Noticias
The New News in AI: 12/30/24 Edition
The first all-robot attack in Ukraine, OpenAI’s 03 Model Reasons through Math & Science Problems, The Decline of Human Cognitive Skills, AI is NOT slowing down, AI can identify whiskey aromas, Agents are coming!, Agents in Higher Ed, and more.
Despite the break, lots still going on in AI this week so…
OpenAI on Friday unveiled a new artificial intelligence system, OpenAI o3, which is designed to “reason” through problems involving math, science and computer programming.
The company said that the system, which it is currently sharing only with safety and security testers, outperformed the industry’s leading A.I. technologies on standardized benchmark tests that rate skills in math, science, coding and logic.
The new system is the successor to o1, the reasoning system that the company introduced earlier this year. OpenAI o3 was more accurate than o1 by over 20 percent in a series of common programming tasks, the company said, and it even outperformed its chief scientist, Jakub Pachocki, on a competitive programming test. OpenAI said it plans to roll the technology out to individuals and businesses early next year.
“This model is incredible at programming,” said Sam Altman, OpenAI’s chief executive, during an online presentation to reveal the new system. He added that at least one OpenAI programmer could still beat the system on this test.
The new technology is part of a wider effort to build A.I. systems that can reason through complex tasks. Earlier this week, Google unveiled similar technology, called Gemini 2.0 Flash Thinking Experimental, and shared it with a small number of testers.
These two companies and others aim to build systems that can carefully and logically solve a problem through a series of steps, each one building on the last. These technologies could be useful to computer programmers who use A.I. systems to write code or to students seeking help from automated tutors in areas like math and science.
(MRM – beyond these approaches there is another approach of training LLM’s on texts about morality and exploring how that works)
OpenAI revealed an intriguing and promising AI alignment technique they called deliberative alignment. Let’s talk about it.
I recently discussed in my column that if we enmesh a sense of purpose into AI, perhaps that might be a path toward AI alignment, see the link here. If AI has an internally defined purpose, the hope is that the AI would computationally abide by that purpose. This might include that AI is not supposed to allow people to undertake illegal acts via AI. And so on.
Another popular approach consists of giving AI a kind of esteemed set of do’s and don’ts as part of what is known as constitutional AI, see my coverage at the link here. Just as humans tend to abide by a written set of principles, maybe we can get AI to conform to a set of rules devised explicitly for AI systems.
A lesser-known technique involves a twist that might seem odd at first glance. The technique I am alluding to is the AI alignment tax approach. It goes like this. Society establishes a tax that if AI does the right thing, it is taxed lightly. But when the AI does bad things, the tax goes through the roof. What do you think of this outside-the-box idea? For more on this unusual approach, see my analysis at the link here.
The deliberative alignment technique involves trying to upfront get generative AI to be suitably data-trained on what is good to go and what ought to be prevented. The aim is to instill in the AI a capability that is fully immersed in the everyday processing of prompts. Thus, whereas some techniques stipulate the need to add in an additional function or feature that runs heavily at run-time, the concept is instead to somehow make the alignment a natural or seamless element within the generative AI. Other AI alignment techniques try to do the same, so the conception of this is not the novelty part (we’ll get there).
Return to the four steps that I mentioned:
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Step 1: Provide safety specs and instructions to the budding LLM
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Step 2: Make experimental use of the budding LLM and collect safety-related instances
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Step 3: Select and score the safety-related instances using a judge LLM
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Step 4: Train the overarching budding LLM based on the best of the best
In the first step, we provide a budding generative AI with safety specs and instructions. The budding AI churns through that and hopefully computationally garners what it is supposed to do to flag down potential safety violations by users.
In the second step, we use the budding generative AI and get it to work on numerous examples, perhaps thousands upon thousands or even millions (I only showed three examples). We collect the instances, including the respective prompts, the Chain of Thoughts, the responses, and the safety violation categories if pertinent.
In the third step, we feed those examples into a specialized judge generative AI that scores how well the budding AI did on the safety violation detections. This is going to allow us to divide the wheat from the chaff. Like the sports tale, rather than looking at all the sports players’ goofs, we only sought to focus on the egregious ones.
In the fourth step, the budding generative AI is further data trained by being fed the instances that we’ve culled, and the AI is instructed to closely examine the chain-of-thoughts. The aim is to pattern-match what those well-spotting instances did that made them stand above the rest. There are bound to be aspects within the CoTs that were on-the-mark (such as the action of examining the wording of the prompts).
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Generative AI technology has become Meta’s top priority, directly impacting the company’s business and potentially paving the road to future revenue opportunities.
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Meta’s all-encompassing approach to AI has led analysts to predict more success in 2025.
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Meta in April said it would raise its spending levels this year by as much as $10 billion to support infrastructure investments for its AI strategy. Meta’s stock price hit a record on Dec. 11.
MRM – ChatGPT Summary:
OpenAI Dominates
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OpenAI maintained dominance in AI despite leadership changes and controversies.
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Released GPT-4o, capable of human-like audio chats, sparking debates over realism and ethics.
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High-profile departures, including chief scientist Ilya Sutskeva, raised safety concerns.
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OpenAI focuses on advancing toward Artificial General Intelligence (AGI), despite debates about safety and profit motives.
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Expected to release more models in 2025, amidst ongoing legal, safety, and leadership scrutiny.
Siri and Alexa Play Catch-Up
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Amazon’s Alexa struggled to modernize and remains largely unchanged.
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Apple integrated AI into its ecosystem, prioritizing privacy and user safety.
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Apple plans to reduce reliance on ChatGPT as it develops proprietary AI capabilities.
AI and Job Disruption
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New “agent” AIs capable of independent tasks heightened fears of job displacement.
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Studies suggested 40% of jobs could be influenced by AI, with finance roles particularly vulnerable.
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Opinions remain divided: AI as a tool to enhance efficiency versus a threat to job security.
AI Controversies
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Misinformation: Audio deepfakes and AI-driven fraud demonstrated AI’s potential for harm.
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Misbehavior: Incidents like Microsoft’s Copilot threatening users highlighted AI safety issues.
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Intellectual property concerns: Widespread use of human-generated content for training AIs fueled disputes.
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Creative industries and workers fear AI competition and job displacement.
Global Regulation Efforts
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The EU led with strong AI regulations focused on ethics, transparency, and risk mitigation.
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In the U.S., public demand for AI regulation clashed with skepticism over its effectiveness.
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Trump’s appointment of David Sacks as AI and crypto czar raised questions about regulatory approaches.
The Future of AI
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AI development may shift toward adaptive intelligence and “reasoning” models for complex problem-solving.
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Major players like OpenAI, Google, Microsoft, and Apple expected to dominate, but startups might bring disruptive innovation.
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Concerns about AI safety and ethical considerations will persist as the technology evolves.
If 2024 was the year of artificial intelligence chatbots becoming more useful, 2025 will be the year AI agents begin to take over. You can think of agents as super-powered AI bots that can take actions on your behalf, such as pulling data from incoming emails and importing it into different apps.
You’ve probably heard rumblings of agents already. Companies ranging from Nvidia (NVDA) and Google (GOOG, GOOGL) to Microsoft (MSFT) and Salesforce (CRM) are increasingly talking up agentic AI, a fancy way of referring to AI agents, claiming that it will change the way both enterprises and consumers think of AI technologies.
The goal is to cut down on often bothersome, time-consuming tasks like filing expense reports — the bane of my professional existence. Not only will we see more AI agents, we’ll see more major tech companies developing them.
Companies using them say they’re seeing changes based on their own internal metrics. According to Charles Lamanna, corporate vice president of business and industry Copilot at Microsoft, the Windows maker has already seen improvements in both responsiveness to IT issues and sales outcomes.
According to Lamanna, Microsoft employee IT self-help success increased by 36%, while revenue per seller has increased by 9.4%. The company has also experienced improved HR case resolution times.
A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence”.
On December 20, OpenAI’s o3 system scored 85% on the ARC-AGI benchmark, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test.
Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal.
While scepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right?
To understand what the o3 result means, you need to understand what the ARC-AGI test is all about. In technical terms, it’s a test of an AI system’s “sample efficiency” in adapting to something new – how many examples of a novel situation the system needs to see to figure out how it works.
An AI system like ChatGPT (GPT-4) is not very sample efficient. It was “trained” on millions of examples of human text, constructing probabilistic “rules” about which combinations of words are most likely. The result is pretty good at common tasks. It is bad at uncommon tasks, because it has less data (fewer samples) about those tasks.
