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Perplexity’s Deep Research Tool is Reshaping Market Dynamics
Perplexity’s Deep Research tool is challenging premium AI subscriptions by offering advanced research capabilities at a fraction of the cost
TL;DR – What You Need to Know in 30 Seconds
- Perplexity’s Deep Research tool offers advanced AI research capabilities for a fraction of typical enterprise costs.
- It provides five free queries daily and charges $20 per month for 500 queries—compared to big AI providers charging thousands.
- Scored 93.9% on SimpleQA and 20.5% on Humanity’s Last Exam, outpacing Google’s Gemini Thinking, with OpenAI only slightly higher at 26.6%.
- Enterprise AI spending is projected to rise by 5.7% in 2025, although some companies are increasing their AI budget by 10% or more.Deep Research could shift the market by making companies question premium AI subscriptions that cost up to 100x more.
- The tool handles a range of tasks (healthcare, finance, market research) in under three minutes, democratising AI for smaller businesses and individuals.
- This affirms a new era in AI, where affordability meets performance, and big spenders must now justify their exorbitant costs.
Unpacking Perplexity Deep Research Tool, and its Impact
Today, we’re diving into one of the most talked-about innovations in AI right now: Perplexity’s new Deep Research tool. If you haven’t heard of it yet, don’t fret—this is precisely what we’re here for. Grab your favourite cuppa, because we’re about to explore how Perplexity is turning AI research upside down, smashing cost barriers, and making us question every pricey AI subscription that’s ever crossed our desks. Sound good? Let’s get stuck in!
The Big Bang of Affordable AI
You know how some products come along and make you wonder why you ever paid so much for something else? That’s exactly what’s happening with Perplexity’s Deep Research. In a single, bold move, Perplexity has basically told the rest of the AI industry: “We’re here, we’re cheap, and we’re not messing about.” If you haven’t caught wind of it, Deep Research is a tool that can generate comprehensive research reports in just minutes. Yes, minutes. And here’s the kicker: it offers advanced AI capabilities at a fraction of the typical enterprise costs.
Take a look at what’s on the table: while Anthropic and OpenAI can easily charge into the thousands every month for their premium services, Perplexity is throwing in five free queries daily for all users and an upgrade at $20 per month for 500 daily queries plus faster processing speeds. That’s not just cheaper; it’s borderline scandalous when you see that other AI giants charge almost 100 times more for near-similar (and, in some cases, arguably lesser) capabilities.
But it’s not just a marketing gimmick. Aravind Srinivas, Perplexity’s CEO, shared the company’s ethos on X (formerly Twitter), saying, “Knowledge should be universally accessible and useful. Not kept behind obscenely expensive subscription plans that benefit the corporates, not in the interests of humanity!” It’s hard not to be inspired by that. The democratisation of AI has long been touted as the Next Big Thing in tech, but Perplexity is making some serious strides to actually achieve it, rather than just talk about it.
Deep Research is now a commodity thanks to Perplexity pic.twitter.com/Fk8yvPTLzV— Aravind Srinivas (@AravSrinivas) February 14, 2025
Enterprise AI Spending Under the Microscope
As you might guess, this sudden plunge in price is raising eyebrows—big time. Large enterprises have been funnelling massive budgets into AI, with some expecting to increase their AI spending by 5.7% in 2025. That’s despite overall IT budgets going up by less than 2%. In certain sectors, that surge in AI spending could be as high as 10%, and on average, some businesses plan to throw in an additional $3.4 million into AI initiatives. With the rise of Deep Research, though, those expensive subscriptions now look a little, well, questionable.
Let’s be real. When you’ve got a brand-new AI tool that gives near-enterprise level performance (and sometimes even more advanced capabilities) for $20 a month, it begs the question: What are we actually paying for with those premium AI subscriptions? If you’re on the corporate side, you might be reviewing your budgets as we speak. Think about the training, the data hosting, the staff overhead—yes, those are real costs. But are they enough to justify a 100x difference in price?
Technical Mastery That’s Giving Giants a Run for Their Money
Now, let’s talk numbers, because who doesn’t love a good metric? Perplexity’s Deep Research scored a whopping 93.9% accuracy on the SimpleQA benchmark and clocked 20.5% on Humanity’s Last Exam. If you’re wondering why that second number is interesting, consider that it outperforms Google’s Gemini Thinking and other top-tier models. Even more eyebrow-raising is that OpenAI scores 26.6% on Humanity’s Last Exam—yes, that’s higher than Perplexity’s 20.5%—but let’s not forget the monstrous cost difference for that extra 6 percentage points.
