Fatal Frame 2 Remake: The Camera Obscura's Unrivaled Reign of Terror in Modern Horror

February 13, 2026

REVIEW · FEBRUARY 13, 2026 · 8 MIN READ

Fatal Frame 2: Crimson Butterfly Remake – The Camera Obscura Still Haunts

After roughly four hours with the first four chapters of the highly anticipated Fatal Frame 2: Crimson Butterfly Remake on PC, it's unequivocally clear: the game has its hooks in us, and it's not just the haunting narrative or the meticulously recreated environments. It's the ingenious, terrifying central mechanic – the Camera Obscura – elevated to new, excruciating heights by modern technical prowess. In an industry saturated with firearms and blunt instruments, Fatal Frame 2 masterfully weaponizes observation itself, transforming the act of seeing into the most perilous and potent form of defense.

“The Camera Obscura isn't just a weapon; it's an interface for dread. It forces you to look at the things that want to hurt you, and that changes everything about how horror works.”

The Camera Obscura: A Lens into Terror

At its core, the Camera Obscura isn't merely a point‑and‑shoot weapon; it's an intricate interface for confrontation, forcing players to actively engage with the spectral antagonists. Unlike a shotgun that allows for quick, reactive bursts, wielding the Camera Obscura demands a deliberate, almost ritualistic engagement. Players must raise the camera, hold their ground, and frame the fleeting, often aggressive apparitions within the viewfinder. This process inherently slows down player movement, narrows their field of vision, and amplifies the psychological pressure, making every encounter a harrowing test of nerve and precision.

The genius lies in its dual function: it's both our only means of defense and our primary tool for unveiling the game's grim lore. Every successful shot, every spirit captured, reveals fragments of the past, linking the act of survival directly to the unraveling of the Crimson Butterfly ritual. This narrative integration deepens the player's investment, turning combat encounters into essential investigative steps rather than mere obstacles.

Technical Foundations of Fear Amplified

The remake leverages contemporary graphical and audio engines to breathtaking effect. The original game's atmosphere, already potent, is now rendered with an oppressive fidelity. Dynamic lighting and shadow play brilliantly obscure and reveal, making the dilapidated Minakami Village feel more suffocatingly real. The subtle shimmer of a distant spirit, the sudden manifestation of a vengeful ghost through a wall, or the spectral outlines appearing only when viewed through the camera's lens, are all enhanced by advanced particle effects and high‑resolution textures. This visual upgrade ensures that the terror is not just psychological but viscerally present.

Audio design, often the unsung hero of horror, is particularly impactful in the remake. Spatial audio cues are critical; the faint whispers of unseen entities, the distant wails, or the sudden, sharp shrieks as a ghost lunges are meticulously placed. When aiming the Camera Obscura, the soundscape shifts, often amplifying the spectral sounds, creating an auditory tunnel vision that complements the visual one. The distinct 'click' of the shutter, followed by the satisfying but brief burst of light that repels a ghost, provides crucial auditory feedback, signaling both danger and momentary relief.

Psychological Warfare: When Sight Becomes a Trap

The Camera Obscura's design is a masterclass in psychological warfare. It compels the player to fixate on the very things that cause dread. Instead of averting gaze or fleeing, the game demands direct eye contact, forcing players to confront the grotesque and the tragic head‑on. The mechanics of 'Fatal Frames' – landing a shot just as a ghost attacks – push players to delay their defense, inviting maximum risk for maximum reward. This constant high‑stakes decision‑making, coupled with the camera's often slow charge time, creates an agonizing tension that few other games can replicate.

Furthermore, the limitation of film types and their varying effectiveness against different spectral manifestations introduces a strategic layer. Deciding when to use precious, high‑damage film or conserve it for a more formidable encounter adds another dimension of resource management and psychological strain. The game doesn't just scare you with jump scares; it grinds you down with persistent anxiety and the terrifying implications of every choice.

Evolution of the Crimson Butterfly's Grip

The remake doesn't just polish; it refines. Controls feel more fluid, making the act of maneuvering and aiming less cumbersome, allowing players to focus more intently on the horror. Ghost AI appears more sophisticated, with spirits exhibiting more unpredictable movement patterns and attack timings, demanding even greater observational skill and quicker reflexes. The visual feedback for ghost weak points and charging attacks is clearer, making the 'Fatal Frame' mechanic more satisfying to master without diminishing its challenge. These enhancements ensure that the core gameplay loop, while faithful, feels fresh and modern, amplifying the original's strengths rather than simply reproducing them.

In conclusion, the Fatal Frame 2: Crimson Butterfly Remake is more than just a graphical overhaul; it's a profound re‑analysis of what makes its central mechanic so terrifyingly effective. By enhancing the visual and auditory fidelity, refining gameplay nuances, and maintaining its core psychological design, the Camera Obscura solidifies its legacy as perhaps the most unique and terrifying 'weapon' in gaming history. It doesn't just fight ghosts; it forces you to truly see them, and in doing so, reveals the deepest fears within ourselves.

🎮 The Open Question

What are your thoughts on games that weaponize unconventional mechanics, forcing players to confront fear rather than merely avoid it? Do you think the Camera Obscura remains the pinnacle of such design? The remake proves that twenty years later, pointing a camera at a ghost is still more terrifying than any shotgun.

Filed under: Horror · Remake · Fatal Frame · Camera Obscura · PC Gaming · Psychological Horror

Firmus Secures $10 Billion: A Deep Dive into the AI Infrastructure Arms Race

February 13, 2026

AI INFRASTRUCTURE · FEBRUARY 13, 2026 · 8 MIN READ

Why a $10 Billion GPU Deal Is the Real Story Behind the AI Boom

Australian AI infrastructure firm Firmus just secured a staggering USD 10 billion GPU financing facility. That's not a typo. Ten. Billion. Dollars. And it's not just about the money – it's about what that money buys: tens of thousands of Nvidia H100s and next‑gen B200s, the networking fabric to connect them, the power to run them, and the facilities to house them. In the global race for AI supremacy, this deal is a declaration that the real winners won't be just the algorithm inventors – they'll be the ones who control the physical infrastructure that makes AI possible.

“This isn't a loan to buy a few servers. This is a strategic bet that owning the compute layer is as valuable as owning the models themselves.”

The Insatiable Hunger for AI Compute

The number – USD 10 billion – is almost abstract. To understand what it really means, you have to look at what it buys. A single Nvidia H100 GPU, the current workhorse of large language model training, can cost anywhere from $25,000 to $40,000 on the open market. The upcoming B200 "Blackwell" series will be even more expensive. Training a frontier‑level model like GPT‑4 requires tens of thousands of these chips running in parallel for months, connected by ultra‑fast networking like InfiniBand, consuming megawatts of power, and generating enough heat to require advanced liquid cooling.