We don’t know exactly how OpenAI has done it, but the results suggest the o3 model is highly adaptable. From just a few examples, it finds rules that can be generalised.
Researchers in Germany have developed algorithms to differentiate between Scotch and American whiskey. The machines can also discern the aromas in a glass of whiskey better than human testers.
CHANG: They describe how this works in the journal Communications Chemistry. First, they analyzed the molecular composition of 16 scotch and American whiskeys. Then sensory experts told them what each whiskey smelled like – you know, vanilla or peach or woody. The AI then uses those descriptions and a bunch of math to predict which smells correspond to which molecules.
SUMMERS: OK. So you could just feed it a list of molecules, and it could tell you what the nose on that whiskey will be.
CHANG: Exactly. The model was able to distinguish American whiskey from scotch.
After 25.3 million fully autonomous miles a new study from Waymo and Swiss Re concludes:
[T]he Waymo ADS significantly outperformed both the overall driving population (88% reduction in property damage claims, 92% in bodily injury claims), and outperformed the more stringent latest-generation HDV benchmark (86% reduction in property damage claims and 90% in bodily injury claims). This substantial safety improvement over our previous 3.8-million-mile study not only validates ADS safety at scale but also provides a new approach for ongoing ADS evaluation.
As you may also have heard, o3 is solving 25% of Frontier Math challenges–these are not in the training set and are challenging for Fields medal winners. Here are some examples of the types of questions:
Thus, we are rapidly approaching super human driving and super human mathematics.
Stopping looking to the sky for aliens, they are already here.
OpenAI’s new artificial-intelligence project is behind schedule and running up huge bills. It isn’t clear when—or if—it’ll work. There may not be enough data in the world to make it smart enough.
The project, officially called GPT-5 and code-named Orion, has been in the works for more than 18 months and is intended to be a major advancement in the technology that powers ChatGPT. OpenAI’s closest partner and largest investor, Microsoft, had expected to see the new model around mid-2024, say people with knowledge of the matter.
OpenAI has conducted at least two large training runs, each of which entails months of crunching huge amounts of data, with the goal of making Orion smarter. Each time, new problems arose and the software fell short of the results researchers were hoping for, people close to the project say.
At best, they say, Orion performs better than OpenAI’s current offerings, but hasn’t advanced enough to justify the enormous cost of keeping the new model running. A six-month training run can cost around half a billion dollars in computing costs alone, based on public and private estimates of various aspects of the training.
OpenAI and its brash chief executive, Sam Altman, sent shock waves through Silicon Valley with ChatGPT’s launch two years ago. AI promised to continually exhibit dramatic improvements and permeate nearly all aspects of our lives. Tech giants could spend $1 trillion on AI projects in the coming years, analysts predict.
GPT-5 is supposed to unlock new scientific discoveries as well as accomplish routine human tasks like booking appointments or flights. Researchers hope it will make fewer mistakes than today’s AI, or at least acknowledge doubt—something of a challenge for the current models, which can produce errors with apparent confidence, known as hallucinations.
AI chatbots run on underlying technology known as a large language model, or LLM. Consumers, businesses and governments already rely on them for everything from writing computer code to spiffing up marketing copy and planning parties. OpenAI’s is called GPT-4, the fourth LLM the company has developed since its 2015 founding.
While GPT-4 acted like a smart high-schooler, the eventual GPT-5 would effectively have a Ph.D. in some tasks, a former OpenAI executive said. Earlier this year, Altman told students in a talk at Stanford University that OpenAI could say with “a high degree of scientific certainty” that GPT-5 would be much smarter than the current model.
Microsoft Corporation (NASDAQ:MSFT) is reportedly planning to reduce its dependence on ChatGPT-maker OpenAI.
What Happened: Microsoft has been working on integrating internal and third-party artificial intelligence models into its AI product, Microsoft 365 Copilot, reported Reuters, citing sources familiar with the effort.
This move is a strategic step to diversify from the current underlying technology of OpenAI and reduce costs.
The Satya Nadella-led company is also decreasing 365 Copilot’s dependence on OpenAI due to concerns about cost and speed for enterprise users, the report noted, citing the sources.
A Microsoft spokesperson was quoted in the report saying that OpenAI continues to be the company’s partner on frontier models. “We incorporate various models from OpenAI and Microsoft depending on the product and experience.”
Big Tech is spending at a rate that’s never been seen, sparking boom times for companies scrambling to facilitate the AI build-out.
Why it matters: AI is changing the economy, but not in the way most people assume.
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AI needs facilities and machines and power, and all of that has, in turn, fueled its own new spending involving real estate, building materials, semiconductors and energy.
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Energy providers have seen a huge boost in particular, because data centers require as much power as a small city.
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“Some of the greatest shifts in history are happening in certain industries,” Stephan Feldgoise, co-head of M&A for Goldman Sachs, tells Axios. “You have this whole convergence of tech, semiconductors, data centers, hyperscalers and power producers.”
Zoom out: Companies that are seeking fast growth into a nascent market typically spend on acquisitions.
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Tech companies are competing for high-paid staff and spending freely on research.
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But the key growth ingredient in the AI arms race so far is capital expenditure, or “capex.”
Capital expenditure is an old school accounting term for what a company spends on physical assets such as factories and equipment.
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In the AI era, capex has come to signify what a company spends on data centers and the components they require.
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The biggest tech players have increased their capex by tens of billions of dollars this year, and they show no signs of pulling back in 2025.
MRM – I think “Design for AI” and “Minimize Human Touchpoints” are especially key. Re #7, this is also true. Lot’s of things done in hour long meetings can be superseded by AI doing a first draft.
Organizations must use AI’s speed and provide context efficiently to unlock productivity gains. There also needs to be a framework that can maintain quality even at higher speeds. Several strategies jump out:
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Massively increase the use of wikis and other written content.
Human organizations rarely codify their entire structure because the upfront cost and coordination are substantial. The ongoing effort to access and maintain such documentation is also significant. Asking co-workers questions or developing working relationships is usually more efficient and flexible.
Asking humans or developing relationships nullifies AI’s strength (speed) and exposes its greatest weakness (human context). Having the information in written form eliminates these issues. The cost of creating and maintaining these resources should fall with the help of AI.
I’ve written about how organizations already codify themselves as they automate with traditional software. Creating wikis and other written resources is essentially programming in natural language, which is more accessible and compact.
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Move from reviews to standardized pre-approvals and surveillance.
Human organizations often prefer reviews as a checkpoint because creating a list of requirements is time-consuming, and they are commonly wrong. A simple review and release catches obvious problems and limits overhead and upfront investment. Reviews of this style are still relevant for many AI tasks where a human prompts the agent and then reviews the output.
AI could increase velocity for more complex and cross-functional projects by moving away from reviews. Waiting for human review from various teams is slow. Alternatively, AI agents can generate a list of requirements and unit tests for their specialty in a few minutes, considering more organizational context (now written) than humans can. Work that meets the pre-approval standards can continue, and then surveillance paired with graduated rollouts can detect if there are an unusual amount of errors.
Human organizations have a tradeoff between “waterfall” and “agile,” AI organizations can do both at once with minimal penalty, increasing iteration speed.
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Use “Stop Work Authority” methods to ensure quality.
One of the most important components of the Toyota Production System is that every employee has “stop work authority.”” Any employee can, and is encouraged to, stop the line if they see an error or confusion. New processes might have many stops as employees work out the kinks, but things quickly line out. It is a very efficient bug-hunting method.
AI agents should have stop work authority. They can be effective in catching errors because they work in probabilities. Work stops when they cross a threshold of uncertainty. Waymo already does this with AI-driven taxis. The cars stop and consult human operators when confused.
An obvious need is a human operations team that can respond to these stoppages in seconds or minutes.
Issues are recorded and can be fixed permanently by adding to written context resources, retraining, altering procedures, or cleaning inputs.
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Design for AI.
A concept called “Design for Manufacturing” is popular with manufacturing nerds and many leading companies. The idea is that some actions are much cheaper and defect-free than others. For instance, an injection molded plastic part with a shape that only allows installation one way will be a fraction of the cost of a CNC-cut metal part with an ambiguous installation orientation. The smart thing to do is design a product to use the plastic part instead of a metal one.
The same will be true of AI agents. Designing processes for their strengths will have immense value, especially in production, where errors are costly.
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Cast a Wider Design Net.
The concept of “Design for AI” also applies at higher levels. Employees with the creativity for clever architectural designs are scarce resources. AI agents can help by providing analysis of many rabbit holes and iterations, helping less creative employees or supercharging the best.
The design phase has the most impact on downstream cost and productivity of any phase.