Perplexity also claims that Deep Research completes most tasks in under three minutes, performing dozens of searches and analysing hundreds of sources simultaneously. That’s lightning-fast by any measure, especially when you realise it’s essentially replicating what expert human researchers would do—but in a fraction of the time. For advanced tasks like financial analysis, market research, technical documentation, or even healthcare insights, it’s an absolute game-changer.
Why This Matters to You (and Everyone Else)
Alright, it’s cheap, it’s fast, and it’s accurate. Who cares, right? Well, pretty much anyone who’s ever wanted to make use of advanced AI capabilities but balked at the price tag. It’s no secret that enterprise AI has often ended up creating a digital divide: if you’ve got the budget, you can do some serious data-crunching, but if not, you’re left in the dark ages. This means smaller businesses, individual researchers, students, or freelancers could only dream of some of these AI services because they couldn’t justify the cost.
But along comes Perplexity, democratising the whole playing field. The potential is enormous. If you’re a small tech start-up, you no longer have to pay thousands just to get your data insights. Researchers can use Deep Research for thorough academic or industry analyses. Professionals in healthcare or finance can produce detailed reports that would usually cost an arm and a leg. And because Perplexity plans to expand Deep Research to iOS, Android, and Mac platforms, access is only going to get easier.
Is Enterprise AI in for a Shake-up?
If you’re in charge of procurement or strategic decisions for a big firm, your job just got a bit more complicated. Do you stick with the big-name provider with that hefty subscription fee, or do you try Perplexity to see if it meets your organisation’s needs? The key question is: Are you really getting the added value for your money when your monthly AI bills are in the thousands?
Sure, there could be a few reasons to keep paying extra. Perhaps you’re already deeply integrated with a certain AI ecosystem, or you need custom solutions that only a big player can provide. Maybe you rely on dedicated customer support that’s included with your pricy subscription. But the argument that premium cost automatically translates to premium capability is quickly losing steam.
With Perplexity’s impressive performance, we might see a future where expensive enterprise AI tools have to scramble to prove they’re worth it. You can’t just plaster “enterprise-grade” on a service and watch the money roll in—users want tangible, cost-effective results.
How Deep Research Outperforms (and Where It May Still Lag)
Let’s not gloss over the fact that OpenAI’s own research capabilities still technically inch out ahead in certain benchmarks. A 26.6% score on Humanity’s Last Exam compared to Perplexity’s 20.5% might be a big deal for mission-critical tasks in specialised domains. Then again, Perplexity’s 93.9% on SimpleQA is hardly peanuts. And let’s remember the price difference—OpenAI can charge hundreds (if not thousands) of percent more. So is that extra 6 percentage points in performance worth the colossal increase in cost?
It all boils down to your use case. If you’re a hedge fund manager who needs the absolute best of the best and every fraction of a percent could mean millions in revenue, you might still throw your money at the top-of-the-line model. But if you’re a mid-sized firm or an independent researcher, Perplexity’s offering is more than enough—especially at $20 a month.
Practical Implications: From Healthcare to Finance
Let’s look at some real-world scenarios. Healthcare professionals can use Deep Research to scour medical journals, clinical trial results, and official guidelines faster than you can say “NHS queue”. This means better patient outcomes, quicker insights, and less reliance on massive IT budgets.
Financial analysts can crunch market data, follow the latest economic news, and whip up in-depth reports that previously needed entire teams of well-paid data scientists. Technical documentation tasks become a breeze when Deep Research can parse through troves of manuals, development forums, and official documents in minutes.
Plus, Perplexity’s user-friendly features—like exporting findings as PDFs or sharing them directly through its platform—make collaboration straightforward. If you’ve ever had to wrestle with clunky enterprise software, you’ll appreciate the simplicity that Perplexity offers.
The Democratisation Ripple Effect
We’ve talked about how smaller entities stand to benefit from cheaper AI tools. But let’s not forget the social dimension. When you lower the barrier to entry, you empower not just businesses, but also students, civil society organisations, journalists, and independent researchers. Knowledge stops being locked behind corporate walls. That’s a big deal in Asia—where the digital transformation wave is sweeping nations at very different speeds and scales.
