:

“I asked AI to analyse my business,”

versus a screenshot showing:

  • REVENUE RM183,420 (US$45,368) up 18.4 per cent
  • CONVERSION 3.8 per cent up 0.7 per cent
  • TOP PRODUCT: Necklace

charts, graphs, customer segments, forecasts.

The second one looks like someone built a sophisticated internal software system. That’s highly shareable even when the underlying data analysis isn’t particularly complicated.

Dashboard does not equal business intelligence. A beautiful dashboard made from bad data is just a beautiful way of making a bad decision. If the revenue figure is wrong, or the “top product” metric is counting returns as sales, none of that shows up on the screenshot. It just looks confident. That’s the trap: the format signals rigour whether or not the underlying numbers earned it.

Also Read: In Southeast Asia, going global used to mean picking the biggest market, that logic is already dead

For a business, I’d rank the value like this:

  • Reliable underlying data
  • Correct KPIs
  • Useful business questions
  • Analysis and decision rules
  • Dashboard design

Instagram naturally makes dashboard design look like the important part because that’s what photographs well. Nobody posts a screenshot of “we finally reconciled our SKU-level cost data,” even though that’s usually the unglamorous work that makes everything above it trustworthy.

For something like a jewellery business, for example, I’d much rather have a relatively boring dashboard that tells you sales, gross profit, SKU sell-through, inventory ageing, booth ROI, customer basket size, channel profitability, and flags where money is being lost, than a gorgeous 20-chart dashboard nobody actually uses. One of those tells you to reorder before you run out of your best-selling piece. The other just looks good in a founder’s story highlights.

So the trend itself is useful, but the opportunity isn’t “I should make a Claude dashboard too.” It’s “I can now build a lightweight management system without paying someone to develop one from scratch.” That’s much more interesting, and it’s the part that doesn’t screenshot nearly as well.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Why podcasts are the next big data revolution

Podcast production has exploded. The number of episodes published annually grew from roughly 3.9 million in 2015 to around 29 million in 2023.

Hours of valuable information are shared every day through this long-form audio. Yet despite how much useful information is buried inside podcasts, there still isn’t a comprehensive way to index them.

We have searchable indexes for news and databases for financial filings and academic papers. So what makes podcasts so much harder?

Having worked on indexing podcast content, I think the difficulty comes down to four problems: transcription, fragmented sources, content quality, and speaker identification.

Podcasts are audio first

Before you can make sense of a podcast, you first have to turn it into text. That sounds straightforward, especially given how much speech-to-text models have improved. But transcription is still far from a solved problem when you care about accuracy.

Proper nouns are a particularly difficult problem. Speakers constantly mention company names, product names, people, tickers, and industry-specific terminology that transcription models may not recognise. A company like Lyft, for example, can easily become “lift,” or Claude can become “cloud.”

This is a huge issue, especially when you are trying to build a searchable index. If the company name itself is transcribed incorrectly, the episode may never appear when someone searches for it. You need another layer that understands the context and corrects these transcription errors. The problem becomes even more challenging with different accents, speaking styles, recording environments, overlapping speakers, and poor audio quality.

The news on the other hand doesn’t have this problem. No form of processing is required as it’s already in the text form.

With podcasts, transcription is an additional computational step before indexing can even begin. At a large scale, that becomes a meaningful cost.

The source landscape is fragmented

The second problem is fragmentation. The barrier to creating a podcast is extremely low. Unlike traditional media, where a relatively small group of publications accounts for much of the trusted coverage, valuable podcast content can come from almost anywhere.

This is partly what makes podcasts interesting. You get perspectives, conversations, and expertise that would never appear in traditional media. But it also makes indexing them much harder. With news, there are a relatively limited number of publications that people consistently trust. You can get by with just indexing the major outlets.

Also Read: Asia’s AI trust gap: strong transparency, weak security and unclear data practices

Podcasts work very differently. A valuable piece of information might come from a huge show, a niche industry podcast, an independent expert, or a founder appearing on a tiny podcast with only a few thousand listeners. That means you cannot simply identify a few hundred trusted sources and call the job done. To build a useful podcast index, the coverage has to be dramatically wider. The long tail is not optional. It is often where the most interesting information lives.

That creates a scale problem that is easy to underestimate until you actually try to build it.

Content provenance and noise

The lack of editorial boundaries creates another problem: noise. AI-generated podcasts have become increasingly common. In our own work with financial podcasts, we have seen roughly 15 per cent of the content we encounter appear to be AI generated. Identifying and filtering this content is becoming surprisingly difficult.

I have worked around voice AI since 2023, and I used to think I had a good ear for identifying synthetic voices. I am far less confident today. As voice models improve, identifying AI voices has become a challenge.

There are other forms of duplication too. Podcasters frequently publish clips from other podcasts (reactions). An indexing system has to understand the difference. Is the person speaking actually a guest on this podcast? Is this a clip from another show? These problems can be solved using LLMs these days.

Not to forget, podcast advertising introduces complications. Podcasts increasingly use dynamically inserted ads, meaning the audio file itself can change depending on when or where it is played. Unlike an article sitting at a fixed URL with pretty much fixed content, podcast content is not always static.

Knowing what was said isn’t enough

The final problem may be the most important: speaker identification. Knowing that a statement was made is useful. Knowing who made it is far more useful.

Imagine someone saying that a particular company has an enormous competitive advantage. The meaning of that statement changes depending on whether the speaker is the company’s CEO, a competitor, an investor, a customer, or an independent industry expert.

The words might be identical but the perception will change accordingly. A useful podcast index needs to understand who is speaking and what their relationship is to the subject.

This is one area where modern AI models come in handy. Given enough context, they can often identify speakers, infer roles and resolve ambiguous references.

Also Read: Why Singapore’s AI finance race is now about data, not models

AI changes what is possible

A few years ago, building a comprehensive podcast index would have been theoretically unfeasible. You would need to transcribe millions of hours of audio, fix errors, identify speakers, and continuously process a huge stream of new episodes.

The only probable way would have been to hire troves of human annotators. LLMs and modern speech models change the economics of that problem. For the first time, it is becoming practical to turn podcasts from an audio format people have to manually consume into a structured data source that machines can understand. And that matters because the information inside podcasts is unusually valuable.

Executives explain how they think. Investors discuss their theses. Researchers describe work that may never appear in a paper. Founders talk about their companies in more detail than they would in a press release. Industry experts casually reveal insights buried inside hour-long conversations.

Until now, most of that information disappeared into the podcast feed after it was published. We are finally reaching the point where it doesn’t have to. Now that podcasts can be indexed, the more interesting question is what we can build once all of that information becomes searchable.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsAppInstagramFacebookX, and LinkedIn to stay connected.

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