Sora Review: I Finally Got Access to OpenAI's Video AI and Here's the Truth

I've been on the Sora waitlist since February 2024, refreshing my email obsessively like I was waiting for concert tickets. When OpenAI first teased their AI video generator with those mind-blowing demos—the woman walking through Tokyo, the golden retrievers podcasting on a mountain—I immediately knew this was different from anything else in the AI video space.

Ten months later, in December 2024, I finally got access. I've now spent six weeks generating hundreds of videos, testing Sora's limits, comparing it obsessively to every competitor, burning through my monthly credits, and forming opinions that are honestly more complicated than I expected.

Here's what nobody's saying clearly enough: Sora is simultaneously the most impressive AI video tool I've used and the most frustrating. The quality ceiling is higher than anything else available, but the reliability floor is lower than tools that have been on the market for a year. It produces absolute magic about 30% of the time, complete garbage about 20% of the time, and something in between the remaining 50%.

This isn't a hype piece or a hit job. This is my honest experience after extensive real-world use of what might be the most talked-about AI tool that almost nobody can actually access yet.

What Is Sora and Why Does Everyone Care?

Sora is OpenAI's text-to-video AI model that generates videos from written descriptions. You type a prompt describing what you want to see, and Sora creates video footage of that scene. It can also animate still images, extend existing videos, or blend multiple videos together.

OpenAI announced Sora in February 2024 with demo videos that looked dramatically better than anything else in the AI video space at the time. While competitors like Runway and Pika were producing impressive results, Sora's demos showed a level of realism, motion consistency, and physical understanding that seemed almost impossible. That woman walking through Tokyo wasn't just moving—she was interacting with the environment in ways that showed sophisticated understanding of physics, lighting, and spatial relationships.

The hype exploded immediately. Every tech publication covered it. Social media buzzed with speculation about how Sora would revolutionize filmmaking. Hollywood started worrying about AI replacing human creators. And then... OpenAI went quiet. For months, there was no public release, no clear timeline, just occasional updates about red-teaming and safety testing.

The limited access program started in late March 2024 for select creators, researchers, and red-teamers. True public access didn't begin until December 2024 when OpenAI launched Sora as part of ChatGPT Pro subscriptions. Even then, access was restricted to paying subscribers, not available in many countries, and subject to heavy usage limits.

This scarcity created a hunger for information. What does Sora actually do beyond those cherry-picked demos? How does it perform in real-world use? Is it worth the hype and the wait? Those are the questions I set out to answer through extensive testing.

My Access Journey and Testing Approach

Getting access was the first challenge. I signed up for the waitlist in February, was rejected from the initial creator program, tried again in September, and finally got approved for the ChatGPT Pro tier in December when access expanded. The $200/month subscription fee for ChatGPT Pro is steep, but it includes unlimited GPT-4 usage, priority access to new features, and Sora access with monthly generation limits.

Once I had access, I committed to testing Sora seriously rather than just playing with it casually. I generated videos across multiple categories to understand where it excels and where it fails. I created photorealistic footage to test visual quality and physics understanding. I attempted character-driven narratives to see if it could maintain consistency. I generated abstract and artistic content to explore creative possibilities. I tried practical use cases like product demos and B-roll footage. I pushed boundaries with complex prompts to find its limits.

I also compared Sora directly to competitors by using identical prompts across multiple platforms. I generated the same scenes in Sora, Runway ML, Kling AI, Google Veo, and Pika to see how they handled identical scenarios. This revealed both Sora's advantages and its surprising weaknesses compared to tools that have been available for longer.

I tracked my success rate carefully because headlines about AI capabilities rarely mention reliability. Out of my first 100 generations, about 30 produced results I was genuinely excited about and would consider using in professional projects. About 20 were complete failures with obvious glitches, impossible physics, or incoherent motion. The remaining 50 were usable but not exceptional—decent quality but nothing I couldn't get from cheaper, more accessible alternatives.

The Quality: When Sora Works, It's Stunning

Let me start with what Sora does brilliantly, because when it works, the results are genuinely jaw-dropping.

The visual fidelity and realism exceed anything else I've tested. Sora can generate footage that, at first glance, looks like it was filmed with professional equipment. The detail in textures, the quality of lighting, the depth of focus effects—all of these are noticeably better than competitors when Sora is firing on all cylinders.

I generated a video of ocean waves crashing on rocks at sunset. The detail in the water spray, the way light refracted through the foam, the realistic motion of the waves—it looked like professional stock footage. I've shown it to people without mentioning it was AI-generated, and they assumed I'd filmed it or licensed it from a stock library. That level of realism is rare in AI-generated video.