The hyperscalers – AWS, Azure, Google Cloud – have been buying these chips in such quantities that they've essentially cornered the supply chain for years. Smaller players, startups, and even national research institutions have been left scrambling for scraps. Firmus's financing changes that calculus. With $10 billion in purchasing power, they can pre‑order future‑generation GPUs, secure supply agreements, and build out data centres specifically optimized for AI workloads – not generic cloud compute, but purpose‑built AI factories.

Firmus's Play: Specialized Infrastructure for Specialized Demand

Firmus isn't trying to be another AWS. They're positioning themselves as a pure‑play AI infrastructure provider. That means their data centres are designed from the ground up for the unique demands of machine learning: high‑density GPU racks, low‑latency fabric between nodes, software stacks tuned for distributed training frameworks like PyTorch and TensorFlow, and power systems that can handle the insane peak loads of model training.

For AI startups, this is a lifeline. Renting time on hyperscaler GPUs can be prohibitively expensive, and the terms often come with vendor lock‑in. A dedicated AI infrastructure provider can offer more flexible contracting, better performance per dollar (because everything is optimized for AI, not general compute), and potentially even geographic advantages – being in the APAC region with low latency to local markets.

The Geopolitics of GPU Supply

This deal also has a geopolitical dimension that's impossible to ignore. Australia, like many nations, has grown increasingly nervous about its dependence on foreign – particularly US‑ and China‑based – cloud infrastructure. Sovereign AI capability is becoming a national security talking point. If all of your AI models run on servers controlled by another country's companies, what happens when tensions rise?

By financing a domestic company with massive GPU capacity, Australia is effectively buying a piece of its own AI future. It ensures that Australian researchers, companies, and government agencies have access to cutting‑edge compute without necessarily routing everything through Silicon Valley. It's a strategic hedge, and one that other mid‑sized economies will likely emulate.

The Operational Nightmare Ahead

Of course, buying the GPUs is only the first step. Running them is a completely different challenge. A facility with tens of thousands of GPUs draws power like a small city. Heat management at that scale requires either geographic placement in cool climates or massive investment in cooling infrastructure – or both. The networking alone is a monumental engineering problem: moving terabytes of data between chips with microsecond latency demands physical proximity and fiber density that most data centres weren't designed for.

Then there's the talent. The people who can architect, deploy, and maintain these systems are among the most sought‑after in the tech industry. Firmus will need to build a world‑class engineering team, likely competing directly with the hyperscalers they're trying to differentiate from. It's a hiring challenge on top of an engineering challenge.

The Bigger Picture: AI's Physical Layer

The Firmus deal is a reminder that AI is not just software. It's not just clever algorithms and massive datasets. At its core, AI is a physical industry – it requires chips, power, cooling, buildings, and billions of dollars of capital equipment. The companies that control that physical layer will capture enormous value, regardless of which models end up winning the popularity contest.

In that sense, this $10 billion financing is a bet on the whole AI sector. It's saying that demand for compute will continue to explode, that GPUs will remain scarce, and that owning a dedicated AI infrastructure business is a winning long‑term strategy. It's a bold move, and it positions Firmus as a serious player in the global AI infrastructure race.

⚡ The Takeaway

The AI boom is often described in terms of models and benchmarks, but the real story is beneath the surface: the chips, the data centres, the financing deals that make it all possible. Firmus's $10 billion GPU facility is a reminder that the future of AI will be built on physical infrastructure as much as on code. Who controls the compute will shape who controls the technology.

Filed under: AI Infrastructure · GPUs · Nvidia · Sovereign AI · Data Centres · Investment

How I Use AI Image Generators for My Clothing Brand (And You Can Too)

February 12, 2026

How AI Image Generators Saved My Clothing Brand (And Then Tried to Ruin It)

Okay, so let me start with a confession. I was that person. You know the one – scrolling Instagram, seeing those perfect AI-generated model photos, and rolling my eyes so hard I nearly pulled a muscle. "Fake," I thought. "Cheating," I muttered. "I would NEVER use that for my brand."

Well, folks, meet me six months later: the queen of AI-generated product photos, with a closet full of regrets and a very awkward story about a model with three arms. Yeah. That happened.

Grab your coffee (or tea, I don't judge), get comfy, and let me tell you the whole messy story of how I went from "AI is the devil" to "AI is my BFF" – with all the fails, facepalms, and "wait, what the heck?" moments along the way.

Quick heads-up: I'm not a tech guru. I'm just a girl with a laptop, a clothing brand, and zero patience for expensive photoshoots. If I can figure this out, trust me, anyone can.

Why I Almost Threw My Laptop Out the Window

Let's rewind to last year. I had this cute little summer collection – think flowy dresses, pastel colors, the whole vibe. I saved up for months, booked a "professional" photographer (who turned out to be a guy with a decent camera and a very strong opinion about my lighting), rented a studio, and hired two models. Total cost? $1,200. For one day. And guess what? The photos were a hot mess.

The lighting made the dresses look washed out. One model kept posing like she was in a 90s catalog – you know, the overly dramatic hand-on-hip thing? And the other model? She had a last-minute family emergency, so I ended up using a friend who kept blinking in every single shot. Every. Single. One.

I got home, looked at the photos, and literally cried. Like, ugly cried. My boyfriend walked in, saw me surrounded by tissues, and slowly backed out of the room. Smart guy.

That's when I thought, "There has GOT to be a better way." And then I remembered those AI tools I'd been trash-talking for months. Oh, the irony.

My First Week with AI: A Complete Disaster

So I dove in headfirst. I signed up for every free trial I could find. Photoroom, Botika, VogueShot, WeShop, plus three others I can't even remember the names of. I was like a kid in a candy store, except the candy kept glitching and giving me nightmares.

Day 1: I tried to upload a photo of a simple white t-shirt. The AI turned it into... a toga? I don't even know. The model was wearing it like a Roman emperor. My brand is not Ancient Rome-inspired, last time I checked.

Day 3: This is where the three-armed model incident happened. I swear on my life, the AI generated a beautiful girl in my hoodie – except she had three arms. One was holding a coffee cup, one was in her pocket, and the third was just... hanging there. Waving at me. Mocking me. I showed it to my sister and we laughed for an hour. Then I panicked – what if a customer sees this? "Oh, our hoodies come with an extra limb, it's a limited edition."

Day 5: I tried to generate a model with a specific hairstyle. The AI gave me a model with half her head shaved and the other half neon green. Cute, but not exactly my target audience. My brand is boho-chic, not punk-rock-rebellion.

At this point, I was this close to giving up. Like, really close. I told myself, "Maybe AI just hates me personally." But then, on Day 7, something clicked.

The Day It Finally Worked (And I Screamed)

I decided to slow down. I watched some tutorials (yes, I'm one of those people who never reads instructions), and I realized my source photos were garbage. I was uploading dark, blurry pictures taken in my bedroom at 10 PM. No wonder the AI was confused.

