In March, Thailand’s exports reached a record high due to strong demand for technology and AI-related products. However, officials cautioned that challenges such as global economic uncertainties might affect future growth. The surge highlights Thailand’s increased role in tech markets, though maintaining momentum may require navigating external economic pressures.

Thailand’s exports reached a record high in March, hitting $35.1 billion, which is an 18.7% increase year-on-year. This growth marks the 21st consecutive month of export expansion, contributing to a total first-quarter growth of 17.6%.

Key takeaways

• The electronic sector and ongoing momentum in AI technology are expected to continue supporting export growth in the near future.
• Officials warned of potential risks ahead, including geopolitical tensions, fluctuating energy prices, and rising inflation.
• Thailand recorded a trade deficit of $3.3 billion in March as imports surged by nearly 36%.
• The country maintains a trade surplus with the United States but runs a deficit with China due to high imports of raw materials and machinery.

The surge was primarily driven by strong global demand for technology and AI-related products. Industrial goods showed the strongest performance, growing by over 21%, with high demand for computers, data center equipment, and mobile phones.

The electronics sector experienced a significant boost, with increased orders from key partners in Asia and Europe. Simultaneously, the automobile industry benefited from innovations in eco-friendly vehicles, capturing the attention of environmentally conscious consumers worldwide. Agricultural exports, including rice and rubber, also grew substantially, benefiting from favorable weather conditions and improved farming techniques.

This record-breaking performance underscores Thailand’s capacity to adapt and thrive. By fostering innovation and global collaboration, Thailand continues to bolster its economic growth, setting a promising trajectory for the future. The government remains committed to sustaining this momentum by exploring new markets and investing in sustainable technologies.

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The rising cost of digital advertising has made it harder than ever for small businesses to grow. Research shows that customer acquisition costs (CAC) have jumped by nearly 60% in the last five years. For a business owner, it can feel like you’re spending more and more just to stand still.

However, things have changed dramatically in 2026. AI is not a luxury anymore, but rather an effective tool helping small teams operate more efficiently. Leveraging AI to perform boring tasks such as processing data and automation, one can concentrate on real decision-making that generates profits.

As the CEO of UK-based B2B SEO agency, Push Group, says, “AI can take a huge amount of manual load out of the system, but it can’t replace strategic judgment, brand nuance, or the commercial decisions that move performance.” This is the essence of marketing today: leveraging technology to locate your customers’ whereabouts, then leveraging your own skills to make the sale. By ceasing to pay for time spent on tedious tasks by humans and focusing instead on AI-based strategies, you can stretch your budget further than ever before.

Let’s dive into five practical ways AI marketing is cutting costs and making small businesses more profitable.

1. No More Guesswork in Your Ad Spend

One of the biggest money pits for small businesses is running ads that don’t convert. Traditionally, you’d set a budget, pick some keywords, and hope for the best. If it didn’t work, you’d lose that money.

AI changes the game with Predictive Analytics. Tools now analyze thousands of data points in real-time to see which ads are performing and which aren’t. Instead of waiting until the end of the month to see your results, AI can automatically shift your budget toward the winning ads and pause the losers.

• The Result? Businesses using AI-powered ad optimization see up to 30% higher ROI on their advertising spend compared to manual management.

2. Hyper-Personalization at Scale

Surely we have all gotten those “Hi [Name]” emails that seem impersonal and automated. Personalization does not mean greeting people by name in such emails; it means looking at their actual behavior on your site, such as  the products they click on, the time they spend looking at a certain item, and their past purchases.

Using AI, you can offer personalized experiences to each individual. Perhaps they will be offered an exclusive discount when they are just leaving or be suggested a product that suits their tastes.

• Why does it save money? When the content is relevant, people buy more. AI-driven campaigns can lead to a 32% increase in conversions, meaning you get more customers from the same amount of traffic.

3. 24/7 Support That Actually Sells

Hiring a full-time sales or support team to be available at 2 a.m. is impossible for most small businesses. But customers in 2026 expect instant answers. If they have to wait four hours for an email reply, they’ve already moved on to your competitor.

Modern AI chatbots aren’t the frustrating ‘I don’t understand’ boxes of the past. They are now Agentic, meaning they can actually help a customer book an appointment, track an order, or even qualify a lead.

• The Impact: By handling basic inquiries instantly, AI reduces the need for extra staff while ensuring you don’t lose leads due to slow response times. This can lower your overall acquisition costs by nearly 29%.