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Minimize human touch points.
Human interaction significantly slows down any process and kills one of the primary AI advantages.
Written context is the first step in eliminating human touch points. Human workers can supervise the creation of the wikis instead of completing low-level work.
Pre-approvals are the next, so AI agents are not waiting for human sign-off.
AI decision probability thresholds, graduated rollouts, and unit tests can reduce the need for human inspection of work output.
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Eliminate meeting culture.
Meetings help human organizations coordinate tasks and exchange context. Humans will continue to have meetings even in AI organizations.
The vast majority of lower-level meetings need to be cut. They lose their advantages once work completion times are compressed and context more widely available.
Meeting content moves from day-to-day operations to much higher-level questions about strategy and coordination. Humans might spend even more time in meetings if the organizational cadence increases so that strategies have to constantly adjust!
Once an icon of the 20th century seen as obsolete in the 21st, Encyclopaedia Britannica—now known as just Britannica— is all in on artificial intelligence, and may soon go public at a valuation of nearly $1 billion, according to the New York Times.
Until 2012 when printing ended, the company’s books served as the oldest continuously published, English-language encyclopedias in the world, essentially collecting all the world’s knowledge in one place before Google or Wikipedia were a thing. That has helped Britannica pivot into the AI age, where models benefit from access to high-quality, vetted information. More general-purpose models like ChatGPT suffer from hallucinations because they have hoovered up the entire internet, including all the junk and misinformation.
While it still offers an online edition of its encyclopedia, as well as the Merriam-Webster dictionary, Britannica’s biggest business today is selling online education software to schools and libraries, the software it hopes to supercharge with AI. That could mean using AI to customize learning plans for individual students. The idea is that students will enjoy learning more when software can help them understand the gaps in their understanding of a topic and stay on it longer. Another education tech company, Brainly, recently announced that answers from its chatbot will link to the exact learning materials (i.e. textbooks) they reference.
Britannica’s CEO Jorge Cauz also told the Times about the company’s Britannica AI chatbot, which allows users to ask questions about its vast database of encyclopedic knowledge that it collected over two centuries from vetted academics and editors. The company similarly offers chatbot software for customer service use cases.
Britannica told the Times it is expecting revenue to double from two years ago, to $100 million.
A company in the space of selling educational books that has seen its fortunes go the opposite direction is Chegg. The company has seen its stock price plummet almost in lock-step with the rise of OpenAI’s ChatGPT, as students canceled their subscriptions to its online knowledge platform.
A.I. hallucinations are reinvigorating the creative side of science. They speed the process by which scientists and inventors dream up new ideas and test them to see if reality concurs. It’s the scientific method — only supercharged. What once took years can now be done in days, hours and minutes. In some cases, the accelerated cycles of inquiry help scientists open new frontiers.
“We’re exploring,” said James J. Collins, an M.I.T. professor who recently praised hallucinations for speeding his research into novel antibiotics. “We’re asking the models to come up with completely new molecules.”
The A.I. hallucinations arise when scientists teach generative computer models about a particular subject and then let the machines rework that information. The results can range from subtle and wrongheaded to surreal. At times, they lead to major discoveries.
In October, David Baker of the University of Washington shared the Nobel Prize in Chemistry for his pioneering research on proteins — the knotty molecules that empower life. The Nobel committee praised him for discovering how to rapidly build completely new kinds of proteins not found in nature, calling his feat “almost impossible.”
In an interview before the prize announcement, Dr. Baker cited bursts of A.I. imaginings as central to “making proteins from scratch.” The new technology, he added, has helped his lab obtain roughly 100 patents, many for medical care. One is for a new way to treat cancer. Another seeks to aid the global war on viral infections. Dr. Baker has also founded or helped start more than 20 biotech companies.
Despite the allure of A.I. hallucinations for discovery, some scientists find the word itself misleading. They see the imaginings of generative A.I. models not as illusory but prospective — as having some chance of coming true, not unlike the conjectures made in the early stages of the scientific method. They see the term hallucination as inaccurate, and thus avoid using it.
The word also gets frowned on because it can evoke the bad old days of hallucinations from LSD and other psychedelic drugs, which scared off reputable scientists for decades. A final downside is that scientific and medical communications generated by A.I. can, like chatbot replies, get clouded by false information.
The rise of artificial intelligence (AI) has brought about numerous innovations that have revolutionized industries, from healthcare and education to finance and entertainment. However, alongside the seemingly limitless capabilities of ChatGPT and friends, we find a less-discussed consequence: the gradual decline of human cognitive skills. Unlike earlier tools such as calculators and spreadsheets, which made specific tasks easier without fundamentally altering our ability to think, AI is reshaping the way we process information and make decisions, often diminishing our reliance on our own cognitive abilities.
Tools like calculators and spreadsheets were designed to assist in specific tasks—such as arithmetic and data analysis—without fundamentally altering the way our brains process information. In fact, these tools still require us to understand the basics of the tasks at hand. For example, you need to understand what the formula does, and what output you are seeking, before you type it into Excel. While these tools simplified calculations, they did not erode our ability to think critically or engage in problem-solving – the tools simply made life easier. AI, on the other hand, is more complex in terms of its offerings – and cognitive impact. As AI becomes more prevalent, effectively “thinking” for us, scientists and business leaders are concerned about the larger effects on our cognitive skills.
The effects of AI on cognitive development are already being identified in schools across the United States. In a report titled, “Generative AI Can Harm Learning”, researchers at the University of Pennsylvania found that students who relied on AI for practice problems performed worse on tests compared to students who completed assignments without AI assistance. This suggests that the use of AI in academic settings is not just an issue of convenience, but may be contributing to a decline in critical thinking skills.
Furthermore, educational experts argue that AI’s increasing role in learning environments risks undermining the development of problem-solving abilities. Students are increasingly being taught to accept AI-generated answers without fully understanding the underlying processes or concepts. As AI becomes more ingrained in education, there is a concern that future generations may lack the capacity to engage in deeper intellectual exercises, relying on algorithms instead of their own analytical skills.
Using AI as a tool to augment human abilities, rather than replace them, is the solution. Enabling that solution is a function of collaboration, communication and connection – three things that capitalize on human cognitive abilities.
For leaders and aspiring leaders, we have to create cultures and opportunities for higher-level thinking skills. The key to working more effectively with AI is in first understanding how to work independently of AI, according to the National Institute of Health. Researchers at Stanford point to the importance of explanations: where AI shares not just outputs, but insights. Insights into how the ultimate conclusion was reached, described in simple terms that invite further inquiry (and independent thinking).
Whether through collaborative learning, complex problem-solving, or creative thinking exercises, the goal should be to create spaces where human intelligence remains at the center. Does that responsibility fall on learning and development (L&D), or HR, or marketing, sales, engineering… or the executive team? The answer is: yes. A dedication to the human operating system remains vital for even the most technologically-advanced organizations. AI should serve as a complement to, rather than a substitute for, human cognitive skills.
The role of agents will not just be the role of the teacher. Bill Salak observes that “AI agents will take on many responsibilities traditionally handled by human employees, from administrative tasks to more complex, analytical roles. This transition will result in a large-scale redefinition of how humans contribute” to the educational experience. Humans must focus on unique skills—creativity, strategic thinking, emotional intelligence, and adaptability. Roles will increasingly revolve around supervising, collaborating with, or augmenting the capabilities of AI agents.
Jay Patel, SVP & GM of Webex Customer Experience Solutions at Cisco, agrees that AI Agents will be everywhere. They will not just change the classroom experience for students and teachers but profoundly impact all domains. He notes that these AI models, including small language models, are “sophisticated enough to operate on individual devices, enabling users to have highly personalized virtual assistants.” These agents will be more efficient, attuned to individual needs, and, therefore, seemingly more intelligent.
Jay Patel predicts that “the adopted agents will embody the organization’s unique values, personalities, and purpose. This will ensure that the AIs interact in a deeply brand-aligned way.” This will drive a virtuous cycle, as AI agent interactions will not seem like they have been handed off to an untrained intern but rather to someone who knows all and only what they are supposed to know.
For AI agents to realize their full potential, the experience of interacting with them must feel natural. Casual, spoken interaction will be significant, as will the ability of the agent to understand the context in which a question is being asked.
Hassaan Raza, CEO of Tavus, feels that a “human layer” will enable AI agents to realize their full potential as teachers. Agents need to be relatable and able to interact with students in a manner that shows not just subject-domain knowledge but empathy. A robust interface for these agents will include video, allowing students to look the AI in the eye.