So I set up a little "studio" in my living room – a white sheet taped to the wall, good natural light, and my phone propped up on a stack of books. I took clear, well-lit photos of my products: flat lays, hanging shots, the works.

Then I uploaded one to Photoroom. I picked a model (a cute girl with curly hair, because representation matters, people), chose a beach background (because summer vibes), and clicked generate. And then... I screamed. Like, actually screamed. My boyfriend ran in again, ready to call 911.

The image was PERFECT. The dress looked amazing, the lighting was on point, and the model had TWO arms. I was shook. I immediately generated five more, and they all looked legit. I might have done a happy dance in my living room. No regrets.

My Honest Take on the Tools I've Tried (With Zero Filter)

Alright, so here's my brutally honest opinion on the tools I tested. Keep in mind, I'm picky and easily annoyed, so take it with a grain of salt.

Photoroom Virtual Model

Verdict: My ride-or-die. The fabric accuracy is insane – like, scary good. It actually understands how a cotton t-shirt drapes vs. a silk blouse. The model diversity is decent, and the backgrounds don't look fake. BUT the free version slaps a watermark on everything, so you'll have to pay. It's like $10 a month, which is less than I spend on takeout coffee, so I'm not mad.

Botika

Verdict: Solid, but has a weird lighting obsession. Sometimes it makes my products look like they're glowing from within – which is cool if I'm selling alien apparel, but not so much for everyday wear. It integrates with Shopify, which is a lifesaver if you're lazy like me. Pricey though – $33/month. I use it occasionally, but we're not exclusive.

VogueShot

Verdict: This one's for when I want to feel fancy. The models are VERY diverse – like, all ages, body types, ethnicities. It's a mobile app, so I can create content while waiting in line at the grocery store. But sometimes the results look a little too "edited," if you know what I mean. Like, the models are so perfect they look like aliens. Creeps me out a bit.

WeShop AI

Verdict: My go-to for social media. The images have this trendy, editorial vibe that works great for Instagram. But accuracy? Meh. I once uploaded a red hoodie and it came out burgundy. If you're a perfectionist about colors, this might trigger you. Still, for creating "mood" content, it's fun.

Here's the tea: none of these tools are perfect. They all have quirks, glitches, and moments where you want to throw your phone. But compared to the stress and cost of real photoshoots? I'll take the occasional three-armed model any day.

The "Ethical" Debate That Everyone Loves to Argue About

Okay, let's address the elephant in the room. People LOVE to hate on AI models. I see the comments: "You're stealing jobs from real models!" "It's so fake!" "This is ruining fashion!"

And honestly? I get it. At first, I felt guilty. I kept thinking about the models I could have hired. But then I remembered: I couldn't afford to hire them anyway. With my budget, I'd be lucky to get one model for one day. With AI, I can feature dozens of diverse models for the same price. It's actually MORE inclusive, in a way.

Plus, let's be real – the fashion industry has been using unrealistic, photoshopped images for decades. Now suddenly everyone's concerned about "authenticity"? Please. Spare me.

My controversial opinion: AI models are just another tool. It's how you use them that matters. If you're transparent about it, and you still focus on making great products, who cares? My customers ask sometimes, and I tell them straight up: "Yep, it's AI. But the quality of the clothes is real." And you know what? No one has ever returned a hoodie because the photo was AI-generated.

The Fails That Taught Me Everything

I promised you stories about my epic fails, so here we go. These are the moments that made me want to delete all my accounts and move to a farm with no internet.

Fail #1: The Background That Almost Got Me Sued
I generated a model in front of a cute café, because coffee and clothes, right? Turns out the café had a giant logo on the window – a real brand. I almost used it on my website. My friend spotted it and said, "Uh, trademark infringement much?" I literally photoshopped it out pixel by pixel. Lesson learned: CHECK THE DETAILS.

Fail #2: The Model Who Didn't Exist But Had a Better Life Than Me
I got so attached to one AI-generated model that I named her "Sophia" and started dressing her in all my new designs. I'd even think, "Sophia would look great in this." Then I realized I was emotionally invested in a fictional person. My boyfriend said I needed therapy. He's not wrong.

Fail #3: The Time I Forgot to Remove the Watermark
I was so excited about a new image that I posted it on Instagram immediately. Someone commented, "Nice watermark, did you pay for this?" I wanted to disappear. I deleted the post, paid for the subscription that night, and never looked back. Embarrassing? Yes. Character-building? Also yes.

My Step-by-Step Routine (That Actually Works)

So after all that trial and error, here's what I do now. Maybe it'll help you skip the headaches.

Step 1: Take decent source photos. Natural light, plain background, in-focus. It doesn't have to be perfect, but don't make the AI guess what your product looks like.

Step 2: Choose your tool based on the vibe. Photoroom for e-commerce accuracy, WeShop for social media flair. I use different tools for different purposes – like having multiple friends for different occasions.

Step 3: Generate a bunch of options. I usually do 10-15 per product. Most will be meh, but 2-3 will be fire. Pick the best and delete the rest.

Step 4: Edit if needed. Sometimes I adjust brightness, crop, or remove weird artifacts. A little touch-up goes a long way.

Step 5: Post and be honest. If someone asks, I tell them it's AI. No shame. My products are awesome, and that's what matters.

Real Results: The Numbers Don't Lie

Okay, so after six months of this madness, here's what actually happened:

Time savings: From 2 weeks per photoshoot to 2 hours per collection.
Money saved: Thousands of dollars. I'm not kidding. I used that money to buy more inventory.
Engagement: My Instagram posts are doing better because the images are more varied and interesting. People actually stop scrolling now.
Sales: Up about 20% since I started showing clothes on diverse models. Seeing the product on someone who looks like you? Game-changer.

But here's the real talk: it's not a magic bullet. If your products are ugly, AI won't save you. You still need good design and quality. AI just helps you show it off better.

What I'd Tell My Past Self (And You, If You're Curious)

If I could go back to that day I was crying over bad photos, here's what I'd say:

"Girl, chill. You're gonna try AI, it's gonna be a disaster at first, and you're gonna wanna quit. But then you'll figure it out, and it'll change everything. Also, don't name the AI models. That's weird."

And to you, reading this right now? If you're on the fence, just try it. Pick one tool – doesn't matter which – and play around for a week. Use free trials. Make mistakes. Laugh at the three-armed models. Eventually, you'll find your groove.

And if anyone gives you crap about "authenticity" or "stealing jobs," just smile and say, "My clothes are real, my passion is real, and my sales are real. Everything else is just a tool." Then walk away dramatically. Works every time.