4. Smarter Lead Scoring

Not every lead is a good lead. Your team might spend hours calling people who were just window shopping and have no intention of buying. This is a massive waste of time and salary.

AI tools can now score your leads. They look at a person’s behavior and assign a probability of them actually making a purchase.

• The Strategy: By focusing your energy only on high-intent leads, your sales process becomes much more efficient. You aren’t just working harder; you’re working on the right people. This shift is crucial because, as Bill Gates famously said, “AI is the biggest productivity advance in our lifetime.” In the context of marketing, this productivity comes after the noise is cut and the focus is only on the customer.

5. Content Creation Without the Burnout

Creating blogs, social media posts, and newsletters is a full-time job. Many small business owners try to do it themselves, which takes them away from actually running their business.

AI doesn’t just write content; it helps you research what your audience is actually searching for. It can take one video and turn it into ten social media posts, three emails, and a blog summary.

• The Efficiency: UK marketers have reported that AI tools make their teams 76% more productive. Instead of spending $1,000 on a single campaign, you can use AI to stretch that content across multiple channels, drastically lowering the cost per post.

Putting It Into Action

If you’re feeling overwhelmed, don’t try to do everything at once. Start small:

1. Audit your ads: Use a basic AI tool to see which ones are actually making money.
2. Try a smart chatbot: Set up a simple AI assistant on your site to answer FAQs.
3. Repurpose your best content: Take your most popular blog and use AI to turn it into a week’s worth of social media updates.

The goal of AI in marketing isn’t to replace the human touch; it’s to remove the expensive, boring, and repetitive tasks that keep you from connecting with your customers. By making your marketing smarter, you don’t just save money; you build a business that is ready for the future.

Conclusion

Customer acquisition doesn’t have to be a debt trap. With AI, small businesses can finally compete with the big guys by being more precise, more personal, and much faster.

CUPERTINO, Calif. — Apple is preparing one of its most significant iPhone redesigns in years for the iPhone 18 Pro Max, expected to launch in September 2026, with supply chain leaks and analyst reports pointing to a dramatically thinner profile, a powerful new A20 chip, under-display Face ID and major camera advancements that could solidify its position as the premium flagship in Apple’s lineup.

Multiple reliable sources, including Bloomberg’s Mark Gurman and respected supply chain analyst Ming-Chi Kuo, suggest the iPhone 18 Pro Max will measure under 6mm thick when unfolded in its standard slab form — a noticeable reduction from the current generation while maintaining durability through advanced materials and engineering. This thinner design is said to be a priority for Apple as it competes with increasingly sleek Android flagships and prepares consumers for potential future foldable models.

The device is rumored to feature a custom A20-series processor manufactured on TSMC’s advanced 2nm process node, promising significant improvements in both performance and power efficiency. Apple Intelligence features are expected to expand dramatically, with on-device AI capabilities handling more complex tasks such as real-time video editing, advanced photo processing and personalized Siri interactions without relying heavily on cloud servers.

Camera System Set for Major Leap

One of the most anticipated upgrades involves the camera system. Reports indicate the iPhone 18 Pro Max could feature a new 48-megapixel tetraprism periscope telephoto lens with enhanced optical zoom capabilities, potentially reaching 10x optical zoom or better. The main sensor is also expected to see improvements in low-light performance and computational photography, building on the already strong foundation of previous Pro models.

Under-display Face ID is another major rumor gaining traction. Apple is reportedly close to perfecting the technology that would eliminate the Dynamic Island entirely, allowing for a true full-screen experience while maintaining secure biometric authentication. This change would represent the biggest visual redesign since the notch was introduced in 2017.

Design and Build Changes

Beyond thinness, the iPhone 18 Pro Max is expected to retain the titanium frame introduced with the iPhone 15 Pro series but with further refinements for weight reduction. New color options, including bolder finishes, are rumored to be in testing. The device is also said to feature improved thermal management to handle the increased performance of the A20 chip without compromising battery life.

Battery capacity is projected to increase modestly, supported by more efficient components and possibly new stacking technology. Combined with software optimizations, this could deliver noticeably better all-day battery life, a frequent request from Pro Max users who value the larger chassis for extended usage.

Pricing and Availability

Pricing is expected to start at $1,199 for the base iPhone 18 Pro Max, maintaining the premium positioning established in recent years. Higher storage variants and new color options could push top configurations well above $1,500. Initial supply is likely to be constrained, following Apple’s traditional pattern for major redesigns.