Imagine an NGO in a rural part of Southeast Asia that can now access top-notch AI research capabilities for $20 a month. That’s a giant leap forward in bridging the digital gap, enabling them to better serve local communities, gather data, and deliver more effective programmes. It’s not just a business story; it’s a social justice story too.
What’s Next in 2025 and Beyond?
Given that AI spending is expected to rise by 5.7% in 2025, the question on everyone’s lips is how this new wave of budget-friendly AI offerings will redistribute the market. Will companies continue throwing millions at established AI giants, or will they pivot to nimble, cost-effective alternatives like Perplexity?
In many ways, this sets the stage for an AI arms race of affordability and performance, where large players need to prove they’re worth the extra cash—or risk losing market share. From what we’re seeing, the AI community (and the public) are hungry for an open-source, reasonably priced alternative. Perplexity’s decision to offer a free daily query allowance and then a generous 500 queries a day for a mere $20 might be the blueprint for the future of AI subscription models.
The Jury’s Verdict and a New Era
So, does this mark the end of expensive AI subscriptions? We’ll have to wait and see. But one thing is clear: Perplexity’s Deep Research has seriously called into question the notion that you need to pay through the nose for quality AI. If performance is almost on par with more expensive services, or in some benchmarks better, why wouldn’t you jump on board?
Perhaps the biggest indicator of success will be user adoption. And it’s already looking promising—thousands of folks have begun testing Deep Research, singing its praises, and pondering whether they really need those thousand-dollar monthly fees. In the dynamic, ever-shifting AI world, the best technology won’t be the one with the biggest marketing budget but the one that’s truly accessible to the people who need it most.
There you have it, folks: a whirlwind tour of how a single innovation from Perplexity is rattling the foundations of AI’s business model. Whether you’re a budding researcher, a startup founder, or a corporate decision-maker, the paradigm is changing right before your eyes. Will you be part of the revolution—or left clinging to yesterday’s overpriced subscriptions? The choice, as always, is yours! And don’t forget to subscribe to keep up to date with all the latest AI happenings in Asia.
What Do YOU Think?
As AI costs plummet and quality soars, will businesses continue to pay premium prices out of habit—or dare to embrace the affordable future? Let us know in the comments below!
You may also like:
- Perplexity Deep Research Tool Debuts, Challenging OpenAI and Google
- Perplexity Assistant: The New AI Contender Taking on ChatGPT and Gemini
- Or try Perplexity AI by tapping here.
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Adrian’s Arena: Stop Collecting AI Tools and Start Building a Stack
How to transform scattered AI tools into a strategic stack that drives real business outcomes. Practical advice for startups and enterprises.
TL;DR — What You Need To Know
- Stop collecting random AI tools and start building an intentional “stack” – a connected system of tools that work together to solve your specific business problems.
- The best AI stacks aren’t complicated but intentional – they reduce friction, create clarity, and become second nature to your team’s workflow.
- For Southeast Asian businesses, successful AI stacks must address regional complexities like language diversity, mobile-first users, and local regulations.
Why Your AI Approach Needs a Rethink
Look around and you’ll see AI tools popping up everywhere – they’re like coffee shops in Singapore, one on every corner promising to give your business that perfect boost.
But here’s what I keep noticing in boardrooms and startup meetings: everyone’s got tools, but hardly anyone has a proper stack.
Most teams aren’t struggling to find AI tools. They’re drowning in disconnected tabs – ChatGPT open here, Perplexity bookmarked there, Canva floating around somewhere, and that Zapier automation you set up months ago but barely remember how to use.
They’ve got all the ingredients but no kitchen. No real system for turning all this potential into actual business results.
AI stack vs. tool collection
It’s so easy to jump on the latest shiny AI thing, isn’t it? The hard part is connecting these tools into something that actually moves your business forward.
When I talk to leaders about building real AI capability, I don’t start by asking what features they want. I ask what problems they’re trying to solve. What’s slowing their team down? Where are people burning valuable time on tasks that don’t deserve it?
That’s where stack thinking comes in. It’s not about collecting tools – it’s about designing a thoughtful, functional system that reflects how your business actually operates.
The best AI stacks I’ve seen aren’t complicated – they’re intentional. They remove friction. They create clarity. And most importantly, they become second nature to your team.
Building Intentional AI Workflows
For smaller teams and startups, an effective AI stack can be surprisingly simple. I often show founders how just four tools – something like ChatGPT, Perplexity, Ideogram, and Canva – can take you from initial concept to finished marketing asset in a single afternoon. It’s lean, fast, and totally doable for under $100 a month. For small businesses, this kind of setup becomes a secret weapon that levels the playing field without expanding headcount.