So yeah, that's my story. I went from AI hater to AI lover, with plenty of facepalm moments along the way. If you've got your own AI horror stories or wins, drop them in the comments – I'd love to hear them. And if you're still reading this, thanks for sticking with my rambling. You're a real one. 💕

P.S. If you want me to review a specific tool or share more tips, just ask. I have Opinions (with a capital O). Also, I promise no more three-armed models in future posts. Probably.

The $14.5 Billion Black Box: How AI is quietly taking over technical recruitment in America

February 12, 2026

The $14.5 Billion Black Box: How AI Quietly Took Over Technical Recruitment in America

Workday, HireVue, and LinkedIn process 50 million job searches daily. Their algorithms decide who gets interviewed—and who gets rejected at 2 a.m. without human review. The market is worth $15 billion. The liability is uncalculated. And nobody knows exactly how the black box works.

DETROIT, MI — Derek Mobley checked his email at 2:47 a.m. on a Tuesday. Another rejection. He had lost count after 100 ^[citation:7].

The timing wasn't random. Workday's AI-powered screening software, deployed by dozens of Fortune 500 companies, had evaluated his resume against thousands of others. The algorithm decided—without human intervention, without an interview, without ever hearing his voice—that he was not a fit. The rejection landed in the middle of the night because that's when automated systems fire ^[citation:7].

Mobley is Black, over 40, and disabled. In May 2025, a federal judge ruled that his lawsuit against Workday could proceed—opening the door to something unprecedented: holding a software vendor liable for discrimination under Title VII of the Civil Rights Act, a law written 61 years before the algorithm that rejected him was coded ^{[citation:2][citation:7]}.

This is not a story about one man's lawsuit. It is a story about how a $15.18 billion industry built on opaque algorithms became the gatekeeper to the American middle class—and why, in 2026, nobody can agree on who to sue when the black box gets it wrong ^[citation:4].

“We're determining how we're fitting these 30- to 60-year-old employment statutes into the modern workplace. This case could have a great impact on how employers conduct business generally.” — Brent D. Hockaday, partner, K&L Gates, on Mobley v. Workday ^[citation:7]

1. The $15 Billion Invisible Industry

In 2025, the global online recruitment technology market was valued at $15.18 billion. By 2026, it will reach $17.48 billion. By 2034, Fortune Business Insights projects it will hit $46.07 billion, growing at a compound annual rate of 12.9% ^[citation:4].

North America alone accounts for $6.05 billion of that market—40% of global spend. The United States is projected to reach $4.91 billion in 2026 ^{[citation:4][citation:9]}. American companies are not just adopting AI recruitment tools; they are building their entire talent acquisition strategies around them.

The adoption numbers are staggering:

70% of companies have shifted to AI-driven hiring tools ^[citation:9].
75% of IT job applications are now submitted through AI-powered platforms ^[citation:9].
45% of companies have adopted automated resume screening ^[citation:9].
40% of enterprises use AI-powered video interviewing ^[citation:9].
30% of firms are experimenting with blockchain-based credential verification ^[citation:9].

The pitch is seductive. AI promises to eliminate bias, surface hidden talent, and reduce time-to-hire by 50% or more ^[citation:6]. But beneath the marketing, a different story is emerging—one of automated discrimination, legal liability, and a fundamental mismatch between how these tools work and how they are sold.

2. Inside the Black Box: What the Algorithms Actually Do

To understand the crisis, you must first understand what these systems are doing in the milliseconds between "Submit Application" and "Thank you for your interest."

First-generation ATS: Simple keyword matching. If your resume contained "Python" and the job required "Python," you passed. Candidates stuffed white-text keywords into document margins. Recruiters complained. Vendors promised better technology.

Second-generation (2024–2026): Contextual AI. Today's systems don't scan for keywords—they understand. They analyze:

Skill depth: Not just "Python," but whether your GitHub history shows sustained contribution or a single forked repo .
Code patterns: Some platforms claim to evaluate the sophistication of your actual code ^[citation:1].
Problem-solving ability: HireVue's AI analyzes your word choice, tone, and facial expressions during recorded interviews
Cultural fit: Algorithms infer whether you'll thrive in a specific company's environment based on linguistic patterns in your resume and social media ^[citation:2].

The leading platforms in 2026 illustrate the spectrum:

HireVue: Dominates enterprise video interviewing. Its AI evaluates candidate responses against millions of prior interviews. Used by 40% of large enterprises ^[citation:6].
Greenhouse AI: Embedded directly into one of the most popular ATS platforms. Automates resume scoring and candidate matching ^[citation:6].
SmartRecruiters: Focuses on recruiter productivity—smart scheduling, multilingual parsing, talent rediscovery ^[citation:6].
TestGorilla: The skills-testing specialist. 1,200+ tests, anti-cheating features, and detailed analytics ^[citation:6].
Scaletwice: A newer entrant combining AI video analysis with a community of pre-interviewed candidates .

Each platform makes claims about accuracy and bias reduction. But independent validation is scarce. The algorithms are proprietary. The training data is secret. The vendors say transparency would expose trade secrets.

Which brings us back to Derek Mobley.

3. The Lawsuit That Could Upend the Industry

Workday operates a two-sided platform. Employers use it to collect and process applications. Candidates submit resumes through it. Workday's AI scores each applicant and recommends who advances .

Derek Mobley applied to more than 100 jobs through Workday-powered systems. He was rejected from all of them—often within hours, sometimes minutes. The speed, he alleged, proved that no human ever saw his application .

In May 2025, U.S. District Judge Rita Lin granted conditional certification to Mobley's class action. Her reasoning was methodical:

The plaintiff alleged that Workday's algorithms were trained only on incumbent employee data, creating a homogenous workforce profile that systematically excluded applicants over 40 ^[citation:2].
Because the same algorithmic tool was applied across multiple employers, applicants were subject to a "common policy"—a key requirement for class certification .
If an algorithm automatically rejects candidates above a certain age with no human review, those applicants may plausibly be subject to discrimination under federal law .

Judge Lin was careful. She noted that AI recommendations might still reflect individual employer preferences. But she opened the door—and 2026 will determine how wide .

“If an algorithm is trained on historical data showing that a company often hires candidates under 40, the algorithm may initially learn that bias and amplify it with successive self-reinforcing recommendations in favor of younger hirings.” — Dr. Stuart Gurrea & Dr. Nicolas Suarez, Secretariat, on algorithmic feedback loops

This is the crux of the problem. AI doesn't just inherit bias—it amplifies it. Feedback loops reinforce initial patterns. If a company has historically hired few Black engineers, the algorithm learns that as a feature, not a bug. Each recommendation further entrenches the homogeneity. The system believes it is optimizing for "success." In reality, it is optimizing for the past.

4. The $29.99 Illusion: LinkedIn Premium and the Attention Economy

This is where the story intersects with something 900 million professionals encounter: LinkedIn Premium.

