Technology
AI vs Humanity: A Battle of Identity
Artificial intelligence (AI) is changing the world. The last two decades have laid the infrastructure to give over 5 billion people access to digital services through smartphones and the Internet. This has primed the world for an AI revolution, the exponential growth of which we’re beginning to see through services like ChatGPT that fundamentally change how we interact with technology.
Like every technological paradigm shift, from fire to flying or the industrial revolution to the Internet, the benefits of AI will also be challenged by its threat. While my pronoid nature is certain that the net impact will be positive (because there are many more good people in this world than bad), one growing area of AI concern is how we distinguish ourselves as human beings versus AI.
Trust is a base requirement for our lives to operate effectively, from our relationships with those around us to our interactions with business and government services. We have built systems to facilitate trust; our ID cards prove who we are, and our physical address ensures that we can be found. But in Africa and other emerging markets, poor identity and physical addressing infrastructure limits trust increase fraud and hold back the economy. MIT estimated that India’s lack of a physical addressing system costs its economy 0.5% of its GDP. Visa’s latest fraud report shows that attempted fraud in Africa is 5x more than in the US.
Over the past two decades, we’ve seen technology try to help us prove we are who we say we are. Before, every transaction at a bank had to be done in person, but over time, these physical verifications have been replaced by digital ones; we’ve all solved annoying online captcha puzzles, fumbled for another one-time pin (OTP), and maybe more recently awkwardly recorded a selfie video of yourself.
However, as more and more services become digital, the fraudsters keep out-innovating these measures. AI can now impersonate a customer service agent or make a video of you speaking from just a photo. This undermines the ability of businesses across various industries to identify and verify their customers. In January 2023, Visa saw a 60x increase in fraud rate for Financial Services compared to just a year earlier.
Proof of address is stuck in the analogue era
Smart operators worldwide understand the threat posed to customer verification by AI and are already investing in mitigations. Meta has begun using paid-for verification for Instagram and Facebook. PayPal uses a detailed process that relies on multiple layers of compliance, verification, and monitoring to verify and onboard customers.
However, proof of identity using an ID card is no longer enough, so startups are innovating to help businesses truly know their customer. Worldcoin launched earlier this year to use a person’s iris as a form of identity; others like Bright ID schedule group video calls where you need to hold a conversation to prove you’re a real human being.
One area that is being overlooked is knowing where the customer lives. In developed markets like the US and UK, your proof of address is the ultimate form of accountability because whether it’s your bank or the police, they can physically find you if you commit fraud.
Yet, proof of address has become harder to validate in our modern world. People don’t live in the same house for most of their lives like before; in fact, digital nomads don’t even have a fixed abode at all. Bank statements or utility bills are no longer posted through the letterbox, enforcing a point of verification because they’re now digital PDFs delivered to your phone. It used to be relatively easy to update your few services when you moved, but now you have an overwhelming number of accounts to update.
And this is the best-case scenario. It’s estimated that 4 billion people – half the globe – do not have a formal physical address because their building or road has no identifier. And what about those who do not have a fixed home because they are homeless or have had to flee their country as refugees?
When global banking regulation forces financial services to only offer their services if the customer can prove their address, this creates a massive problem for the world’s economy. On paper, the regulators are doing the right thing to ensure financial services correctly implement effective Know Your Customer (KYC) measures. However, it creates a catch-22 for financial service providers; to open accounts, they need to have verified customer addresses, but there are no practical ways to do this beyond sending a human agent to the customer’s address, which costs too much, especially for accounts for lower-income customers.
AI to the rescue
My Kenyan co-founder once said, “We’re blessed in Africa to have so many problems because it creates so much opportunity”, and it’s this problem and opportunity that my co-founders and I have spent the last 10 years trying to solve. We believe that it’s a human right for every person to have a verified address so that they can access the services they deserve, from opening up a bank account to having an ambulance arrive at their door.
We see a world where anyone with a smartphone has a digital address that uses the location data in their phone, behavioural science and AI to verify they live where they say they do. As a centralised addressing system, when the person moves, they just have to notify us for all other services to be updated. It puts control of the address into the hands of the user, who can manage their data privacy and only give access to their address to the businesses and people they trust.
The behavioural science in our solution grounds a user’s digital account to the real world through where they live, enabling both proof of address and proof of humanity. While AI can impersonate your voice and create a video of you, it cannot impersonate where you live.
In our new world, where it’s becoming increasingly difficult to build trust and distinguish the difference between AI and a human, perhaps the solution is closer to home than we think.