But once you’re in mid-sized or enterprise territory, things get more layered. You’re not just looking for speed – you’re managing complexity, accountability, and scale. Tools need to talk to each other, yes, but they also need to fit into approval workflows, compliance requirements, and multi-market realities.
That’s where most random collections of tools start to break down.
When Your AI Stack Actually Works
You know your AI stack is working when it feels like flow, not friction.
Your marketing team moves from insight to idea to finished asset in hours instead of weeks. Your sales team walks into meetings already knowing the context that matters. Your HR people personalise onboarding without rebuilding slides for every new hire.
This isn’t theoretical – I’ve watched it happen in real organisations across Southeast Asia, where tools aren’t just available, they’re aligned. When AI stacks are built thoughtfully around actual business needs, they deliver more than efficiency – they bring clarity, confidence, and control.
And again, this is exactly what we focus on at SQREEM. Our ONE platform isn’t designed to replace your stack – it’s built to expand its capabilities, delivering the intelligence layer that boosts performance, cuts waste, and turns behavioural signals into strategic advantage.
Because the best stacks don’t just work harder. They help your people think better and move faster.
The Southeast Asia Factor
If you’re building a business in Southeast Asia, the game is a little different.
Your AI stack needs to handle the region’s complexity – language diversity, mobile-first users, and regulatory differences. That means choosing tools that are multilingual, work well on phones, and respect local privacy laws like PDPA. There’s no point automating customer outreach if it gets flagged in Vietnam or launching a chatbot that can’t understand Bahasa Indonesia.
The smartest stacks I’ve seen in SEA are light, fast, and culturally aware. They don’t try to do everything. They focus on what matters locally – and they deliver results.
Why This Matters Right Now
If AI is the new electricity, then stacks are the wiring. They determine what gets powered, what stays dark, and what actually transforms your business.
Too many teams are stuck in the “tool hoarding” phase – downloading, demoing, trying things out. But that’s not transformation. That’s just tinkering.
The real shift happens when teams design their workflows with AI at the centre. When they align their stack with their business strategy – and build in engines like SQREEM that drive real-world precision from day one.
That’s when AI stops being a novelty and starts being your competitive edge.
It’s the same shift we see in startups that go from idea to execution in a weekend. It’s the same shift large companies make when they finally move from small pilots to company-wide impact.
And it’s available to any team willing to think system-first.
A Simple Test
Here’s a quick way to check where you stand: If every AI tool you use disappeared overnight… what part of your workflow would actually break?
If the answer is “nothing much,” you don’t have a stack. You have some clever toys.
But if the answer is “everything would grind to a halt” – good. That means you’re not just playing with AI. You’ve made it essential to how you operate.
And here’s the harder question: Is your AI stack simply helping you move faster – or is it actually helping you compete smarter?
If you’re serious about building the kind of AI stack that drives real outcomes – not just activity – I’d love to hear how you’re approaching it. What’s in your stack today? Where are you seeing gaps? Drop a comment below and let’s swap ideas.
Thanks for reading!
Adrian 🙂
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Apple’s China AI pivot puts Washington on edge
Apple’s partnership with Alibaba to deliver AI services in China has sparked concern among U.S. lawmakers and security experts, highlighting growing tensions in global technology markets.
As Apple courts Alibaba for its iPhone AI partnership in China, U.S. lawmakers see more than just a tech deal taking shape.
TL;DR — What You Need To Know
- Apple has reportedly selected Alibaba’s Qwen AI model to power its iPhone features in China
- U.S. lawmakers and security officials are alarmed over data access and strategic implications
- The deal has not been officially confirmed by Apple, but Alibaba’s chairman has acknowledged it
- China remains a critical market for Apple amid declining iPhone sales
- The partnership highlights the growing difficulty of operating across rival tech spheres
Apple Intelligence meets the Great Firewall
Apple’s strategic pivot to partner with Chinese tech giant Alibaba for delivering AI services in China has triggered intense scrutiny in Washington. The collaboration, necessitated by China’s blocking of OpenAI services, raises profound questions about data security, technological sovereignty, and the intensifying tech rivalry between the United States and China. As Apple navigates declining iPhone sales in the crucial Chinese market, this partnership underscores the increasing difficulty for multinational tech companies to operate seamlessly across divergent technological and regulatory environments.