Analysts believe Apple will announce the iPhone 18 series during its annual September event, with pre-orders opening shortly after and general availability in late September or early October. The Pro Max model typically sees the strongest initial demand due to its larger display and advanced features.

Competitive Landscape

The iPhone 18 Pro Max will face stiff competition from Samsung’s Galaxy S26 Ultra, Google’s Pixel 11 Pro and various Chinese manufacturers pushing innovative foldables and camera technology. Apple’s strategy appears focused on refinement and ecosystem integration rather than radical experimentation, betting that its polished user experience and long-term software support will continue to win over customers.

The integration of Apple Intelligence across hardware and software is expected to be a major selling point. With on-device processing becoming more capable, the iPhone 18 Pro Max could offer privacy-focused AI features that competitors relying on cloud processing may struggle to match.

What It Means for Consumers

For loyal Apple users, the iPhone 18 Pro Max represents a meaningful upgrade worth considering, particularly for those seeking the best camera system, longest battery life and most refined software experience. The thinner design and potential under-display Face ID could make the device feel fresh despite the incremental nature of many annual updates.

Budget-conscious consumers or those with older models may choose to wait for the iPhone 19 series or consider current discounts on iPhone 17 Pro Max units. Trade-in programs and carrier deals are expected to make the upgrade more accessible for many.

Industry Implications

The development of the iPhone 18 Pro Max underscores Apple’s continued dominance in the premium smartphone segment. Its ability to command high prices while maintaining strong sales volumes remains the envy of the industry. Success with under-display Face ID and advanced AI features could set new standards that competitors will be forced to follow.

As leaks continue to surface in the months leading to the official announcement, excitement around the iPhone 18 Pro Max is only expected to grow. Whether the final product lives up to the rumors or delivers unexpected surprises, Apple’s next flagship is shaping up to be one of the most significant iPhone updates in recent memory — a device that could define the smartphone experience for the latter half of the decade.

Speaking to ET Now, he suggested that oil prices like Brent may remain elevated and that markets need to adjust to a prolonged phase of disruption rather than a temporary shock.

He said, “It seems like it is going to be a long drawn thing… Does it seem like $100 could be the new reality for Brent?” Freris argued that the strategic importance of the Strait of Hormuz is likely to decline over time as countries develop alternative supply routes.

According to him, “The Hormuz Straits are going to become irrelevant.” He added that energy exporters are already working on bypass mechanisms, saying, “Turkey is outlining pipework. Saudi Arabia already has bypass routes in place.” In his view, such adjustments will take time, making the transition disruptive but structural, and he described the outlook as a “two-year view” rather than a short-term market event.

On equity markets, Freris noted the disconnect between record highs and underlying fundamentals. While global indices continue to scale new peaks, he said the rally is being driven largely by artificial intelligence-related enthusiasm rather than broad-based earnings strength.

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Responding to a question on why markets remain firm despite global uncertainty, he said, “It is driven by artificial intelligence.” He cautioned that valuations are being pushed higher without sufficient earnings justification, adding, “We will push the S&P to highs on AI, but there is no real earnings justification.” He also expressed discomfort with the level of concentration in US benchmarks and advised caution, stating, “I am telling clients to reduce US positions.” At the same time, he pointed out that several Asian markets have performed better in dollar terms compared to the S&P.
Freris further highlighted the uneven nature of US corporate earnings, noting that while some sectors remain strong, especially those linked to technology and AI, the overall picture is inconsistent. He said, “Earnings from AI and IT are variable.” He also warned that expectations around investment in artificial intelligence may be overstated, remarking, “Investment in AI is massively exaggerated.” According to him, the heavy reliance on a small group of large-cap stocks makes the broader index vulnerable, as “the S&P is driven by about 10 stocks,” which he believes creates discomfort from a valuation and risk standpoint.Overall, Freris sees a widening gap between markets and macro risks, with energy markets adjusting to long-term geopolitical realignments while equity markets remain heavily dependent on a narrow AI-driven narrative. He suggests that both themes—structural energy disruption and concentrated equity leadership—are likely to define global markets over a multi-year horizon rather than in the near term.

LOS ANGELES — Taylor Swift has filed applications with the U.S. Patent and Trademark Office to trademark her voice and likeness, a significant and forward-looking intellectual property strategy designed to protect her unique artistic identity from unauthorized commercial exploitation, particularly in the rapidly advancing era of artificial intelligence and deepfake technology.