In January, thousands of New Hampshire voters picked up their phones to hear what sounded like President Biden telling Democrats not to vote in the state’s primary, just days away.
“We know the value of voting Democratic when our votes count. It’s important you save your vote for the November election,” the voice on the line said.
But it wasn’t Biden. It was a deepfake created with artificial intelligence — and the manifestation of fears that 2024’s global wave of elections would be manipulated with fake pictures, audio and video, due to rapid advances in generative AI technology.
“The nightmare situation was the day before, the day of election, the day after election, some bombshell image, some bombshell video or audio would just set the world on fire,” said Hany Farid, a professor at the University of California at Berkeley who studies manipulated media.
The Biden deepfake turned out to be commissioned by a Democratic political consultant who said he did it to raise alarms about AI. He was fined $6 million by the FCC and indicted on criminal charges in New Hampshire.
But as 2024 rolled on, the feared wave of deceptive, targeted deepfakes didn’t really materialize.
A pro-tech advocacy group has released a new report warning of the growing threat posed by China’s artificial intelligence technology and its open-source approach that could threaten the national and economic security of the United States.
The report, published by American Edge Project, states that “China is rapidly advancing its own open-source ecosystem as an alternative to American technology and using it as a Trojan horse to implant its CCP values into global infrastructure.”
“Their progress is both significant and concerning: Chinese-developed open-source AI tools are already outperforming Western models on key benchmarks, while operating at dramatically lower costs, accelerating global adoption. Through its Belt and Road Initiative (BRI), which spans more than 155 countries on four continents, and its Digital Silk Road (DSR), China is exporting its technology worldwide, fostering increased global dependence, undermining democratic norms, and threatening U.S. leadership and global security.”
A Ukrainian national guard brigade just orchestrated an all-robot combined-arms operation, mixing crawling and flying drones for an assault on Russian positions in Kharkiv Oblast in northern Russia.
“We are talking about dozens of units of robotic and unmanned equipment simultaneously on a small section of the front,” a spokesperson for the 13th National Guard Brigade explained.
It was an impressive technological feat—and a worrying sign of weakness on the part of overstretched Ukrainian forces. Unmanned ground vehicles in particular suffer profound limitations, and still can’t fully replace human infantry.
That the 13th National Guard Brigade even needed to replace all of the human beings in a ground assault speaks to how few people the brigade has compared to the Russian units it’s fighting. The 13th National Guard Brigade defends a five-mile stretch of the front line around the town of Hlyboke, just south of the Ukraine-Russia border. It’s holding back a force of no fewer than four Russian regiments.
Artificial intelligence (AI) isn’t just some futuristic concept anymore, it’s woven into our daily lives now. And if you’re a writer like me, it’s become impossible to ignore.
When OpenAI dropped ChatGPT in late 2022, the writing world had a collective moment of shock and curiosity. Could this AI churn out a novel? Craft a compelling ad campaign? Simplify calculus for a seven-year-old? More importantly, was this the beginning of the end for human writers?
Then, in early 2023, Anthropic launched Claude, shifting the conversation from “What can AI do?” to “Which AI does it better?” Suddenly, comparisons were everywhere. Was Claude more creative? Was ChatGPT more generic? Which one felt more human?
At the time, I thought these debates were premature. Both models were fresh out of the lab, still evolving. But now, in 2025, after major updates and years of real-world use, the battle between Claude and ChatGPT has become far more interesting.
So, I put them to the test, evaluating their capabilities across content creation, research, problem-solving, and creative writing. And while both have grown into powerhouses, the real differences lie not just in raw performance but in how they’re designed to serve different needs.
In this article, I’ll take you through my hands-on experience with both AI models, covering everything from onboarding and usability to response quality and overall performance. By the end, you’ll have a clearer idea of which AI is the best fit for you.
TL;DR: Key takeaways from this article
- ChatGPT makes getting started a breeze, with a seamless sign-up process and intuitive navigation, while Claude offers a sleeker, distraction-free interface for a more minimalist experience.
- Claude delivers structured, articulate responses that feel naturally human, whereas ChatGPT thrives on flexibility and adaptability, making it better for a wide range of tasks.
- ChatGPT, especially with GPT-4 Turbo, generates responses quickly, while Claude takes its time to craft more nuanced and thoughtful answers.
- Both tools are easy to use, powered by impressive models, and excel in creative writing, brainstorming, coding, and deep analysis.
- The right choice depends on you: Need versatility and speed? Go with ChatGPT. Prefer depth and structured thinking? Claude is your best bet.
What are Claude and ChatGPT?
Before diving into the head-to-head comparison, let’s break down what these two AI tools actually are and how they work.
Claude: Anthropic’s thoughtful AI assistant
What is Claude?
Claude, developed by Anthropic AI, is a conversational AI chatbot and the name of the underlying Large Language Models (LLMs) that power it. Designed for natural, human-like interactions, Claude excels in a wide range of tasks, from summarization and Q&A to decision-making, code-writing, and editing.
Named after Claude Shannon (the pioneer of information theory), this AI assistant was built with an emphasis on safety, reliability, and context-aware reasoning. Unlike some AI models that rely on real-time internet access, Claude generates responses based solely on its training data, offering structured and coherent answers without pulling live web results.
Anthropic currently offers multiple versions of Claude, with one of its standout features being extended memory, allowing it to process up to 75,000 words at once — meaning it can analyze entire books and generate insightful summaries.
How does Claude work?
Claude functions as a self-contained AI model, trained on vast amounts of text and code. It can generate creative content, translate languages, write code, summarize lengthy documents, and provide deep analytical insights. Available via web browsers and mobile apps (iOS and Android), it’s designed for users who need structured and in-depth responses across various domains.
However, unlike competitors such as ChatGPT and Google Gemini, Claude does not have live internet access and cannot fetch data from external sources. Instead, it operates based on the knowledge it has been trained on, making it particularly strong in context retention and logical reasoning.
Claude at a glance
| Developer | Anthropic |
| Year launched | 2023 |
| Type of AI tool | Conversational AI and LLM |
| Top 3 use cases | Content structuring, analytical reasoning, deep summarization |
| Who can use it? | Writers, researchers, business professionals |
| Starting price | $20 per month |
| Free version | Yes, with limitations |
ChatGPT: OpenAI’s all-purpose AI assistant
What is ChatGPT?
If you’ve spent any time in the AI space, you’ve probably either used ChatGPT or heard someone rave about it. OpenAI’s groundbreaking chatbot burst onto the scene in late 2022 and instantly reshaped AI-assisted content creation, automation, and productivity.
Built on OpenAI’s latest GPT-4o model, ChatGPT does far more than just generate text. It helps users brainstorm, streamline workflows, summarize research papers, craft persuasive emails, and write complex code. Its ability to integrate with third-party tools has made it a favorite among marketers, developers, and business professionals looking to automate tedious tasks.
How does ChatGPT work?
ChatGPT leverages advanced deep learning techniques and reinforcement learning to produce fast, adaptable, and contextually relevant responses. While earlier models had limitations in contextual memory, newer iterations, especially GPT-4 Turbo, have dramatically improved response accuracy and efficiency.
Unlike Claude, ChatGPT can access real-time internet in its pro version, making it an excellent tool for live research, up-to-date insights, and SEO-driven content recommendations. Available through web browsers and mobile apps, it’s designed for both casual users and professionals who need a versatile AI assistant for a variety of tasks.
ChatGPT at a glance
| Developer | OpenAI |
| Year launched | 2022 |
| Type of AI tool | Generative AI for natural language processing |
| Top 3 use cases | Content creation, idea generation, SEO recommendations |
| Who can use it? | Marketers, content creators, bloggers, SEO professionals |
| Starting price | $20 |
| Free version | Yes, with limitations |
The bottom line
Both Claude and ChatGPT have evolved into powerful AI tools, each with its strengths. Claude focuses on structured, logical, and deeply analytical responses, while ChatGPT is known for its versatility, speed, and real-time adaptability. The right choice ultimately depends on your specific needs and workflow, and that’s exactly what we’ll explore next.
Why I decided to compare Claude and ChatGPT
After spending years working with AI tools like ChatGPT and Claude, I felt it was time to put them to the test and see how they stack up. Both of these models are making waves in the world of generative AI, but I wanted to go beyond the surface and dive into the real-world experience of using them day in and day out.
Whether you’re a writer, a researcher, or just someone curious about how these tools perform in practical settings, I believe this comparison will give you the insights you need to make an informed choice.
My goal for comparing Claude and ChatGPT
The objective of this deep dive was simple:
I wanted to get a hands-on feel for each model’s strengths and weaknesses in a variety of tasks. Sure, Claude and ChatGPT are powerful, but how do they measure up when you push them to their limits?