Apple Intelligence Meets Chinese Regulations
When Apple unveiled its ambitious “Apple Intelligence” system in June, it marked the company’s most significant push into AI-enhanced services. For Western markets, Apple seamlessly integrated OpenAI’s ChatGPT as a cornerstone partner for English-language capabilities. However, this implementation strategy hit an immediate roadblock in China, where OpenAI’s services remain effectively banned under the country’s stringent digital regulations.
Faced with this market-specific challenge, Apple initiated discussions with several Chinese AI leaders to identify a compliant local partner capable of delivering comparable functionality to Chinese consumers. The shortlist reportedly included major players in China’s burgeoning AI sector:
- Baidu, known for its Ernie Bot AI system
- DeepSeek, an emerging player in foundation models
- Tencent, the social media and gaming powerhouse
- Alibaba, whose open-source Qwen model has gained significant attention
While Apple has maintained its characteristic silence regarding partnership details, recent developments strongly suggest that Alibaba’s Qwen model has emerged as the chosen solution. The arrangement was seemingly confirmed when Alibaba’s chairman made an unplanned reference to the collaboration during a public appearance.
Washington’s Mounting Concerns
The revelation of Apple’s China-specific AI strategy has elicited swift and pronounced reactions from U.S. policymakers. Members of the House Select Committee on China have raised alarms about the potential implications, with some reports indicating that White House officials have directly engaged with Apple executives on the matter.
Representative Raja Krishnamoorthi of the House Intelligence Committee didn’t mince words, describing the development as “extremely disturbing.” His reaction encapsulates broader concerns about American technological advantages potentially benefiting Chinese competitors through such partnerships.
Greg Allen, Director of the Wadhwani A.I. Centre at CSIS, framed the situation in competitive terms:
“The United States is in an AI race with China, and we just don’t want American companies helping Chinese companies run faster.”
The concerns expressed by Washington officials and security experts include:
- Data Sovereignty Issues: Questions about where and how user data from AI interactions would be stored, processed, and potentially accessed
- Model Training Advantages: Concerns that the vast user interactions from Apple devices could help improve Alibaba’s foundational AI models
- National Security Implications: Worries about whether sensitive information could inadvertently flow through Chinese servers
- Regulatory Compliance: Questions about how Apple will navigate China’s content restrictions and censorship requirements
In response to these growing concerns, U.S. agencies are reportedly discussing whether to place Alibaba and other Chinese AI companies on a restricted entity list. Such a designation would formally limit collaboration between American and Chinese AI firms, potentially derailing arrangements like Apple’s reported partnership.
Commercial Necessities vs. Strategic Considerations
Apple’s motivation for pursuing a China-specific AI solution is straightforward from a business perspective. China remains one of the company’s largest and most important markets, despite recent challenges. Earlier this spring, iPhone sales in China declined by 24% year over year, highlighting the company’s vulnerability in this critical market.
Without a viable AI strategy for Chinese users, Apple risks further erosion of its market position at precisely the moment when AI features are becoming central to consumer technology choices. Chinese competitors like Huawei have already launched their own AI-enhanced smartphones, increasing pressure on Apple to respond.
The situation is further complicated by China’s own regulatory environment, which requires foreign technology companies to comply with data localisation rules and content restrictions. These requirements effectively necessitate some form of local partnership for AI services.
A Blueprint for the Decoupled Future?
Whether Apple’s partnership with Alibaba proceeds as reported or undergoes modifications in response to political pressure, the episode provides a revealing glimpse into the fragmenting global technology landscape.
As digital ecosystems increasingly align with geopolitical boundaries, multinational technology firms face increasingly complex strategic decisions:
- Regionalised Technology Stacks: Companies may need to develop and maintain separate technological implementations for different markets
- Partnership Dilemmas: Collaborations beneficial in one market may create political liabilities in others
- Regulatory Navigation: Operating across divergent regulatory environments requires sophisticated compliance strategies
- Resource Allocation: Developing market-specific solutions increases costs and complexity
Looking Forward
For now, Apple Intelligence has no confirmed launch date for the Chinese market. However, with new iPhone models traditionally released in autumn, Apple faces mounting time pressure to finalise its AI strategy.
The company’s eventual approach could signal broader trends in how global technology firms navigate an increasingly bifurcated digital landscape. Will companies maintain unified global platforms with minimal adaptations, or will we see the emergence of fundamentally different technological experiences across major markets?