The understanding of physics and natural motion is where Sora shows its technical sophistication. Objects move with realistic momentum, gravity, and interaction with their environment. Fluids flow naturally. Fabric moves convincingly. People walk with proper gait and weight distribution most of the time.

I tested this specifically with a prompt for smoke rising from a candle and dispersing in the air. The smoke movement showed realistic turbulence, dispersion patterns, and light interaction that demonstrated sophisticated physics simulation. Compare this to earlier AI video tools where smoke looked animated and artificial, and the improvement is dramatic.

The camera movement capabilities allow for complex, cinematic camera work that was difficult or impossible in earlier AI video tools. Sora understands camera concepts like dolly zooms, rack focus, tracking shots, and crane movements. You can specify these in prompts and Sora executes them with surprising accuracy.

A prompt for "slow dolly zoom on a woman's face, gradually revealing the busy coffee shop behind her, cinematic" produced exactly that effect—the vertiginous perspective shift of a dolly zoom combined with focus changes and environmental reveal. That's sophisticated camera work that demonstrates real understanding, not just pattern matching.

The temporal consistency—maintaining coherent visuals throughout the video duration—is better than most competitors. Characters and objects that appear in frame generally maintain their appearance and proportions across the full clip. Backgrounds don't morph randomly. The visual style stays consistent from beginning to end.

I generated a video of a dog running through a field, which is notoriously difficult because maintaining the dog's appearance, proportions, and anatomy while animating complex motion across multiple seconds is technically challenging. Sora's version kept the dog recognizably the same dog throughout the clip with consistent fur color, body proportions, and movement that looked like a real dog rather than morphing between frames.

The prompt understanding and adherence to complex instructions is generally excellent. Sora can handle multi-part prompts with specific details about subjects, actions, environment, lighting, style, and camera work. It doesn't ignore parts of your prompt the way less sophisticated tools often do.

I gave Sora a deliberately complex prompt describing a specific scene with multiple elements: "An elderly man in a weathered blue coat feeding pigeons in a foggy Victorian-era London square at dawn, gas lamps still lit, one pigeon mid-flight, soft golden light breaking through the fog, static camera with shallow depth of focus on the man." Sora generated a video that included every element I specified, positioned reasonably as described, with the lighting and atmosphere I requested. That level of prompt fidelity is impressive.

The Reliability Problem: Sora's Achilles Heel

Now let me talk about what frustrates me most about Sora—the inconsistency and unpredictability that makes it difficult to rely on for actual work.

The generation success rate is far lower than I expected based on the demo videos. As I mentioned earlier, only about 30% of my generations were genuinely excellent. Twenty percent were outright failures. The remaining 50% were mediocre—usable but not impressive. For a tool that's been in development this long with this much hype, that success rate is disappointing.

I generated the same prompt five times to test consistency, describing a simple scene of a person pouring coffee. Out of five generations, one was excellent with realistic liquid physics and smooth motion. Two were decent but had minor issues with hand movements or the liquid stream. One had the coffee mysteriously changing color mid-pour. One had the cup melting and morphing into the table. The variance from identical prompts is wild.

The random glitches and artifacts that appear even in otherwise good videos undermine professional usability. Objects occasionally morph unexpectedly, backgrounds sometimes shift subtly between frames, body parts can multiply or disappear briefly, and physics breaks down randomly in ways that destroy the illusion of reality.

I had a beautiful video of a forest path with sunlight filtering through trees absolutely ruined when a squirrel in the background suddenly doubled—there were briefly two identical squirrels in the same location before they merged back into one. These uncanny moments are jarring and make videos unusable for professional applications because you can't predict when they'll happen.

The prompt interpretation is excellent when it works but maddeningly unpredictable when it doesn't. Sora might nail a complex prompt perfectly, then completely misinterpret a simpler prompt for no apparent reason. Sometimes it focuses on the wrong elements, emphasizing background details while getting the main subject wrong. Other times it adds elements you didn't request or ignores key specifications.

I asked for "a red sports car driving down a coastal highway at sunset." Simple enough. Sora gave me a blue sedan on a mountain road at midday. Same prompt, regenerated three times—finally on the fourth attempt I got something resembling what I asked for. There's no clear reason why it misinterpreted such a straightforward prompt, which makes it feel random and frustrating.