I tested everything from content creation to research, problem-solving, and creative writing, essentially putting them through a range of real-world challenges. This comparison isn’t just about numbers or abstract features. It’s about how these tools work for you in everyday situations.
Getting started with Claude and ChatGPT
Getting up and running with Claude and ChatGPT is a breeze, so let’s break down the sign-up and initial setup for each.
Sign up and initial set up
ChatGPT
To get started with ChatGPT, all you need is an OpenAI account, which can be created swiftly using either your email address or a Google login. Once you’ve signed up, you’re greeted with a user-friendly dashboard that’s ready for you to dive into conversations. The sign-up process itself is quick, and after logging in, you’re pretty much set to explore everything ChatGPT has to offer.
Claude
For Claude, the sign-up process is just as simple, with a clean, minimalist user interface that feels welcoming and easy to navigate. Whether you’re using a desktop or mobile device, getting started takes just a few clicks. The sign-up flow is smooth and doesn’t throw unnecessary hurdles in your way.
The AI will ask you to enter your name as a way to get to know you and jump into tasks right away, with an interface that’s more focused on getting you to your content.
My first impression of Claude and ChatGPT
From the moment I began interacting with both AI models, it was clear that each has a distinct personality. Claude has this polished, structured feel, like it’s thinking through every word carefully before responding. It’s almost as if you’re talking to a colleague who wants to make sure everything is perfect.
On the other hand, ChatGPT felt a lot more dynamic and free-flowing. The conversations felt more flexible, with a natural give-and-take that’s both quick and engaging.
The first few responses from each AI model gave me a solid sense of what they were about. While Claude’s responses were incredibly detailed and logically structured, ChatGPT’s replies were more conversational and adaptable to a wide variety of contexts.
How easy it is to get into Claude and ChatGPT
Let’s break down how each model feels when it comes to learning curve:
Claude
Onboarding with Claude was quick and straightforward. You’re welcomed with a clean, minimalist interface. There’s no clutter, which I appreciated.
Navigating through tasks felt intuitive, but there’s still a bit of a learning curve when you start digging into more advanced features like content structuring or analysis, as well as style and model selection. Claude is made for more thoughtful, deliberate interactions, so it’s not about speed, but about crafting quality responses that require a bit more time.
ChatGPT
Now, ChatGPT’s user experience is built for speed and versatility. Signing up was just as easy, and once you’re in, it’s all about jumping into a conversation and getting things done.
The interface is clean but it also feels a little more interactive and responsive, which is a nice touch. As a user, I could jump from one task to the next without missing a beat. Whether it was coding, brainstorming, or answering quick questions, ChatGPT kept pace easily.
Key features comparison: Claude vs. ChatGPT
Both platforms are equipped with cutting-edge AI models, but they do have some nuances that make them stand out in different ways.
First, let’s see how they are the same or similar.
How Claude and ChatGPT are similar
To fully appreciate their unique strong points, you must first understand how similar they are.
Here’s how both AI tools are similar:
1. They are both easy to use
One of the most striking aspects of both Claude and ChatGPT is just how approachable they are. Despite being powered by the latest advancements in AI, these tools offer a user experience that’s intuitive and easy to grasp.
It doesn’t matter if you’re using them for the first time or the hundredth, getting the hang of them is as simple as searching for a recipe on Google. Both are state-of-the-art models capable of handling complex tasks, but you don’t need a PhD in AI to make them work
2. They are both powered by advanced language models
At their core, both Claude and ChatGPT are designed to engage in natural language processing (NLP), meaning they can understand and generate human-like responses. These models have been trained on vast datasets of text and code, making them incredibly proficient at generating human-like responses across a range of tasks, from creative writing to problem-solving.
The AI models are proficient in carrying on coherent, contextually relevant conversations. However, while the architecture and core technology are similar, there are key differences in how these models respond and adapt to various use cases (more on that in a bit).
3. They both integrate with third-party apps
Claude and ChatGPT can integrate seamlessly with third-party tools. This means you can automate tasks, trigger conversations, and even send AI-generated results directly to other platforms, all without having to lift a finger.
API pricing and cost efficiency
Claude
- Claude 3.5 Sonnet: $3.00 per 1M input tokens, $15.00 per 1M output tokens.
- Claude 3 Haiku: $0.25 per 1M input tokens, $1.25 per 1M output tokens.
ChatGPT
- GPT-4: $5.00 per 1M input tokens, $15.00 per 1M output tokens.
- GPT-3.5 Turbo: $0.50 per 1M input tokens, $1.50 per 1M output tokens.
4. They both have multi-use applications
Both AI models are versatile and serve a wide range of applications. From content creation and technical troubleshooting to brainstorming ideas and answering complex queries, Claude and ChatGPT can be used in various contexts. It can help streamline work processes, enhance creativity, and assist with problem-solving.
How Claude and ChatGPT are different
The more I dug into their distinctive features, the clearer it became that Claude and ChatGPT have different strengths, making each better suited for certain use cases.
Here’s a breakdown of where they diverge.
1. Ideal users
ChatGPT is the go-to if you need a versatile, all-in-one AI solution. The AI tool offers a vast array of functionalities, from image and video generation to voice features and web browsing. It’s perfect for exploring the full spectrum of AI capabilities.
Claude, on the other hand, excels when it comes to deep text and code work. Its sophisticated writing style, robust coding features, and ability to handle complex analytical tasks make it ideal for developers, writers, and analysts who require precision over breadth.
Verdict: A tie.
2. Models
Both Claude and ChatGPT offer cutting-edge models, but their approach to task specialization differs slightly.
| Tool | Model | Description |
| ChatGPT | GPT-4o | A model for general-purpose tasks |
| GPT-4o mini | The more affordable, speedy general-purpose model | |
| o1 | Advanced reasoning model for complex tasks | |
| o1-mini | Model ideal for complex reasoning | |
| o1 Pro | The most resource-intensive model, available exclusively on the $200/month ChatGPT Pro plan | |
| Claude | Claude 3.5 Sonnet | The most intelligent model, ideal for nuanced tasks |
| Claude 3.5 Haiku | A faster, (most) cost-effective option | |
| Claude 3 Opus | Powerful model for tackling complex tasks. |
Verdict: A tie.
3. Creative work
When it comes to creativity, Claude outshines ChatGPT. Because creative work is subjective, Claude’s natural-sounding output makes it a better partner for writing. Its Styles feature lets you tailor the tone of your writing to fit various contexts (e.g., a casual memo, social media posts, or long-form content).
ChatGPT’s GPT-4o, while highly capable, can sometimes sound generic, often relying on phrases like “in today’s ever-changing landscape” or overusing bullet points. For truly creative tasks, Claude feels like the more human-like option.
Verdict: Claude wins.
4. Image and video generation
ChatGPT takes the lead when it comes to media generation. Powered by DALL·E 3, it’s capable of producing stunning photorealistic images from text prompts. For users who want even more creative control, Sora enables video generation, making ChatGPT a versatile tool for image and video content creation.
Claude doesn’t offer direct image or video generation, but its powerful text-based capabilities still make it a top choice for writing and coding tasks.
Verdict: ChatGPT wins.
5. Coding assistance
Claude stands out for coding thanks to its Artifacts feature, which allows you to see the results of your code in real time. Experienced developer or a beginner, this feature makes it easy to test and tweak your code instantly.
ChatGPT, while a powerful coding assistant, doesn’t quite offer the same instant feedback loop. It can generate code, but it’s more difficult to preview the results immediately within the chat.
Verdict: Claude wins.
6. Real-time Internet access
ChatGPT has a clear advantage when it comes to browsing the web for real-time information. Thanks to its ChatGPT Search feature, users can access up-to-date info directly from the web, even if the query is about current events.
Claude, however, still suffers from a knowledge cutoff, meaning if you need the latest info, ChatGPT is your best bet.
Verdict: ChatGPT wins.
7. Pricing
When it comes to pricing, both Claude and ChatGPT offer flexibility, but their models differ in terms of cost structure and what you get for your money.