As this situation evolves, it highlights a critical reality for the technology sector: in an era of intensifying great power competition, even seemingly routine business decisions can quickly acquire strategic significance.
You May Also Like:
- Alibaba’s AI Ambitions: Fueling Cloud Growth and Expanding in Asia
- Apple Unleashes AI Revolution with Apple Intelligence: A Game Changer in Asia’s Tech Landscape
- Apple and Meta Explore AI Partnership
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AI Just Killed 8 Jobs… But Created 15 New Ones Paying £100k+
AI is eliminating roles — but creating new ones that pay £100k+. Here are 15 fast-growing jobs in AI and how to prepare for them in Asia.
TL;DR — What You Need to Know:
- AI is replacing roles in moderation, customer service, writing, and warehousing—but it’s not all doom.
- In its place, AI created jobs paying £100k: prompt engineers, AI ethicists, machine learning leads, and more.
- The winners? Those who pivot now and get skilled, while others wait it out.
Let’s not sugar-coat it: AI has already taken your job.
Or if it hasn’t yet, it’s circling. Patiently. Quietly.
But here’s the twist: AI isn’t just wiping out roles — it’s creating some of the most lucrative career paths we’ve ever seen. The catch? You’ll need to move faster than the machines do.
The headlines love a doomsday spin — robots stealing jobs, mass layoffs, the end of work. But if you read past the fear, you’ll spot a very different story: one where new six-figure jobs are exploding in demand.
And they’re not just for coders or people with PhDs in quantum linguistics. Many of these jobs value soft skills, writing, ethics, even common sense — just with a new AI twist.
So here’s your clear-eyed guide:
- 8 jobs that AI is quietly (or not-so-quietly) killing
- 15 roles growing faster than a ChatGPT thread on Reddit — and paying very, very well.
8 Jobs AI Is Already Eliminating (or Shrinking Fast)
1. Social Media Content Moderators
Remember the armies of humans reviewing TikTok, Instagram, and Facebook posts for nudity or hate speech? Well, they’re disappearing. TikTok now uses AI to catch 80% of violations before humans ever see them. It’s faster, tireless, and cheaper.
Most social platforms are following suit. The remaining humans deal with edge cases or trauma-heavy content no one wants to automate… but the bulk of the work is now machine-led.
2. Customer Service Representatives
You’ve chatted with a bot recently. So has everyone.
Klarna’s AI assistant replaced 700 human agents in one swoop. IKEA has quietly shifted call centre support to fully automated systems. These AI tools handle everything from order tracking to password resets.
The result? Companies save money. Customers get 24/7 responses. And entry-level service jobs vanish.
3. Telemarketers and Call Centre Agents
Outbound sales? It’s been digitised. AI voice systems now make thousands of simultaneous calls, shift tone mid-sentence, and even spot emotional cues. They never need a lunch break — and they’re hard to distinguish from a real person.
Companies now use humans to plan campaigns, but the actual calls? Fully automated. If your job was cold-calling, it’s time to reskill — fast.
4. Data Entry Clerks
Manual input is gone. OCR + AI means documents are scanned, sorted, and uploaded instantly. IBM has paused hiring for 7,800 back-office jobs as automation takes over.
Across insurance, banking, healthcare — companies that once hired data entry clerks by the dozen now need just a few to manage exceptions.
5. Retail Cashiers
Self-checkout kiosks were just the start. Amazon Go stores use computer vision to eliminate the checkout experience altogether — just grab and go.
Walmart and Tesco are rolling out similar models. Even mid-sized retailers are using AI to reduce cashier shifts by 10–25%. Humans now restock and assist — not scan.
6. Warehouse & Fulfilment Staff
Amazon’s warehouses are a case study in automation. Autonomous robots pick, pack, and ship faster than any human.
The result? Fewer injuries, more efficiency… and fewer humans.
Even smaller logistics firms are adopting warehouse AI, as costs drop and robots become “as-a-service”.
7. Translators & Content Writers (Basic-Level)
Generative AI is fast, multilingual, and on-brand. Duolingo replaced much of its content writing team with GPT-driven systems.
Marketing teams now use AI for product descriptions, blogs, and ads. Humans still do strategy — but the daily word count? AI’s job now.
8. Entry-Level Graphic Designers
AI tools like Midjourney, Ideogram, and Adobe Firefly generate visuals from a sentence. Logos, pitch decks, ad banners — all created in seconds. The entry-level designer who used to churn out social graphics? No longer essential.