The filings, submitted in recent weeks and first reported Monday, cover an extensive range of categories including sound recordings, live performances, merchandise, digital content, advertising, and entertainment services. Legal experts describe the move as both ambitious and timely, reflecting Swift’s determination to maintain control over one of her most valuable assets — her distinctive voice and public image — as generative AI tools make it easier than ever to replicate celebrities.

By seeking trademark protection for her voice, Swift aims to establish legal grounds to challenge unauthorized uses in commercials, AI-generated songs, virtual performances, and other commercial contexts. Her likeness filing extends beyond traditional name and signature trademarks to cover visual representations closely associated with her brand, including signature poses, fashion aesthetics, and overall persona.

Strategic Move in the AI Era

The timing of the filings is notable. As AI technology has advanced, concerns have grown across the entertainment industry about unauthorized voice cloning and deepfake videos. Swift has been vocal in the past about protecting artists’ rights in the digital age, and these applications represent a concrete step toward safeguarding her work and image from potential misuse.

Intellectual property attorneys say voice trademarks are challenging to secure but not impossible, especially for artists with highly recognizable vocal styles. Swift’s application focuses on specific characteristics and commercial contexts, which could strengthen her position if approved. Similar efforts by other major artists have gained traction in recent years as the legal system catches up with technological realities.

Swift’s Expanding Business Empire

The trademark push fits into Swift’s broader evolution from music superstar to one of the most successful business figures in entertainment. Her Eras Tour generated over $1 billion in revenue, making it the highest-grossing tour in history. Her decision to re-record her early albums gave her greater ownership and control over her catalog, a move that has proven both artistically and financially rewarding.

These latest filings further solidify her position as a business-savvy artist who understands the importance of intellectual property in the modern music industry. Swift has built a powerful personal brand that extends far beyond music into fashion, philanthropy, and cultural influence. Protecting that brand comprehensively is a logical next step.

Fan and Industry Reaction

The news has sparked widespread discussion among Swift’s global fanbase, known as Swifties. Many praised the move as smart and necessary, with comments highlighting concerns about AI-generated “fake Taylor” content that has proliferated online. Others expressed curiosity about how such trademarks would be enforced in practice, particularly regarding fan art, social media content, and non-commercial uses.

Music industry executives view the filings as part of a growing trend. As generative AI becomes more accessible and sophisticated, major artists and their teams are increasingly focused on protecting voice, likeness, and overall brand identity. Swift’s high-profile action could set a precedent for others in the industry.

Potential Legal Implications

If approved, the trademarks would give Swift stronger legal tools to combat unauthorized commercial uses. This could be particularly relevant for AI-generated music, virtual concerts, advertising campaigns, and merchandise that mimics her image or sound without permission.

However, the applications will face examination by the U.S. Patent and Trademark Office, a process that can take months or years. Opposition from other parties is possible, and approval is not guaranteed, especially for the voice component, which is traditionally more difficult to protect than visual likeness.

Broader Context in Celebrity IP

Swift joins a growing list of celebrities seeking expanded intellectual property protections. The rise of AI has accelerated this trend, as traditional copyright and right of publicity laws struggle to keep pace with new technologies. Courts and lawmakers are increasingly being asked to address how far an individual’s control over their voice and image should extend in the digital realm.

For Swift, these filings represent more than legal defense — they reflect a strategic approach to career management that has consistently maximized both creative control and financial returns. At 36, she continues to expand her influence while maintaining tight oversight of her brand.

Looking Ahead

The U.S. Patent and Trademark Office will now review the applications. Regardless of the final outcome, the filings signal Swift’s proactive stance on protecting her legacy in an increasingly complex digital landscape. As AI technology continues to evolve, such measures are likely to become standard for major artists seeking to maintain control over their work and public image.

For fans, the news reinforces Swift’s reputation as both a creative powerhouse and a shrewd businesswoman. As she continues releasing music and planning future projects, these intellectual property moves ensure she remains in control of how her voice and likeness are used — preserving the authenticity that has defined her career from the beginning.

Taylor Swift’s latest trademark filings represent a significant step in the ongoing evolution of celebrity rights in the digital age. By moving to protect her voice and likeness, she is not only safeguarding her brand but also helping shape the conversation around AI, creativity, and intellectual property in the modern entertainment industry.

AI isn’t failing because models or platforms fall short. It’s failing because legacy ETL cannot support continuous, reliable execution at scale.

As enterprises move from analytics to AI-driven workflows, the constraint shifts from building systems to trusting them to run.