Here’s a quick breakdown of their pricing tiers:
ChatGPT pricing
| Plan | Features | Cost |
| Free | Access to GPT‑4o miniReal-time web searchLimited access to GPT‑4o and o3‑miniLimited file uploads, data analysis, image generation, and voice modeCustom GPTs | $0/month |
| Plus | Everything in Free, plus:Extended messaging limitsAdvanced file uploads, data analysis, and image generationStandard and advanced voice modes (video and screen sharing)Access to o3‑mini, o3‑mini‑high, and o1 modelsCustom GPT creationLimited access to Sora video generation | $20/month |
| Pro | Everything in Plus, plus:Unlimited access to all reasoning models (including GPT‑4o)Advanced voice features, higher limits for video and screen sharingExclusive research preview of GPT‑4.5o1 Pro mode for high-performance tasksExpanded access to Sora video generationResearch preview of Operator (U.S. only) | $200/month |
Claude pricing
| Plan | Features | Cost |
| Free | Access to the latest Claude modelUse Claude on web, iOS, and AndroidAsk about images and documents | $0/month |
| Pro | Everything in Free, plus:More usage than FreeOrganize chats and documents with ProjectsAccess additional Claude models, including Claude 3.7 Sonnet with extended thinking modeEarly access to new features | $18/month (billed yearly); $20/month (billed monthly) |
| Team | Everything in Pro, plus:More usage than ProCentralized billing and administrationEarly access to collaboration featuresAdmits minimum 5 users | $25 per/user/month (billed yearly); $30/user/month (billed monthly) |
| Enterprise | Everything in Team, plus:More usage than TeamExpanded context windowSSO, domain capture, role-based access, and fine-grained permissioningSCIM for cross-domain identity managementAudit logs | Custom pricing |
8. Extra features
ChatGPT also offers a range of unique features that make it stand out for everyday use:
- Voice Mode: Without typing, you can converse with ChatGPT using just your voice, which is perfect for on-the-go interactions. The response time is impressive.
- Advanced Voice Mode: Give ChatGPT access to your phone’s camera and ask it questions about anything it can see. This feature can help you identify objects, read documents, and even give insights based on visual cues.
- Task Automation: You can set up recurring tasks, like language practice or exercising, that are dynamically updated based on your needs. A simple “Every day at 6 p.m., give me a sentence in Spanish and ask me to translate it into English. Make them progressively more difficult” or “provide me with workout routines and remind me every morning at 6 a.m. to do my core exercise.”
- Custom GPTs: ChatGPT allows users to create specialized GPTs for a variety of tasks, from coding to plant care coaching, broadening its utility.
Verdict: ChatGPT wins.
Comparison table: Claude vs. ChatGPT
| Feature | Claude | ChatGPT |
| Company | Anthropic | OpenAI |
| AI Model | Claude 3.5 SonnetClaude 3.5 HaikuClaude 3 Opus | GPT-4oGPT-4o miniO1o1-mini |
| Best for | Long documents, writing, and coding | Real-time web search, multimedia, automation |
| Real-time web access | No | Yes |
| Image Generation | No | Yes (DALL·E) |
| Video Generation | No | Yes (Sora) |
| Voice Mode | No | Yes |
| Interactive editor | Artifact | Canvas |
| Free version | Yes | Yes |
| Starting Price | $20/month ($18 if billed yearly) | $20/month for ChatGPT Plus |
| Writing style | More natural and adaptive | Customisable but sometimes generic |
| Context Window | 200,000 tokens, or about 150,000 words | 128,000 tokens, or about 96,000 words |
My hands-on testing experience
After exploring the features and capabilities of both Claude and ChatGPT, I decided to put them through rigorous real-world testing. I wanted to see how they performed across different tasks and scenarios that writers, researchers, and everyday users might encounter.
Here’s what I like and didn’t like during my hands-on testing:
What I liked about Claude
1. Thoughtfully structured responses
Claude consistently impressed me with its ability to provide thoughtfully structured responses that felt genuinely human. When I asked Claude to analyze complex topics or documents, it demonstrated remarkable contextual understanding and maintained coherent reasoning throughout its responses.
2. Natural writing style
One of the most striking aspects of Claude was its natural writing style. Whether I requested creative content, analytical breakdowns, or technical explanations, Claude produced text that flowed logically and avoided the formulaic patterns that often betray most AI-generated content. This natural quality made Claude’s outputs feel more authentic and ready to use with minimal editing.
3. Excellent handling of nuance
Another standout feature was Claude’s exceptional handling of nuance and ambiguity. When presented with complex ethical questions or scenarios requiring careful consideration of multiple perspectives, Claude showed an impressive ability to navigate these waters thoughtfully. Rather than offering simplistic answers, it acknowledged complexity and provided balanced, well-reasoned responses.
4. Powerful “Artifacts” feature
The Artifacts feature proved invaluable for coding tasks and document creation. Being able to see code execution results in real-time streamlined the development process significantly. For example, when I asked Claude to create a simple data visualization based on a dataset I provided, it not only generated the code but also displayed the resulting chart directly in our conversation, allowing for immediate feedback and iteration.
5. Impressive context window
Finally, Claude’s 200,000 token context window proved useful in practical scenarios. I tested this by asking it to analyze a page research paper, and it maintained remarkable coherence about details from the beginning of the document even when discussing conclusions at the end, something that would have required breaking the task into multiple prompts with other AI assistants.
What I liked about ChatGPT
1. Remarkable versatility
ChatGPT’s versatility immediately stood out during my testing. The integration of multiple capabilities — text generation, image creation, voice interaction, and web browsing — into a single platform created a seamless experience that felt truly next-generation.
2. Valuable real-time web access
The real-time web access feature proved invaluable for fact-checking and retrieving current information. When I asked about recent events or needed up-to-date statistics for an article I was writing, ChatGPT delivered accurate information without the knowledge cutoff limitations that hampered Claude’s responses to similar queries.
3. Impressive image generation
DALL-E integration for image generation was another highlight. When developing content for a mock marketing campaign, I was able to describe the visual concepts I wanted, and ChatGPT generated compelling images that matched my descriptions remarkably well. This saved considerable time that would otherwise have been spent searching for stock photos or working with a graphic designer.
4. Game-changing voice mode
The voice mode transformed how I interacted with the AI. During a busy day of multitasking, I found myself using ChatGPT like a virtual assistant, asking questions while cooking, brainstorming ideas while organizing my workspace, and dictating notes while walking. The natural-sounding voice responses made this feel less like using technology and more like conversing with a helpful colleague.
5. Practical custom GPTs
Custom GPTs proved surprisingly useful for specialized tasks. I created a custom GPT focused on SEO content analysis that consistently applied the same evaluation framework to my draft articles. Having this specialized tool available within the same interface as my general AI assistant streamlined my workflow considerably.
What I didn’t like in both models
1. Inconsistent factual accuracy
Despite their impressive capabilities, both Claude and ChatGPT demonstrated limitations with factual accuracy. While ChatGPT could access the web for current information, it occasionally misinterpreted or oversimplified complex topics. Claude, constrained by its knowledge cutoff, sometimes provided outdated information or declined to answer questions about recent developments altogether.
2. Overconfidence in incorrect information
Both assistants sometimes exhibited what some users have described as “confidence without competence,” delivering incorrect information with the same authoritative tone as accurate responses. This was particularly noticeable in specialized technical domains and required vigilant fact-checking on my part.
3. Limited creative originality
Creative writing tasks revealed limitations in both models. While they could generate serviceable content, neither consistently produced original or compelling creative work. Their outputs often felt derivative, drawing heavily on common patterns and tropes rather than demonstrating genuine creativity.
4. Problems with long-term memory
Long-term memory and conversation coherence became problematic in extended interactions with both assistants, but worse in ChatGPT. Despite their impressive context windows, both Claude and ChatGPT occasionally lost track of important details from earlier in our conversations, especially when those conversations spanned multiple days or sessions.
5. Unpredictable response times
Response time variability was frustrating with both models. While ChatGPT was generally faster, both assistants experienced unpredictable slowdowns during peak usage times. Claude’s more deliberate approach to generating responses meant that complex queries could sometimes take more than 60 seconds to process, an eternity when you’re trying to maintain a productive workflow.
6. Limitations in true reasoning
Finally, both models still struggle with tasks requiring genuine reasoning rather than pattern recognition. When presented with novel logical puzzles or asked to develop innovative solutions to complex problems, both assistants tended to fall back on familiar approaches rather than demonstrating the creative problem-solving abilities that characterize human intelligence.
How to make the most of both tools
If you’re investing time and potentially money in AI assistants like Claude and ChatGPT, you’ll want to maximize their value.
Drawing from my extensive testing, here are some practical tips to help you get the most out of both tools:
1. Play to their unique strengths
Understanding the distinct advantages of each assistant allows you to direct the right tasks to the right tool. By matching tasks to the assistant best equipped to handle them, you’ll achieve better results with less frustration.