The face and hand generation, while improved from earlier AI video, still has regular failures. Closeups of faces can look uncanny with weird expressions or subtle morphing. Hands are better than they used to be but still sometimes have extra fingers, impossible positions, or disturbing movements. This is a known AI challenge, but it's particularly problematic in video where these issues persist across frames rather than being single-image glitches.

I attempted to generate a video of a person waving at the camera. Out of ten attempts, only three had hands that looked consistently normal throughout the entire clip. The others had fingers that briefly multiplied, hands that morphed position impossibly, or weird articulation that immediately flagged the video as AI-generated. For a model this advanced, the hand problem remaining this prevalent is disappointing.

The generation time is painfully long compared to competitors. Sora videos can take 5-15 minutes to generate depending on length and complexity. During high usage periods, I've waited over 30 minutes. When you're iterating on prompts trying to get exactly what you want, these wait times add up to hours of sitting around waiting for generations to complete.

By comparison, Runway ML typically generates videos in 2-5 minutes, Kling AI in 3-7 minutes, and Google Veo in similar timeframes. Sora's longer wait times would be acceptable if the success rate was dramatically higher, but when you're waiting 15 minutes for a generation that turns out to be unusable and need to regenerate, the time investment becomes frustrating.

Sora vs. The Competition: Direct Comparisons

I tested identical prompts across Sora, Runway ML, Kling AI, Google Veo, and Pika to see how they compare. The results surprised me because Sora didn't dominate across the board like I expected based on the hype.

For photorealistic nature and environment footage, Sora generally produced the best results when it worked correctly. A prompt for "waves crashing on rocky coastline at sunset" gave Sora's best generation the edge over competitors in terms of water physics, lighting quality, and overall realism. However, Kling AI's best generation was remarkably close, and Google Veo produced very competitive results as well.

The key phrase is "when it worked correctly." Sora's best results exceeded competitors, but its worst results were often worse than competitors' failures. Runway and Kling had tighter quality distributions—less spectacular highs but fewer catastrophic lows.

For camera movement and cinematography, Sora excels more consistently than competitors. Complex camera instructions were followed more accurately in Sora than in other tools. A prompt for "slow tracking shot following a cat walking along a fence, shallow depth of focus on the cat with blurred background" produced better camera work in Sora than any alternative I tested.

Runway ML has good camera controls but Sora's understanding of complex camera movements is more sophisticated. Kling AI produces smooth motion but less precise camera control. Google Veo is competitive but not quite at Sora's level for cinematic camera work.

For human characters and people in videos, Sora doesn't have a clear advantage and sometimes performs worse than competitors. Kling AI and Google Veo have produced more consistently natural-looking human characters in my testing. Sora's humans are sometimes stunning but other times fall into uncanny valley territory or have obvious glitches.

A simple prompt for "woman walking down a city street" produced better results more consistently in Kling AI than in Sora. Sora's best attempts were slightly better, but the reliability wasn't there. For use cases focused on human subjects, Sora isn't the automatic best choice.

For stylized and artistic content rather than photorealism, Pika and Runway often match or exceed Sora. When I wanted anime-style content, watercolor aesthetics, or artistic stylization, Pika delivered more consistently. Runway's style controls gave me more precise creative direction than Sora's prompt-based approach.

Sora can absolutely do stylized content, but it seems optimized for photorealism. Tools designed with artistic and creative use cases as priorities sometimes feel more intuitive for non-realistic content.

For speed of generation and iteration, every competitor beats Sora. If you're trying to rapidly iterate on a concept or need quick turnaround, the wait times make Sora impractical. Runway, Kling, Pika, and Veo all generate faster, which dramatically impacts workflow when you're refining ideas through multiple generations.

For cost and accessibility, Sora is at a serious disadvantage. At $200/month for ChatGPT Pro with monthly generation limits, it's the most expensive option. Runway ML, Kling AI, and competitors offer free tiers and subscriptions ranging from free to $30-50/month with reasonable usage limits. Google Veo's access is limited but when available will likely be more accessible than Sora's current pricing.

The honest competitive assessment is that Sora produces the highest quality results when everything works perfectly, but the combination of reliability issues, slow generation times, limited access, and high cost means it's not the obvious best choice for most users despite the superior technology.

Real-World Use Cases: What Actually Works

Let me share specific applications where I found Sora useful and where it disappointed.

For cinematic B-roll and environmental footage, Sora excels when you need high-quality establishing shots, nature footage, cityscape backgrounds, or atmospheric environmental videos. I generated establishing shots for a video project—city streets at night, forest scenes with fog, ocean footage—and Sora's results were good enough to use in the final edit alongside real footage.