LinkedIn Premium Career costs $29.99 per month (or $19.99 if billed annually). Premium Business runs $47.99. Sales Navigator—the tool LinkedIn actually pushes—starts at $64.99 and climbs to $149.99 .

What do you get? InMail credits (3 to 50 per month, depending on plan), advanced search filters, profile views for 90 days, and LinkedIn Learning courses. What you don't get is any guarantee—or even probabilistic estimate—that these features improve your chances of landing a job.

This is not an accident. It is structural.

LinkedIn's business model is not built on successful placements. It is built on subscriptions and attention. Premium users who find jobs cancel their subscriptions. Users who remain unemployed—or employed but anxious—keep paying. The incentive is not to optimize for outcomes. The incentive is to optimize for continued engagement.

Consider the InMail system. You receive credits. You send messages. If the recipient responds within 90 days, LinkedIn refunds your credit ^[citation:8]. This is clever product design. But it is not designed to maximize your response rate. It is designed to keep you sending messages—and paying monthly fees.

The data confirms the mismatch. LinkedIn's own figures show that members are 87% more likely to accept your InMail if you have a complete profile ^[citation:8]. Premium alone does nothing. Yet LinkedIn markets Premium as the solution, while burying the fact that profile quality—which is free—matters more.

The average cold email response rate is 5.1%. LinkedIn DMs perform better—10.3%—yet 90% of outbound outreach still happens via email ^[citation:8]. The platform has the data. It knows what works. But it sells access, not efficacy.

5. The Arms Race: How Job Seekers Are Adapting

In late 2024 and 2025, the job market was flooded with AI-generated applications. Candidates used tools like ChatGPT to "spray and pray"—hundreds of applications, each slightly customized, each keyword-stuffed. Recruiters stopped trusting words .

The result, in 2026, is a fundamental shift in how resumes are evaluated.

Old rule: Keyword stuffing. White-text keywords in document margins.
New rule: Semantic context. Natural integration. Algorithms now detect stuffing and penalize it ^[citation:5].

Old rule: Responsibilities. "Managed a team of engineers."
New rule: Impact metrics. "Accomplished [X] as measured by [Y], by doing [Z]." Every bullet point requires a number. If it doesn't have a number, it didn't happen ^[citation:5].

Old rule: Microsoft Office listed as a skill.
New rule: AI-augmented workflows. "Prompt engineering," "data synthesis via LLMs," "automated workflow design." Companies assume you can use Word. They want to know if you can use AI to work faster ^[citation:5].

Old rule: "References available upon request."
New rule: Deep-link portfolios. Engineers link directly to GitHub repos. Designers link to case studies. Marketers link to live campaign analytics ^[citation:5].

This is the evidence-based resume. It is leaner, harder to fake, and more data-driven. It is also the only strategy that works against AI screening systems that have learned to ignore empty claims.

“The shift in 2026 is actually better for career changers. Recruiters are tired of generic 'AI Spam.' They are looking for authenticity and proof of competence—things you already have from your previous career, if you know how to present them.” — ResumeAdapter, 2026 Resume Trends ^.

6. The Transparency Paradox: Why We Can't Look Inside

In January 2026, the Society for Human Resource Management (SHRM) reported that hiring teams spend 41% of total hiring time on initial screening—the phase most heavily automated by AI ^[citation:6]. Gartner found that companies using structured AI-assisted screening improved quality-of-hire by 24% ^[citation:6].

These numbers suggest AI works. But they obscure a deeper problem: we don't know why.

Economists from Secretariat, a firm that provides expert testimony in class action litigation, have developed methods to interrogate algorithms directly. By emulating applicant profiles and varying protected characteristics, they can observe changes in recommendation scores .

But this requires access. Access to model parameters. Access to training data. Access to version histories. When every employer trains its own model, and models are constantly retrained, reconstructing the exact system that rejected a specific candidate becomes nearly impossible .

Workday, in its defense, has argued that its tools are not subject to employment discrimination law because the company is not the employer. This is the transparency paradox: the entity making the decision is not the entity you can sue, and the entity you can sue claims it isn't making the decision

Management-side attorney Brent Hockaday put it plainly: "We're determining how we're fitting these 30- to 60-year-old employment statutes into the modern workplace".

The law moves slowly. Algorithms move at the speed of inference. By the time courts establish precedent, the technology has already shifted.

7. 2026: The Year the Black Box Opens—or Closes Tighter

Several developments will shape the next 12 months:

Mobley v. Workday: Fact-finding phase. If the class is certified, discovery could force Workday to disclose how its algorithms work. If not, the industry continues operating without scrutiny ^{[citation:2][citation:7]}.
EEOC enforcement: The Commission has signaled interest in algorithmic discrimination. Staffing and priorities remain in flux ^[citation:7].
State legislation: Several states are considering bills requiring bias audits of automated hiring systems. No federal framework exists.
Platform evolution: Microsoft's "Community-First" pledge, announced January 2026, suggests even Big Tech recognizes the political unsustainability of opaque AI. But pledges are not code

The $15 billion question is not whether AI will continue to dominate technical recruitment. It will. The question is whether that dominance will be accompanied by accountability.

Derek Mobley, now in his fourth year of litigation, still applies to jobs. He still receives rejection emails. The difference is that his name is attached to a case that may determine whether millions of future applicants will ever know why they were rejected—or whether the black box remains sealed.

The technology exists to audit these systems. The question is whether we have the will to demand it.

tech_trends Technology Various_news

The Honeymoon Is Over": How Arizona Cities Are Fighting Back Against the Data Center Boom

February 12, 2026

"The Honeymoon Is Over": How Arizona Cities Are Fighting Back Against the Data Center Boom

From Chandler's unanimous rejection to Hobbs' plan to kill tax breaks, to Marana's 2,800-signature referendum — Arizona's relationship with Big Tech has flipped. This is the inside story of a suburban uprising.

By Arizona Tech Watch | Published February 12, 2026 | Updated 35 minutes ago

CHANDLER, AZ — On a Thursday night in early December, the Chandler City Council chambers looked nothing like a routine land-use hearing. Residents packed every seat. Dozens stood against the walls. Some held signs reading "No More Data Centers" ^[citation:2].

The source of their frustration? A New York developer's request to rezone 10 acres of abandoned office land into a 422,000-square-foot AI data center campus. The project had attracted an unusual lobbyist: former U.S. Senator Kyrsten Sinema, who warned local officials that if they didn't approve it voluntarily, federal preemption would soon take the decision out of their hands entirely ^{[citation:2][citation:8]}.

It didn't work. The council voted 6–0 to reject the rezoning. Vice Mayor Christine Ellis, recalling a private meeting with Sinema, said she asked a single question that decided her vote: "What's in it for Chandler?" ^[citation:2].