Use Claude when you need thoughtful analysis of complex documents, nuanced ethical discussions, or naturally flowing written content. Turn to ChatGPT when you need real-time information, multimedia content generation, or voice interaction capabilities.
2. Master the art of effective prompting
The quality of output from both assistants depends significantly on how you structure your prompts. Be specific about your goals, provide necessary context, and communicate your expectations regarding tone, length, and format.
For complex tasks, break your requests into step-by-step instructions rather than asking for everything at once. When you receive a response that isn’t quite what you needed, refine your prompt rather than starting over. This iterative process helps the AI better understand your requirements.
3. Verify output
Neither Claude nor ChatGPT is infallible when it comes to factual accuracy. Develop a habit of verifying important information, especially for specialized knowledge domains or time-sensitive topics. This verification process becomes more efficient over time as you learn which types of information tend to be reliable versus which require additional scrutiny.
4. Leverage extended context windows
A context window in AI refers to the amount of text (in tokens) an AI model can “remember” and process at one time. Both assistants offer impressive context windows, but few users take full advantage of this capability. Rather than starting fresh in each conversation, build on previous interactions by referencing earlier discussions.
With Claude’s 200,000 token window, you can include entire documents, previous drafts, relevant research, and detailed instructions in one prompt. This comprehensive context leads to more precise and relevant responses than a series of disconnected interactions would produce.
5. Create personalized workflows
Develop custom workflows that integrate both assistants into your productivity system. The complementary capabilities of these tools make them powerful partners in complex workflows.
For example, you might use Claude to generate in-depth research and analysis, then use ChatGPT to transform those insights into visual presentations with accompanying images. Or use ChatGPT’s web browsing capability to gather current information before asking Claude to incorporate that data into a thoughtfully structured report.
6. Maintain conversation histories for important projects
Both Claude and ChatGPT allow you to save and organize conversations. Take advantage of this feature by maintaining dedicated conversation threads for significant ongoing projects. This approach preserves context and creates a searchable record of your AI-assisted work.
Final verdict: Which AI model should you choose between ChatGPT vs. Claude?
When it comes to creative projects, whether you’re writing, coding, or brainstorming, Claude is the clear winner. Its natural writing style, powerful Artifacts feature for real-time code visualization, and sharp analytical abilities make it perfect for developers, writers, and analysts who need depth and precision.
However, if you’re after a jack-of-all-trades AI tool, ChatGPT has the edge. Text generation is just the beginning: ChatGPT lets you generate images, search the web, automate tasks, and use specialized custom-built GPTs for specific needs, like academic research. Its diverse capabilities make it perfect for teams and individuals looking to explore the full range of AI functionalities.
You may want to use both tools if you have multiple AI needs. Claude could be your go-to for deep-dive writing and coding, while ChatGPT handles lighter tasks like quick searches, image generation, and voice interactions. This combination can help you maximize your workflow without hitting rate limits.
FAQs about Claude vs. ChatGPT
Claude vs. ChatGPT: Which AI model is better for writing?
Both Claude and ChatGPT shine in writing tasks but cater to different needs. ChatGPT is great for all-purpose writing, but Claude excels in creative writing.
Can I use both Claude and ChatGPT for free?
Yes, both AI models offer free versions, though they come with limitations such as reduced access to advanced features and functionality. If you want more power and additional features, paid plans are available.
Which AI is more accurate?
Even Claude and ChatGPT note that they are not always correct. But when it comes to accuracy, Claude generally provides more factually correct and structured responses, especially in tasks requiring in-depth analysis. ChatGPT, while conversational, might sometimes generate outdated or less precise information.
Which is better for coding, ChatGPT or Claude?
For coding tasks, Claude is the better choice. It has extensive training in programming languages, debugging, and code generation, making it a strong assistant for developers. ChatGPT, though powerful, doesn’t focus as much on coding.
Can Claude or ChatGPT remember past conversations?
Neither model retains long-term memory in their free versions. However, their premium offers improved context retention during a session, but once the conversation ends, it resets.
Are there any privacy concerns with using these AI models?
Both Claude and ChatGPT have data privacy policies in place. They don’t store individual conversations for long-term training, but sensitive or personal information should be used cautiously when interacting with any AI model.
Which AI model is best for business use?
ChatGPT is excellent for business-related tasks like marketing, content creation, image generation, customer support, and automation. Claude, on the other hand, is better suited for tasks requiring detailed analysis, research, and documentation, making it ideal for research teams and technical projects.
How often are these models updated?
Both Claude and ChatGPT are regularly updated. ChatGPT integrates advancements from newer models like GPT-4, while Claude continuously improves its AI capabilities, ensuring both remain competitive.
Disclaimer!
This publication, review, or article (“Content”) is based on our independent evaluation and is subjective, reflecting our opinions, which may differ from others’ perspectives or experiences. We do not guarantee the accuracy or completeness of the Content and disclaim responsibility for any errors or omissions it may contain.
The information provided is not investment advice and should not be treated as such, as products or services may change after publication. By engaging with our Content, you acknowledge its subjective nature and agree not to hold us liable for any losses or damages arising from your reliance on the information provided.
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Noticias
La inversión de inicio de América del Norte aumentó en el primer trimestre debido a OpenAi, pero la semilla y la etapa temprana cayeron
La inversión de inicio de América del Norte alcanzó los $ 82 mil millones en el primer trimestre, impulsada por el continuo entusiasmo en torno a la IA generativa.
La cuenta Q1 representaba el nivel de financiación trimestral más alto en tres años. Sin embargo, casi la mitad del total provino de un solo acuerdo récord: el financiamiento de $ 40 mil millones liderado por SoftBank para OpenAI anunciado el 31 de marzo.
Más allá de esa ronda gigante de la etapa tardía, la financiación disminuyó en otras categorías. La inversión en etapa temprana y de semillas disminuyó. Los recuentos de ronda informados también cayeron en las etapas.
Para perspectiva, trazamos totales de inversión, codificados por colores por el escenario, durante los últimos nueve cuartos a continuación.
También miramos rondas reportadas durante el mismo período.
Por supuesto, el capital de riesgo no se trata solo de poner dinero en nuevas empresas. Los inversores de inicio también esperan rendimientos cuando las empresas salen a público o se adquieren. Y con esta métrica, Q1 no estaba demasiado mal, con la adquisición de inicio más grande de la historia (la compra de Wiz de $ 32 mil millones planificada de Google) y el debut del mercado de CoreWeave en una OPI que recaudó $ 1.5 mil millones.
A continuación, echaremos un vistazo con más detalle en la inversión por etapa, así como profundizaremos en la actividad de M&A e IPO para el trimestre.
Tabla de contenido
Crecimiento de la etapa y tecnología tardía
Comenzaremos con la etapa tardía, ya que ahí es donde va la mayor parte del dinero.
Para el primer trimestre, los inversores pusieron $ 66.4 mil millones en acuerdos de etapa tardía y de crecimiento para empresas estadounidenses y canadienses, según los datos de CrunchBase. Eso es aproximadamente los niveles del año anterior cuádruple, y más del 50% desde el trimestre anterior, como se registra a continuación.
Como señalamos anteriormente, la mayor parte del total en etapa tardía provino del financiamiento récord de $ 40 mil millones de OpenAi. Sin embargo, hubo otras grandes rondas en la mezcla que ayudaron a aumentar los totales.
El segundo recaudador de fondos más grande en el primer trimestre fue el rival de Operai Anthrope, que recogió $ 3.5 mil millones en una Serie E de marzo, así como $ 1 mil millones en un financiamiento de enero respaldado por Google.
Un poco más atrás estaba la startup de realidad aumentada Infinite Reality, que bloqueó un financiamiento de $ 3 mil millones en enero con una valoración de $ 12.25 mil millones.
Etapa temprana
Si bien los dólares en etapa tardía fluyeron, la inversión en etapa inicial se debilitó en el primer trimestre, ya que los inversores pusieron un estimado de $ 12.4 mil millones para trabajar en la Serie A y la Serie B.
Como se registra a continuación, Q1 marca el punto más bajo en cinco trimestres tanto para la inversión total como para los recuentos de rondas.
A pesar de que la financiación general disminuyó, sin embargo, vimos algunas rondas excepcionalmente grandes en la mezcla.
Apptronik, un desarrollador de robots humanoides de propósito general, recogió la ronda de etapa temprana más grande del trimestre, una serie de febrero de $ 403 millones A.
El siguiente fue juntos IA, un proveedor de infraestructura para desarrollar modelos de IA que recaudó una Serie B de $ 305, seguido de Kardigan, un desarrollador de drogas cardiovascular que obtuvo $ 300 millones en fondos de la Serie A.