The key is that these use cases don't require consistency across multiple shots or precise control over specific details. You're looking for general atmosphere and quality, which Sora delivers reliably enough. The occasional failed generation isn't a problem because you can just regenerate until you get something usable.

For abstract and experimental art, Sora creates interesting results for music videos, art installations, experimental film, or creative projects where realism isn't the goal. The occasional glitches and unpredictable elements can even be features rather than bugs in artistic contexts.

I used Sora to generate abstract visuals for a friend's music video—flowing colors, morphing shapes, impossible physics—and the dreamlike quality actually worked perfectly for the psychedelic aesthetic we wanted. The fact that reality broke down occasionally enhanced rather than detracted from the artistic intent.

For concept visualization and pre-visualization, Sora helps directors, designers, or creative teams visualize concepts before committing to production. You can explore different approaches, test visual ideas, or communicate creative vision without expensive production or elaborate storyboards.

I worked with a commercial director testing ideas for a campaign. We generated multiple visual approaches in Sora to show the client different creative directions. The videos weren't final production quality, but they communicated the concepts effectively and helped the client make decisions before investing in actual production.

For product demonstration in stylized contexts, Sora can show products in aspirational or impossible environments that would be expensive to create practically. The key word is "stylized"—realistic product details are still challenging, but creative contexts work well.

I tested generating product videos showing a watch in various environments—mountaintop at sunrise, underwater reef, space station window. The watch details weren't perfect, but the environmental context was impressive and would work for stylized advertising that doesn't require precise product accuracy.

Where Sora consistently failed for me was anything requiring narrative consistency, specific brand requirements, or legal precision. Multiple shots with the same character maintaining appearance across scenes is extremely difficult. Generating specific recognizable brands or logos results in distorted or inaccurate representations. Any content that needs to be legally accurate or contractually precise isn't suitable for Sora's current capabilities.

I tried creating a simple narrative sequence—three shots of the same character in different locations telling a visual story. Maintaining character consistency across the shots was nearly impossible. Each generation produced a subtly different person despite identical character descriptions in prompts. For narrative filmmaking, this is a dealbreaker.

The Prompt Engineering Reality

Getting good results from Sora requires significant prompt engineering skill, which the demo videos and marketing don't emphasize enough.

Effective prompts need to be detailed but not overly complex, specifying subjects and actions clearly, describing environment and lighting, including camera movement instructions, noting desired style or aesthetic, and sometimes using negative prompts to exclude unwanted elements. But there's an art to finding the right balance—too vague and you get random results, too specific and Sora sometimes gets confused or ignores parts of your prompt.

I've developed a working format that gives me better success rates. I start with the main subject and action, then add environmental context, then specify lighting and atmosphere, then camera instructions, then overall style notes. Something like: "A [subject] [action] in a [environment], [lighting details], [camera movement], [style notes]."

For example: "A golden retriever puppy playing with a ball in a sunlit backyard garden, late afternoon warm lighting with long shadows, camera slowly orbiting around the puppy at ground level, shallow depth of focus with bokeh background, shot on vintage 35mm film with slight grain."

That structure gives Sora enough information to work with while maintaining clear priorities about what matters most. But even with well-structured prompts, results vary significantly, and there's still trial and error involved.

The learning curve is real, and it takes time to develop intuition for what Sora understands and how it interprets instructions. After six weeks of regular use, my success rate improved from about 30% early on to maybe 45% now. That's progress, but it demonstrates that getting good results isn't as simple as typing a sentence and getting magic—it requires learning the model's language and tendencies.

Cost Analysis: Is It Worth $200/Month?

The economics of Sora access through ChatGPT Pro require honest examination because $200/month is significant money.

What you get for $200/month includes unlimited GPT-4 and GPT-4o usage for text interactions, priority access to new ChatGPT features and models, access to Sora with monthly generation limits (OpenAI hasn't specified exact limits but reports suggest around 50-100 video generations per month depending on length), and access to other AI tools within the ChatGPT platform.

For someone who uses ChatGPT heavily for work and wants Sora access, the bundled value might justify the cost. If you're already considering ChatGPT Plus at $20/month for better AI access and would pay an additional $180/month for Sora specifically, the math could work.

For someone wanting only Sora for video generation, $200/month is extremely expensive compared to alternatives. Runway ML costs $15-$95/month depending on tier. Kling AI has free access with paid tiers around $10-$30/month. Pika and other competitors have similar pricing. Google Veo's pricing isn't finalized but will likely be more accessible than $200/month.