Through Maia, AI Data Automation is emerging as a new architectural layer, embedding pipeline logic directly into the data environment and eliminating external dependencies.

Why AI systems fail in execution: not development

Enterprises have invested heavily in AI, 77% of CEOs now say it will have the single most significant impact on their industry by 2028.

The platforms are in place. The mandate is clear.

But many organizations are beginning to face a harder question: not whether they can build AI, but whether they can operate it reliably enough to trust it with real business processes.

AI models are built. Pilots succeed. And then progress slows, sometimes quietly, sometimes all at once.

Not because the models don’t work. And not because the platforms aren’t capable.

Because the data layer underneath them, often built on legacy ETL pipelines, cannot sustain continuous execution.

The constraint isn’t new. The stakes are.

Most enterprise data environments were designed for analytics.

Pipelines run on schedules. Data moves in batches, often across separate systems that must extract, move, and rebuild data before it can be used. When something breaks, an engineer investigates.

That model worked when workflows moved at human speed.

AI changes the equation.

Now, systems depend on continuous data pipelines and reliable operational signals.
When those systems fail, the impact is immediate, models stop retraining, applications lose context, and decisions become unreliable.

In some cases, the failure is even more visible: an automated workflow halts mid-process because an upstream pipeline didn’t complete, or worse, completes with stale data no one realizes is wrong.

This is the same pattern many teams are now recognizing as the Velocity Gap, the growing distance between AI ambition and production reality.

At its core, the issue isn’t a lack of tooling or investment.
It’s that the data layer required to support continuous execution was never designed for it.

As the stack moves up, the foundation matters more

The industry is moving beyond analytics.

New execution layers, workflow engines, agentic platforms, and emerging capabilities like Snowflake’s SnowWork, promise to automate business processes end to end.

The architects building these platforms are clear-eyed about it: autonomous execution agents are only as reliable as the data they operate on. A flawed upstream pipeline doesn’t just break a report, it generates a confident, wrong answer at machine speed.

But these systems operate on top of the data layer. And in most enterprises, that layer is still governed by legacy ETL.

These platforms assume data is continuously available, governed, and production-ready.

In reality, it is often manually maintained, fragmented across tools, and dependent on human intervention to recover when something breaks.

Execution doesn’t scale, it becomes inconsistent.

And at that point, the risk isn’t delay. A single pipeline failure in a production AI system can stall downstream inference across every workflow it feeds, often requiring hours of manual intervention. It resets confidence for every business stakeholder watching the rollout.

It’s that the system cannot be trusted to run.

The real gap is data readiness for AI

This is why so many AI initiatives fail to move beyond pilot.

Not because the models aren’t effective, but because the data required to sustain them across AI systems cannot be delivered reliably, continuously, and at scale.

And even when organizations attempt to modernize, the challenge often persists, because execution still depends on systems operating outside the core data environment, introducing latency, fragmentation, and control gaps.

As AI systems move from analytics to execution, the limitations of analytics-era data architecture become harder to ignore.

A data layer that depends on external engines to move, transform, or repair data before it can be used cannot support continuous model retraining, real-time decisioning, or autonomous business workflows.

Until that changes, AI remains constrained, not by innovation, but by execution.

A new layer is emerging

This is why a new layer is taking shape: AI Data Automation.

Not as another tool in the stack, but as a new layer for AI Data Automation: a fundamentally different operating model for how data work gets done.

The shift is away from human-managed pipelines and reactive maintenance toward continuous execution, where pipelines are created, maintained, and governed automatically, without external dependency, handling schema drift, quality issues, and optimization autonomously.

Maia, the AI Data Automation platform, is where this shift becomes operational, removing the need for external systems to build, maintain, and repair pipelines, and embedding that logic directly into the data environment itself.

The goal isn’t faster development. It’s something more fundamental.

A data layer that can support the continuous execution of AI systems, without depending on humans to keep it running.

Execution is the real measure of AI

AI doesn’t fail because the platforms aren’t capable.

It fails because the data layer cannot reliably support, or be trusted to sustain, the systems built on top of it.

Until that changes, every new layer of innovation will inherit the same constraint.

And the gap between ambition and outcome will continue to grow, the very definition of the Velocity Gap.

Book a Maia demo to see how AI Data Automation changes the equation.

A couple of weeks ago, Meta’s (META) internal dashboard for “tokenmaxxing” got a lot of attention after The Information reported (subscription required) on it. Although such tokenmaxxing phenomena only started getting attention recently, Shopify (