Top-tier creatives still thrive. But production design? That’s already AI’s turf.
Are you futureproofed—or just hoping you’re not next?
15 AI-Driven Jobs Now Paying £100k+
Now for the exciting bit. While AI clears out repetitive roles, it also opens new high-paying jobs that didn’t exist 3 years ago.
These aren’t sci-fi ideas. These are real jobs being filled today — many in Singapore, Australia, India, and Korea — with salaries to match.
1. Machine Learning Engineer
The architects of AI itself. They build the algorithms powering everything from fraud detection to self-driving cars.
Salary: £85k–£210k
Needed: Python, TensorFlow/PyTorch, strong maths. Highly sought after across finance, healthcare, and Big Tech.
2. Data Scientist
Translates oceans of data into actual insights. Think Netflix recommendations, pricing strategies, or disease forecasting.
Salary: £70k–£160k
Key skills: Python, SQL, R, storytelling. A killer combo of tech + communication.
3. Prompt Engineer
No code needed — just words.
They craft the perfect prompts to steer AI models like ChatGPT toward accurate, helpful results.
Salary: £110k–£200k+
Writers, marketers, and linguists are all pivoting into this role. It’s exploding.
4. AI Product Manager
You don’t build the AI — you make it useful.
This role bridges business needs and tech teams to launch products that solve real problems.
Salary: £120k–£170k
Ideal for ex-consultants, startup leads, or technical PMs with an eye for product-market fit.
5. AI Ethics / Governance Specialist
Someone has to keep the machines honest. These specialists ensure AI is fair, safe, and compliant.
Salary: £100k–£170k
Perfect for lawyers, philosophers, or policy pros who understand AI’s social impact.
6. AI Compliance / Audit Specialist
GDPR. HIPAA. The EU AI Act.
These specialists check that AI systems follow legal rules and ethical standards.
Salary: £90k–£150k
Especially hot in finance, healthcare, and enterprise tech.
7. Data Engineer / MLOps Engineer
Behind every smart model is a ton of infrastructure.
Data Engineers build it. MLOps Engineers keep it running.
Salary: £90k–£140k
You’ll need DevOps, cloud computing, and Python chops.
8. AI Solutions Architect
The big-picture thinker. Designs AI systems that actually work at scale.
Salary: £110k–£160k
In demand in cloud, consulting, and enterprise IT.
9. Computer Vision Engineer
They teach machines to see.
From autonomous cars to medical scans to supermarket cameras — it’s all vision.
Salary: £120k+
Strong Python + OpenCV/TensorFlow is a must.
10. Robotics Engineer (AI + Machines)
Think factory bots, surgical arms, or drone fleets.
You’ll need both hardware knowledge and machine learning skills.
Salary: £100k–£150k+
A rare mix = big pay.
11. Autonomous Vehicle Engineer
Still one of AI’s toughest challenges — and best-paid verticals.
Salary: £120k+
Roles in perception, planning, and safety. Tesla, Waymo, and China’s Didi all hiring like mad.
12. AI Cybersecurity Specialist
Protect AI… with AI.
This job prevents attacks on models and builds AI-powered threat detection.
Salary: £120k+
Perfect for seasoned security pros looking to specialise.
13. Human–AI Interaction Designer (UX for AI)
Humans don’t trust what they don’t understand.
These designers make AI usable, friendly, and ethical.
Salary: £100k–£135k
Great path for UXers who want to go deep into AI systems.
14. LLM Trainer / Model Fine-tuner
You teach ChatGPT how to behave. Literally.
Using reinforcement learning, you align models with human values.
Salary: £100k–£180k
Ideal for teachers, researchers, or anyone great at structured thinking.
15. AI Consultant / Solutions Specialist
Advises companies on where and how to use AI.
Part analyst, part strategist, part translator.
Salary: £120k+
Management consultants and ex-founders thrive here.
The Bottom Line: You Don’t Need to Fear AI. You Need to Work With It.
If AI is your competition, you’re already behind. But if it’s your co-pilot, you’re ahead of 90% of the workforce.
This isn’t just about learning to code. It’s about learning to think differently.
To communicate with machines.
To spot where humans still matter — and amplify that with tech.
Because while AI might be killing off 8 jobs…
It’s creating 15 new ones that pay double — and need smart, curious, adaptable people.
So—
Will you let AI automate you… or will you get paid to run it?
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