The value calculation depends heavily on your use case and success rate. If you need five usable videos per month and Sora gives you the quality you need, maybe $200 is justifiable for professional work. If you're generating dozens of videos and batting 30-40% success rate, you're paying $200 for maybe 15-20 usable results, which is expensive per video.

My personal assessment after six weeks is that the current pricing isn't justified for most users. Unless you're a professional creator with specific needs that Sora meets better than alternatives and budget isn't a primary concern, cheaper options provide better value. The technology is impressive but not $200/month more impressive than tools costing $15-$30/month.

I suspect OpenAI priced this high deliberately to limit usage during the limited rollout phase rather than as sustainable long-term pricing. I expect prices will adjust once Sora moves to broader availability, but for now, it's a premium product with premium pricing.

The Ethical and Industry Impact Questions

Beyond personal use, Sora raises larger questions about AI video generation's impact on creative industries and society.

The impact on video production jobs is real and concerning. If AI can generate high-quality footage for a fraction of the cost of traditional production, what happens to cinematographers, gaffers, production assistants, and other crew members who depend on video production for their livelihood? The transition will be challenging for many workers in these fields.

At the same time, AI video tools also create new opportunities—prompt engineers, AI video specialists, hybrid creators who combine AI generation with traditional techniques. The industry is changing, not simply disappearing, but the transition period will be painful for some.

The copyright and training data questions remain unresolved. Sora was trained on video data from the internet, likely including copyrighted material. The legal and ethical implications of this training approach are still being litigated and debated. If you generate videos with Sora, you're benefiting from technology built on data that may include other creators' copyrighted work.

OpenAI states that Sora outputs are owned by the user who generates them, but whether AI-generated content can be copyrighted at all remains legally uncertain in many jurisdictions. This creates ambiguity about intellectual property rights that professional creators need to understand.

The misinformation and deepfake potential of video generation AI is genuinely concerning. While OpenAI has implemented watermarking and usage policies to prevent misuse, determined bad actors will find ways around these safeguards. The ability to generate convincing video footage of events that never happened or people doing things they never did has obvious potential for manipulation and harm.

I tested Sora's restrictions by attempting to generate videos of public figures and controversial scenarios. The content filters blocked most obvious misuse attempts, but the technology's inherent capability to create convincing fake video is concerning regardless of current safeguards. As AI video quality improves and access expands, the potential for abuse increases.

The environmental impact of training and running large AI models like Sora is non-trivial. Each video generation requires significant computational resources and energy consumption. At scale, the environmental cost of AI video generation across millions of users becomes a real concern that's rarely discussed in conversations about AI capabilities.

The Access Problem: Who Can Actually Use Sora?

One of the most frustrating aspects of Sora is the extremely limited access that makes most discussion about it theoretical for most people.

Geographic restrictions currently prevent access from many countries, including the UK, EU member states, and others due to regulatory concerns. If you're outside supported regions, you can't access Sora regardless of willingness to pay. This makes Sora effectively unavailable to a significant portion of the global population.

The subscription requirement through ChatGPT Pro at $200/month creates an economic barrier that excludes casual users, students, hobbyists, and anyone for whom $200/month isn't affordable. This is by far the most expensive AI video tool currently available.

The monthly generation limits within the $200/month subscription restrict usage even for paying subscribers. Reports vary on exact limits, but you can't generate unlimited videos—there are monthly caps that prevent using Sora as your primary video production tool if you have high volume needs.

The waitlist and gradual rollout mean even people willing to pay for ChatGPT Pro may not get immediate access. OpenAI is expanding access gradually, which means demand exceeds supply and creates waiting periods.

The comparison to competitors is stark. Runway ML has free trials and accessible paid tiers. Kling AI has generous free access with optional upgrades. Pika and other tools have low-cost entry points. Google Veo's access is limited but will likely be more accessible when it launches commercially. Sora stands out for being the least accessible option despite having impressive technology.

This access strategy seems designed to manage computational costs and prevent misuse during the early rollout, which is understandable from OpenAI's perspective. But it also means most people discussing Sora online haven't actually used it, which creates a weird disconnect between the conversation about Sora and the practical reality of actually accessing and using it.

My Honest Recommendation

After six weeks of intensive use, here's my genuine advice for different types of users.

If you're already a ChatGPT Pro subscriber for other reasons and get Sora as a included benefit, absolutely explore it and see if it fits your creative workflow. The marginal cost is zero since you're already paying for the subscription, so there's no financial downside to experimenting.