That question is now echoing across Arizona — from Phoenix to Tucson, from Marana to Goodyear. And the answer, increasingly, is: not enough.

“This is not just Chandler. We’re dealing with this all over. Having the community show up, it sends a message to the elected officials. They need to feel the people power behind them.” — Eric Runnestrand, Chandler resident and project opponent ^[citation:2]

1. "We Don't Need to Incentivize Them Anymore" — Hobbs Draws a Line

One month after Chandler's vote, Governor Katie Hobbs sat down with Capitol Media Services for an extensive interview. Her message was blunt. The tax breaks Arizona has offered data centers since 2013 — exemptions from state and local sales taxes on billions of dollars of equipment — have "done their job." Now, she said, it's time to "strike the right balance" ^[citation:3].

On January 12, Hobbs made it official in her State of the State address. She announced she would push to eliminate new data center incentives entirely, and impose a penny-per-gallon fee on water used by data centers — estimated to raise $6.5 million annually for conservation efforts ^[citation:3].

The logic is straightforward. Arizona is now the number-two market for data centers in the world. The industry no longer needs taxpayer subsidies to choose the state. "I don't think the taxpayers should be subsidizing them," Hobbs said ^[citation:3].

Her position has created a rare moment of bipartisan convergence. Republican Representative Neal Carter of San Tan Valley pre-filed HB2119 on January 2, 2026 — a bill that would amend state law to stop accepting new data center tax credit applications after December 31, 2026 ^[citation:6]. Carter's reasoning? His constituents "think they're ugly. They think they use a lot of water. It's a bad way to do public policy." ^[citation:3].

Even U.S. Representative Andy Biggs, who voted for the original 2013 incentives as a state senator and is now running for governor, has signaled support for rolling them back ^[citation:3].

2. The Great Arizona Divide: Rejection in Phoenix, Embrace in Marana

But while the legislature debates, cities are already voting with their zoning codes.

Phoenix: The city has effectively halted new data center construction near high-capacity transit, enacting buffer rules that make large-scale projects nearly impossible inside the urban core ^[citation:2].

Tucson: The City Council in August 2025 rejected annexing and providing water to Project Blue, a $3.6 billion data center complex proposed by Beale Infrastructure. The project is now planned outside city limits, but Attorney General Kris Mayes is appealing the Arizona Corporation Commission's approval of its special energy supply agreement, calling it an "unprecedented deal" negotiated "behind closed doors" that violates the Arizona Constitution ^{[citation:3][citation:7]}.

Chandler: Already home to 10 data centers, the city passed strict zoning rules in 2022 limiting where new ones can go. When Active Infrastructure sought a rezoning exception, the planning office recommended denial. The council agreed unanimously ^{[citation:2][citation:8]}.

Then there is Marana.

On January 6, 2026, the Marana Town Council voted 6–0 to rezone 600 acres of farmland at Luckett and Hardin roads for a Beale Infrastructure data center campus. The developer promised $5 billion in investment, $145 million in tax revenue, and 4,200 construction jobs. The land is owned by the Kai Family Trust and the Church of Jesus Christ of Latter-day Saints. Councilmember Herb Kai recused himself ^[citation:5].

But residents had not been consulted. There were no town halls. Beale representatives said they completed outreach to ten nearby property owners — the minimum required by law. That was enough ^[citation:5].

Within four days of the rezoning vote, the No Desert Data Center Coalition collected 2,800 signatures on each of two referendum petitions — double the legal requirement. On February 4, they submitted them to the Town Clerk. If validated, the project goes to the ballot, and the rezoning is on hold indefinitely ^[citation:5].

“We got over 2,800 signatures in four days. It goes to show just how opposed voters in Marana were. Without any public hearing, any town halls to gauge resident support or opposition, they pushed it through.” — Vivek Bharathan, No Desert Data Center Coalition ^[citation:5]

3. The Counter-Movement: 65 Business Leaders Push Back

Not everyone is celebrating the anti-data-center wave.

On February 2, a coalition of 65 Arizona business and industry leaders sent a letter to Governor Hobbs. They thanked her for her focus on sustainability — then warned that eliminating incentives without a transparent public conversation about energy costs and economic impact could harm the state's competitiveness ^[citation:1].

"Data centers," the letter read, "are foundational to economic competitiveness and national security amid a rapid transformation driven by artificial intelligence, cloud computing and digital infrastructure" ^[citation:1].

The tension is real. Arizona's data center boom has created thousands of construction jobs and millions in local tax revenue. The industry points to its evolution: closed-loop cooling systems that use far less water, air-cooled designs that reduce strain on the grid, and commitments to hire locally. Beale Infrastructure, for its part, has said the Marana facility will use only 40 acre-feet of water annually — compared to 2,000 acre-feet for the farmland it replaces ^[citation:5].

But opponents have learned to ask better questions. Water use at the site may be low, they note, but upstream water use at the power plants generating 550–750 megawatts of electricity could be orders of magnitude higher — possibly exceeding 11,000 acre-feet per year. Those costs, and those emissions, are not counted in the developer's presentations ^[citation:5].

4. Microsoft's "Community-First" Pivot: The Industry Knows the Party's Over

Perhaps the clearest signal that the honeymoon is ending came not from a city council chamber, but from Redmond, Washington.

On January 13, 2026, Microsoft announced its "Community-First AI Infrastructure Initiative" ^{[citation:4][citation:10]}. The five-point pledge is extraordinary for its candor:

We will pay the costs so our data centers do not raise your electricity bills.
We will reduce our water consumption and replenish what we use.
We will create jobs for your residents.
We will grow the tax base for local hospitals, schools, parks, and libraries.
We will invest in local AI training and nonprofits ^[citation:4].

Microsoft President Brad Smith was even more direct in a statement to international media. With technology companies earning record profits, he said, asking the public to subsidize AI's electricity costs is "neither fair nor politically sustainable" ^{[citation:10]}.

President Donald Trump praised the move on social media, writing that tech giants building data centers must "pay their own way" ^{[citation:10]}.

This is not charity. It is survival. In Virginia, Ohio, and other data center hubs, residential electricity rates rose 12–16% last year — far above the national average. The political backlash is no longer a future risk. It is here ^{[citation:10]}.

5. "What Are They Afraid Of?" — Mayes Takes On Project Blue's Secrecy

While cities and developers fight over zoning, Attorney General Kris Mayes is waging a parallel war over transparency.

In January, the Arizona Corporation Commission approved a special energy supply agreement between Tucson Electric Power and Beale Infrastructure for Project Blue. The deal allows the data center to negotiate its own electric rates — authority Mayes argues belongs exclusively to the ACC and cannot be delegated to private parties ^[citation:7].

Her office filed for a rehearing. The ACC denied it. Now Mayes is preparing an appeal.