Timbo Drayson is the CEO & Co-Founder of OkHi
By Aduragbemi Omiyale
WhatsApp has been updated with a web calling feature, allowing users to now make and receive audio and video calls, both one-on-one and group, directly from WhatsApp Web.
A notice from the Meta-owned messaging platform disclosed that this feature works without the need to download any app.
It was disclosed that this update was made to WhatsApp to make it easier for users to make calls wherever they are and on whatever device, phone or laptop.
Users will have access to features available on their other devices, including screen sharing, reactions, and a dedicated Calls tab with full call history and favourites, all from their browsers.
WhatsApp noted that calls from this feature are end-to-end encrypted, with no time limits and at no cost, as it is the same private experience they expect from the platform.
WhatsApp has also been embedded with easy call transfer, enabling the movement of an active group call from one device to another without hanging up. Users can seamlessly transfer calls to WhatsApp Web or Desktop when they arrive home to collaborate on a larger screen – or vice versa.
The platform further said it now has QuickHD, which allows for an improved video experience at the beginning of a call. With QuickHD, users can now enjoy high-definition video immediately in the very first few seconds of the call.
In addition, it has launched noise suppression to remove the background noise around callers so their voices come through clearly to the person on the other end, even in loud or busy environments. This can be managed at any time in in-call settings.
WhatsApp said these features are rolling out gradually and will be available to everyone soon, expressing its desire to make WhatsApp calling simpler to use.
Modded Minecraft worlds take weeks to build. Custom biomes, terraformed landscapes, established bases — losing any of it to a corrupted chunk or a failed update is devastating. Backups aren’t optional for modded servers. They’re essential.
Modded hosting with reliable backups protects everything players have built across every minecraft biome types the pack introduces — without requiring manual backup routines that nobody remembers to run.
Why Modded Worlds Are More Vulnerable to Data Loss
Vanilla Minecraft worlds are relatively stable. Modded worlds have more ways to break. Mod updates can corrupt chunk data if block IDs change. Crashes during chunk generation leave partial files. Automated farms that run while no players are online can cause chunk-level issues over time.
Biomes added by mods require extra care. Changing biome mods later can cause unexpected problems. If the old biome data no longer exists, Minecraft may not know how to read parts of the world anymore. The Minecraft Wiki explains that every generated chunk keeps its own biome information in the save. Modded biomes require their mod to be present to load correctly — removing the mod without migrating chunks first causes permanent data loss in those areas.
Minecraft Biome Types: What Mods Add
People often focus on giant mountains or rare structures, but smaller biome changes matter too. A carefully designed minecraft grass biome can completely change the appearance of an entire region. Once that combines with improved forests, rivers, and mountain generation, exploration naturally takes longer because there’s almost always another interesting place nearby. There’s a massive difference once biome mods are installed. Biomes O’ Plenty alone adds over 80 biomes. Terralith adds another 95 plus, although it still builds worlds using Minecraft’s regular generation system rather than a custom one. Oh The Biomes You’ll Go adds tropical and fantasy biomes not found in either.
Each of these creates a richer world to explore — and more unique terrain to protect. A player who spends two sessions exploring and settling in a modded cherry blossom grove loses something irreplaceable if that chunk gets corrupted without a backup.
Content creator and modpack player Noxite has described modded biome exploration as one of the most memorable experiences in the game — “every new biome feels like discovering a location that exists only in your world.” Backups protect that discovery.
Many players only start thinking about backups after something goes wrong. It’s surprisingly easy for a modded world to develop problems. Maybe the server restarts halfway through chunk generation, or a mod update changes world data in a way the old save no longer understands. Even experienced server owners don’t get through every update without issues. Sometimes everything looks fine until someone explores a new biome and discovers broken chunks. At that point, having a recent backup is usually the simplest solution instead of trying to repair damaged terrain piece by piece.
What a Reliable Backup System Looks Like for Modded Servers
For modded worlds, backup requirements go beyond vanilla minimums:
- Daily automated backups — at minimum; hourly for active community servers
- Full world backups — not just player data; entire world folder including all dimensions
- Pre-update snapshots — automatic backup triggered before any mod or server update
- Offsite storage — backups stored separately from the server to survive hardware failure
- Easy restoration — one-click restore to a specific backup without technical support
Protecting Every Biome You’ve Explored
Minecraft biomes wiki entries for modded biomes often include notes about save compatibility across versions. The community’s experience is consistent: mod updates break worlds more often than player actions do. Regular backups are the only reliable protection.
Choose modded hosting with reliable backups that runs automatically, stores multiple restore points, and makes restoration simple. The time investment in setting up proper backups is always less than the time lost rebuilding after a preventable data loss event.