If you're considering ChatGPT Pro primarily for Sora access, I'd recommend trying cheaper alternatives first. Test Runway ML, Kling AI, or Pika extensively before committing $200/month to Sora. You might find that less expensive tools meet your needs well enough that Sora's incremental quality improvement doesn't justify the massive price difference.

If you're a professional video creator evaluating AI tools for commercial work, Sora is worth testing when you can get access. The quality ceiling is higher than alternatives, which might matter for specific high-end projects. But don't expect it to replace your primary video workflow—think of it as one specialized tool among many.

If you're a casual user interested in AI video generation for personal projects or experimentation, Sora is overkill at current pricing. Start with free or low-cost alternatives that provide perfectly adequate quality for non-professional use. Sora's advantages are mainly relevant for professional applications where the quality difference matters economically.

If you're in a country without Sora access, focus your attention on the tools you can actually use. Getting frustrated about limited access to Sora while ignoring accessible alternatives that might serve your needs well is counterproductive.

If you're a creative professional worried about AI replacing your work, my advice is to learn these tools and understand their capabilities and limitations rather than dismissing them. AI video generation is real and improving rapidly, but it's not replacing human creativity—it's changing the toolkit available to creative humans. Understanding how to work with AI as a tool while providing the creative direction, judgment, and refinement it can't provide puts you in a stronger position than ignoring the technology entirely.

FAQ

The Verdict

After six weeks with Sora, my feelings are genuinely mixed in ways I didn't expect.

The technology is undeniably impressive. When Sora produces good results, they're often the best AI-generated video I've seen. The physical understanding, the visual quality, the camera work—all demonstrate sophisticated AI capability that represents real advancement in the field.

But the product experience is frustrating and inconsistent. The unreliable success rate, the slow generation times, the unpredictable prompt interpretation, the high cost, and the limited access all create friction that undermines the impressive technology.

Sora feels like extraordinarily advanced research technology that's been released to users before it's truly ready for prime time. The demos that blew everyone away in February were cherry-picked best results, not representative of typical generations. The real user experience involves a lot more trial and error, failed generations, and disappointment than those perfect demos suggested.

I'm continuing to use Sora for specific projects where its strengths align with my needs, but I'm also continuing to use Runway, Kling, and other tools because no single AI video generator is reliable enough or versatile enough to be my only option. The best approach seems to be maintaining access to multiple tools and choosing the right one for each specific task.

For the broader conversation about AI video generation, Sora represents an important advancement but not the revolutionary leap that will change everything overnight. The technology is improving rapidly across multiple companies, and the gap between Sora and competitors is smaller than the hype suggests. All these tools are impressive, all have significant limitations, and none are ready to replace traditional video production for most applications.

The future of video creation will involve AI tools as part of the creative process, not as replacement for human creators. Learning how to effectively use these tools while providing the creative vision, judgment, and refinement they lack is the skill set worth developing.

Sora is a remarkable piece of technology that sometimes produces astonishing results. It's also an imperfect product with frustrating limitations, high costs, and limited access. Both things are true simultaneously, and understanding that nuance matters more than either hype or dismissal.

If you get the chance to try Sora, do it with realistic expectations. You'll see some genuinely impressive results that feel like magic. You'll also encounter failures that remind you this is early-stage technology still finding its footing. Both experiences are valuable for understanding where AI video generation actually is today, not where marketing suggests it might be.

The hype was both justified and overblown. The technology is real and impressive. The practical reality is messier and more limited than the headlines suggested. Welcome to the actual experience of using Sora in 2025.

Related Articles & Suggested Reading

Synthesia Review 2025: Creating Professional Videos Without a Camera

Synthesia in 2025 — how it works, pricing plans, use cases, pros and cons, and whether it’s worth it for creating professional AI-powered videos without a camera.

Humai.blog - Al Insights, Tools & Productivity WorkflowsDani

Kling AI 2.0 Review: Why 22 Million Users Are Obsessed With This Viral Video Tool

Why 22 million users love Kling AI 2.0. The viral AI video generator that turns simple prompts into smooth, cinematic clips in seconds.

Humai.blog - Al Insights, Tools & Productivity WorkflowsDani

PixVerse vs LTX Studio: AI Video Tools Compared

A detailed comparison of PixVerse and LTX Studio — two leading AI video tools. Explore their key features, strengths, use cases, and which one is best for your creative workflow.

Humai.blog - Al Insights, Tools & Productivity WorkflowsDani