"Why is the agreement full of redactions?" she asked in a public statement. "Why didn't they hold a full hearing or allow the City of Tucson to cross-examine TEP? What are they afraid of?" ^[citation:7].

The case could set a statewide precedent determining whether data centers can negotiate secret, individualized utility rates — or whether those rates must be set transparently, like every other customer's.

6. 2026: The Year Local Democracy Caught Up With Big Tech

Here is where things stand on February 12, 2026.

Legislature: HB2119 would end new data center tax credits after December 31, 2026. Governor Hobbs supports it. It faces opposition from business coalitions but has bipartisan backing ^{[citation:1][citation:3][citation:6]}.
Marana: 2,800 signatures are under verification. If validated, the data center referendum heads to the Pima County Recorder, then to a public vote — likely the August primary or November general election ^[citation:5].
Project Blue: Still planned outside Tucson city limits, but Attorney General Mayes' appeal could unravel its power agreement ^[citation:7].
Microsoft: Implementing its Community-First pledge in Goodyear and El Mirage. Competitors are watching closely. If the model works, others will follow ^{[citation:4][citation:10]}.

The narrative has shifted. In 2022, Arizona lawmakers renewed data center tax breaks through 2033. Four years later, both parties are racing to repeal them. Cities that once competed to offer the most generous terms are now competing to erect the highest walls.

This is not an accident. It is the product of thousands of residents showing up to council meetings, signing petitions, and demanding answers to a simple question Chandler's vice mayor posed three months ago:

"What's in it for us?"

Arizona's cities are still waiting for an answer they can believe.

Technology Various_news

The $5 Billion Mystery: Marana’s Data Center Referendum and the Unknown Tenant

February 12, 2026

The $5 Billion Mystery: Marana’s Data Center Referendum and the Unknown Tenant

The Town Council approved 600 acres. Residents delivered 2,800 signatures. Beale Infrastructure is the developer. But the name on the lease? Nobody is saying.

By Arizona Tech Watch | Published February 12, 2026 | Updated 15 minutes ago

MARANA, AZ — On a Tuesday night in early January, the Marana Town Council chambers looked nothing like a normal government meeting. Opponents held signs asking how councilmembers would pay their own electric bills if a data center spiked rates. Supporters in bright orange safety vests spoke about trade jobs and staying in town long enough to have dinner with their kids. By 10:30 p.m., the vote was 6–0. The rezoning passed. ^[1]

But here is the thing about Marana: it is a town of 60,000 people, and a lot of them felt steamrolled. Within four weeks, the No Desert Data Center Coalition collected more than 2,800 signatures — double the required amount — to force a referendum. If the town clerk validates them (and all signs say she will), this project goes to the ballot. ^[2]

“We’re a town of over 60,000 people. For 0.01% of the town to make a decision of that magnitude… it just didn’t feel right.” — Jackie McGuire, Marana resident and petition organizer

1. The Ghost in the Machine: Who Is the End User?

Let’s start with the most glaring detail. The developer, Beale Infrastructure, is well known in Southern Arizona. They are also behind Project Blue, the controversial data center proposal in Pima County that the Tucson City Council rejected in 2025. But here is where it gets strange: the Marana town website explicitly states that the identity of the end user is unknown. Beale’s representatives have declined to name the tenant. ^[3]

In 2023, Pima County documents identified Amazon Web Services as the prospective operator for Project Blue. But AWS has not confirmed anything publicly since then, and Beale has stopped confirming or denying. For Marana, this silence is fuel on fire. ^[4]

“Beale currently has no customers for this data center and multiple financial agencies believe this is a bubble,” Sue Ritz, a National Guard veteran and Marana resident, told the Planning Commission in December. “I’m not against progress. I want written guarantees.” ^[5]

2. A $5 Billion Bet on 600 Acres of Farmland

The numbers are almost too round. $5 billion in capital investment. $145 million in tax revenue to Marana over 10 years. 4,200 construction jobs. 400 permanent positions. That is the promise Beale Infrastructure laid out in its presentations. The two parcels — one owned by the Kai Family Trust (linked to Councilmember Herb Kai, who recused himself), the other by the Church of Jesus Christ of Latter-day Saints — are currently farmland. They consume about 2,000 acre-feet of water per year growing crops. Beale says the new facility will use only 40 acre-feet. ^[6]

But opponents are not buying the math. “Air-cooled does not mean water-free,” Ritz argued. Because the data center will draw massive amounts of electricity (between 550 and 750 megawatts at full build-out), and because Arizona generates much of its power from thermal plants that consume water, the “upstream” water use could be huge — possibly 11,100 acre-feet per year. That is equivalent to thousands of households. ^[7]

Acres: 600 (two 300-acre parcels)
Investment: $5 billion
Power required: 550–750 MW (enough for ~500,000 homes)
Construction jobs: 4,200
Permanent jobs: 400
Tax revenue (Marana): $145 million / 10 years
Referendum signatures: 2,800+ submitted Feb. 4, 2026
End user: Undisclosed

3. 2,800 Signatures in Four Days: “An Inflection Point”

When the No Desert Data Center Coalition started gathering signatures, even they were surprised by the speed. “We got over 2,800 signatures in four days,” said Vivek Bharathan. “It goes to show just how opposed voters in Marana were.” The requirement was 1,360. They more than doubled it. ^[8]

Town Manager Terry Rozema admitted he was not shocked. “Was I excited about it? Not necessarily. But it’s part of the process, and we anticipated it.” ^[9] The Town Clerk now has 20 business days to verify signatures. If validated, the measure heads to the Pima County Recorder’s Office, then likely to the ballot — possibly as early as the primary election later this year.

Jackie McGuire, who helped lead the petition, told reporters: “This feels like an inflection point for Marana. I’m proud of it, whichever way the vote goes. I hope our elected representatives realize that more people involved in local government is a really good thing.” ^[10]

4. “Get the Hell Out”: Inside the Jan. 6 Meeting

If you watch the recording of the January 6 council meeting, it is not a dry land-use hearing. It is tense. Councilmember Patrick Cavanaugh, who ultimately voted yes, voiced serious concerns about noise and air quality. “I think the noise is going to be a lot greater than you think, and the soot and smoke coming out of those generators is not going to be pretty,” he said. He voted yes anyway, saying, “I need a lot of trust. I need to trust the power companies. I need to trust Beale.” ^[11]

Vice Mayor Roxanne Ziegler had a sharper exchange. When a University of Arizona student mentioned campaign contributions from people tied to Beale, Ziegler demanded a right to reply, told the student to read campaign finance laws, and later told another speaker he could “get the hell out.” Mayor Jon Post also clashed with opponents, expressing disappointment that the late Mayor Ed Honea’s name was brought into the discussion. ^[12]

Herb Kai, whose family trust owns one of the parcels, recused himself and did not attend. The other landowner? The Church of Jesus Christ of Latter-day Saints. ^[13]