The rules have changed. How companies actually know their customers — really know them — looks almost nothing like it did ten years ago. Old-school research methods are drowning. Too slow, too narrow, too dependent on humans manually stitching together datasets that have already gone cold. Markets shift in days now, not quarters. And the cost of a slow read on consumer behavior keeps climbing. This isn’t just a tooling upgrade. The underlying logic of how businesses decide what to build, what to charge, and who to reach has been gutted and rebuilt from scratch. Staying reactive isn’t a strategy anymore. It’s a liability.
1. Real-Time Data Processing and Pattern Recognition
Consumer intelligence used to run on stale numbers. Analysts dug into data weeks — sometimes months — after whatever actually happened. Modern AI kills that lag. Entirely. These systems chew through enormous volumes of behavioral data on the fly, surfacing patterns that human teams couldn’t find in the same timeframe with ten times the headcount. Machine learning algorithms can process millions of customer interactions, transactions, and behavioral signals simultaneously — pulling clean signal out of what would otherwise be undifferentiated noise. A retailer can track sentiment across social media, reviews, and support tickets right now, catching a brewing problem or an emerging trend in hours rather than weeks. Inventory shifts, pricing moves, message pivots — all of it happens before a trend fully crystallizes. That’s a different game entirely.
2. Predictive Analytics and Consumer Behavior Forecasting
Here’s what actually changed: AI stops consumer intelligence from being a backward-looking exercise. Instead of cataloguing what customers already did, companies can now forecast what they’re likely to do next — and with striking accuracy. Advanced ML models thread together historical patterns and live behavioral signals to predict churn, flag high-value prospects, and project demand across entire product lines. A telecom company can spot which customers are quietly drifting toward a competitor before they ever make the switch — and intervene first. That’s not a marginal improvement. It’s a fundamentally different posture. Resources flow toward the segments that actually matter, rather than spreading thin across the whole base and hoping something sticks.
3. Personalization at Scale
Consumers expect personalized experiences. Full stop. Meeting that expectation at scale — for millions of people at once — is simply beyond what human analysts and traditional segmentation can deliver. Machine learning models read individual purchase histories, browsing patterns, preferences, and demographic signals to build dynamic profiles that drive product recommendations, custom messaging, and tailored interfaces. When building and refining these individualized profiles, marketers who need to enrich their first-party data with verified behavioral signals rely on audience data providers to ensure their models are trained on accurate, high-quality consumer information. An e-commerce platform can serve each visitor a genuinely different experience — different layouts, different offers, different content — all built around that visitor’s unique fingerprint. Conversion lifts. Lifetime value climbs. People respond when recommendations actually fit their lives, not just the average of everyone else’s.
4. Sentiment Analysis and Brand Perception Monitoring
Knowing how consumers feel about a brand means wading through unstructured mess. Reviews, comment threads, support tickets, social posts, video captions — none of it parses cleanly by hand at any useful speed. Natural language processing handles it. NLP systems automatically scan text-based content across digital channels, classifying sentiment as positive, negative, or neutral while bucketing feedback by topic, product feature, or customer segment. An automaker can track online conversations about a specific reliability concern and catch it before it snowballs into a full-blown reputation crisis. No waiting for quarterly surveys. No lag. Brand perception monitoring becomes continuous — and decisions about product fixes, messaging shifts, or service interventions get grounded in real signal rather than gut instinct.
5. Competitive Intelligence and Market Positioning Analysis
Competitive intelligence used to mean manual tracking, sprawling spreadsheets, and perpetually incomplete pictures. AI automates the entire collection-and-analysis loop. ML models watch competitor pricing moves, product launches, promotions, and messaging shifts across digital channels — then stack that data against a company’s own position. Gaps surface. Threats register earlier. A financial services firm can monitor exactly which themes competitors are pushing on social media and which ones are actually generating engagement — then sharpen their own positioning accordingly. Real-time visibility into competitive dynamics means strategic calls about where to invest, which markets to enter, and how to stand apart in crowded categories aren’t made blind anymore.
Conclusion
What AI has done to consumer intelligence isn’t incremental. It’s structural. Real-time processing of massive datasets. Forecasting future behavior instead of autopsying the past. Personalization that reaches millions, not hundreds. Continuous sentiment monitoring. Automated competitive tracking. None of these were realistic options a decade ago. They are now. Companies that wire these capabilities into their core operations make faster, sharper decisions — ones that show up directly in revenue, satisfaction scores, and market share. Those that don’t will keep falling further behind. And the gap between organizations that wield these tools well and those still grinding through traditional approaches? It’s not closing. It’s widening every quarter.