5. This Isn’t Just Marana: Arizona’s Data Center Reckoning

What is happening in Marana is a microcosm of a statewide eruption. In January, Governor Katie Hobbs used her State of the State address to announce she no longer supports tax breaks for data centers. “It’s time we make the booming data center industry work for the people of our state rather than the other way around,” she said. She proposed making data centers pay the same per-gallon rate for water as residential customers — which could generate millions for the Colorado River Protection Fund. ^[14]

Meanwhile, Arizona Public Service (APS) has said it has 30,000 megawatts of data center requests in its queue — nearly four times its current capacity. “We’ve never sat in a position before where somebody’s asking you to triple the size of your company,” APS’s executive vice president told KTAR. ^[15]

And then there is Kyrsten Sinema. The former senator now runs the AI Infrastructure Coalition, lobbying for projects like this. She warned Chandler officials last year: if local governments don’t approve AI data centers proactively, federal preemption will take the decision out of their hands. “When federal preemption comes, we’ll no longer have that privilege. It will just happen.” ^[16]

“Make data centers pay their fair share for the water they use. The average Arizona family pays one cent for every gallon. If data centers paid the same, we could make a multi-million-dollar deposit into the Colorado River Protection Fund every single year.” — Gov. Katie Hobbs, State of the State, Jan. 12, 2026

6. The Ballot, The Backlash, and The Black Box

Here is where things stand on February 12, 2026.

Petition status: Submitted Feb. 4. Town Clerk verification in progress (20-day window).
Likelihood of referendum: Almost certain. Signatures far exceeded requirement.
Project status: Rezoning approved, but on hold pending referendum outcome.
Beale’s statement: “We appreciate the council’s vote of confidence. The next phase will go into design and permitting.” No word on groundbreaking. ^[17]
End user: Still unknown.

Beale Infrastructure, backed by Blue Owl Capital (a $295 billion investment firm), is playing the long game. They have already shifted Project Blue from water-cooling to air-cooling after public outcry. They are promising road upgrades, water line extensions, and $20 million for a new road. But the question hanging over every public meeting, every petition signature, and every council vote is simple: Who are we really building this for? ^[18]

Until that question gets an answer, Marana — a town of veterans, farmers, and retirees — is not backing down. And in a few months, its 60,000 residents may get the final say.

tech_trends

State of Play Returns Today: 60+ Minutes of PS5 Games, Marathon, and Silent Hill

February 12, 2026

PLAYSTATION · FEBRUARY 12, 2026 · 7 MIN READ

State of Play Returns Today: 60+ Minutes of PS5 Games, Marathon, and Silent Hill

Sony is flipping the switch on its first major PlayStation showcase of 2026 later today. The February State of Play promises over an hour of fresh trailers, gameplay deep dives, and announcements spanning third‑party blockbusters, indie curios, and first‑party studios. Here is everything you need to know before the stream goes live.

“This is the longest State of Play Sony has ever produced – a clear signal that the PS5 software pipeline is about to become very visible.”

When and Where to Watch

The broadcast kicks off Thursday, February 12 at 2pm PT / 5pm ET / 10pm GMT / 11pm CET. For viewers in Japan, that is 7am JST on Friday, February 13.

You can catch the entire show live on PlayStation’s official YouTube and Twitch channels. The presentation will be in English with Japanese subtitles available. Sony has confirmed the runtime will exceed 60 minutes, making it the most substantial State of Play in years.

Confirmed Games – No Guessing Required

Unlike most pre‑show speculation, we already know a few titles that will appear. Marathon, Bungie’s sci‑fi extraction shooter, is locked in. The studio confirmed on its official Discord that the game will feature during the main showcase, with a dedicated deep‑dive stream to follow. Set to launch March 5, Marathon carries significant weight for Sony’s live‑service ambitions after the success of Helldivers 2 and the swift collapse of Concord.

Silent Hill fans should stay seated after the credits roll. Konami has scheduled a Silent Hill Transmission immediately following State of Play, promising updates on Townfall and potentially other unannounced projects.

Tomb Raider: Legacy of Atlantis is also expected to surface. Crystal Dynamics often celebrates Lara Croft’s canonical birthday on February 14, making this Thursday the perfect moment for a new trailer and a release date reveal.

What Will Likely Appear

Saros and the First‑Party Slate

Housemarque’s follow‑up to Returnal, Saros, is scheduled for April 30. With launch just two months away, a substantial gameplay segment is practically guaranteed. The same logic applies to Phantom Blade Zero (September 2026) and Marvel Tokon: Fighting Souls from Arc System Works, which has already leaked via retail listings.

The Wolverine-sized elephant in the room is Marvel’s Wolverine. Insomniac explicitly stated on X that new information will not arrive until “Spring 2026” (March–May). Yet fans remain hopeful for a brief teaser. Spring technically starts in March, and this is February. It is not impossible.

Capcom, Square Enix, and the Third‑Party Bloc

Resident Evil Requiem releases February 27. A final trailer and a shadow‑dropped demo – mirroring Capcom’s Village playbook – would be a logical capstone. Monster Hunter Stories 3 is also positioned for a March launch and could use the airtime.

The dream is Final Fantasy VII Remake Part 3. Director Naoki Hamaguchi recently confirmed the game is “playable,” and Square Enix has teased a 2026 reveal. A full trailer may be reserved for Summer Game Fest, but the official title could drop today.

Indie and Curveball Predictions

Sony explicitly highlighted “eye‑catching” indie games in its announcement. One perennial hopeful is Little Devil Inside, which has been in development purgatory for years. Another is 007 First Light and LEGO Batman: Legacy of the Dark Knight, both rumored for 2026.

Less likely but not impossible: a PS5 Pro shadow‑drop or an OLED PS Portal announcement. Sony typically reserves hardware for dedicated showcases, but the extended runtime invites speculation.

The Copyright Caveat – Read This If You Stream or Clip

PlayStation has issued an unusually stern warning regarding licensed music embedded in the broadcast. Because Sony does not control those rights, co‑streams and VOD archives may be automatically muted, blocked, or taken down.

If you plan to upload recap videos, clips, or full‑show archives, the official advice is unambiguous: edit out any copyrighted music before posting. The warning language first appeared in 2022 and has been repeated verbatim for this event, so treat it seriously.

📺 The Bottom Line

Today’s State of Play is not a typical 20‑minute sizzle reel. It is a deliberate, long‑form statement about what PlayStation 5 looks like for the next 18 months. Marathon needs a win. Saros needs a spotlight. Third‑party partners need release dates. And somewhere in the mix, there is almost certainly something no one has predicted.

Tune in. Keep your expectations measured for Wolverine. And if you are streaming, mute the music.

Filed under: PlayStation · State of Play · PS5 · Marathon · Silent Hill · Gaming News