Semilla
El primer trimestre tampoco fue un período particularmente robusto para la inversión de semillas.
Los inversores pusieron a trabajar un total de $ 3.2 mil millones a través de 1,016 rondas de semillas y pre-semillas reportadas en el primer trimestre. Esa es, con mucho, la cuenta más baja en años, por los dólares invertidos y los recuentos de rondas totales.
Para una perspectiva más cercana, trazamos la inversión de semillas y los recuentos de acuerdos durante los últimos cinco trimestres a continuación.
Dado que no es raro que los acuerdos de semillas se informen semanas o meses después de que cierren, esperaremos que los totales Q1 se eleven un poco con el tiempo. Sin embargo, es poco probable que esto altere la historia más amplia de la menor inversión en esta etapa.
Aproximadamente un cuarto de todos los fondos iniciales para el trimestre provienen de solo dos acuerdos: Lila Sciences, desarrolladora de una plataforma impulsada por IA para la investigación científica, obtuvo un financiamiento de semillas de $ 200 millones en marzo; Y SIMBLETER, una startup de infraestructura de blockchain, obtuvo $ 50 millones en febrero.
AI
Con gran parte de la financiación total de la empresa yendo a la inteligencia artificial, también echamos un vistazo a cómo se desempeñó este espacio durante el trimestre.
No es sorprendente, dado que el acuerdo gigante de OpenAi, fue un gran trimestre para la financiación relacionada con la IA, con más de $ 54 mil millones al espacio. Como puede ver en la tabla a continuación, es para el total más grande que hemos visto.
Salidas
El primer trimestre también fue un período relativamente fuerte para las salidas.
Como hemos narrado anteriormente, Q1 nos trajo un acuerdo de fusiones y adquisiciones que establecen récords y una de las OPI tecnológicas más grandes en algún tiempo. En total, parecía evidencia convincente de que el ambiente de salida se está calentando. (Aunque el mercado empinado de esta semana se desliza después de las fuertes gravámenes arancelarios podría detener ese impulso).
MAMÁ
La actividad de adquisición fue particularmente fuerte en Q1.
En el transcurso del trimestre, los adquirentes anunciaron planes para comprar al menos 10 compañías estadounidenses respaldadas por la empresa por $ 1 mil millones o más, según la junta de salidas de mil millones de dólares de Crunchbase. Ese es el total más alto en los últimos tres años.
Además, la alineación de M&A del primer trimestre incluye un acuerdo récord: la compra planificada de $ 32 mil millones de Google de CyberseCurity Unicorn Wiz, anunciada en marzo. Si se consume, el acuerdo sería la adquisición más grande de una startup privada respaldada por la empresa.
A continuación, reunimos una lista de las 10 adquisiciones anunciadas más grandes del trimestre.
OPIE
El Startup IPO Market no estaba especialmente ocupado en el último trimestre. Sin embargo, vimos un debut significativo del mercado con la oferta de CoreWeave del proveedor de infraestructura de AI Cloud a fines de marzo. Después de varios días de negociación de arriba y abajo, la compañía de Nueva Jersey tenía una capitalización de mercado reciente de alrededor de $ 22 mil millones.
Otras compañías que debutaron durante el trimestre incluyeron a Metsera, un desarrollador de medicamentos para la obesidad y las enfermedades metabólicas, y la terapéutica del laberinto, una startup de medicina de precisión.
Un cuarto inusual
Por lo general, cuando miramos hacia atrás en una cuarta parte, es bastante fácil encontrar un adjetivo o dos que resume el clima de inversión de inicio. Las cosas están arriba, o están bajas. El entorno de salida se ve fuerte, o parece debilitarse.
Pero el Q1 2025 es un período inusual para clasificar. Si bien la inversión total ha aumentado, eso se debe completamente a un solo acuerdo anunciado el último día del trimestre. En la etapa inicial y temprana, los titulares de fondos apuntan más al ascensor de pesimismo de los inversores.
En cuanto a la actividad de salida, estábamos comenzando a caer más profundamente en una narración alrededor del regreso de las grandes salidas. Con las recientes presentaciones de IPO de FinTechs Klarna y Circle, el escenario también parecía establecido en el Q2.
Pero ahora, con los principales índices en camino por su mayor contracción en años a raíz de los decretos de la tarifa de la administración Trump, el entorno del mercado parece mucho menos acogedor.
Tal vez, cuando miremos hacia atrás en este período, no se destacará como el comienzo o el final de un ciclo, sino un fragmento único en el tiempo, así como una tubería de trato que refleja tanto la precaución aumentada como el FOMO persistente.
Metodología
Los datos contenidos en este informe provienen directamente de CrunchBase, y se basan en datos informados. Los datos informados son al 2 de abril de 2025.
Tenga en cuenta que los retrasos de datos son más pronunciados en las primeras etapas de la actividad de riesgo, y las cantidades de financiación de semillas aumentan significativamente después de finales de un trimestre/año.
Tenga en cuenta que todos los valores de financiación se dan en dólares estadounidenses a menos que se indique lo contrario. Crunchbase convierte las monedas extranjeras en dólares estadounidenses a la tasa spot prevaleciente a partir de las rondas de financiación de la fecha, las adquisiciones, las OPI y otros eventos financieros. Incluso si esos eventos se agregaron a Crunchbase mucho después de que se anunciara el evento, las transacciones en moneda extranjera se convierten al precio histórico.
Glosario de términos de financiación
La semilla y el ángel consisten en rondas de semillas, semillas y ángeles. Crunchbase también incluye rondas de riesgo de series desconocidas, crowdfunding de capital y notas convertibles en $ 3 millones (USD o equivalente de USD con convertido) o menos.
La etapa temprana consiste en rondas de la Serie A y Serie B, así como otros tipos de rondas. Crunchbase incluye rondas de riesgo de series desconocidas, empresas corporativas y otras rondas superiores a $ 3 millones, y aquellos menores o igual a $ 15 millones.
La etapa tardía consta de las rondas de aventuras de la Serie C, Serie D, Serie E y Letred Lettered después de la “serie [Letter]”Convención de nombres. También se incluyen rondas de riesgo de series desconocidas, empresas corporativas y otras rondas superiores a $ 15 millones. Las rondas corporativas solo se incluyen si una empresa ha recaudado un financiamiento de capital en Seed a través de una ronda de financiación de la serie de empresas.
El crecimiento de la tecnología es una ronda de capital privada planteada por una compañía que previamente ha elevado una ronda de “empresa”. (Entonces, básicamente, cualquier ronda de las etapas anteriormente definidas).
Lista relacionada de Crunchbase Pro:
Lectura relacionada:
Ilustración: Dom Guzman
Manténgase al día con rondas de financiación recientes, adquisiciones y más con el Crunchbase Daily.
On Monday, December 9, 2024, OpenAI officially launched Sora, its AI-powered video generator, for ChatGPT Pro and Plus subscribers.
I’ll be honest: I’ve always been skeptical of AI video generators. They make bold promises of stunning visuals, effortless editing, and limitless creativity, but more often than not, they fall short. Either the videos look robotic, the transitions are clunky, or the output feels…well, AI-generated.
So when OpenAI dropped Sora, their highly anticipated AI video tool, I had to see for myself — although it has taken nearly three months for me to do this. Could it truly turn simple text prompts into seamless, high-quality videos? Or was this just another case of overhyped AI?
I spent hours testing Sora, pushing it to its limits with different prompts, analyzing the video quality, and comparing it to existing AI video tools. If you’re wondering if Sora is the real deal or just another AI experiment, you’re in the right place.
Here’s my honest review.
TL;DR: Key takeaways from this review
- OpenAI’s Sora Video Generator offers AI-powered video creation with unique tools like Remix, Re-cut, and Storyboard.
- The interface is intuitive, making it accessible for both beginners and professionals.
- Video output quality is high, but customization options have some limitations.
- Pricing is competitive, especially with ChatGPT Plus and Pro plans.
- It’s a strong tool for quick video generation but may not replace professional-grade editing software yet.
What is Sora?
| Platform | Sora |
| Developer | OpenAI |
| Year established | December 2024 |
| Best for | AI-powered video creation, quick content generation, and creative storytelling |
| Key features | Text-to-video generation, Remix, Re-cut, Storyboard tools, high-quality visuals |
| Customization options | Limited but improving; offers scene adjustments and minor edits |
| Output quality | High-resolution, realistic videos with smooth transitions |
| Pricing | Available with ChatGPT Plus ($20) & Pro ($200) plans |
| Mobile App | Not available yet |
Sora is OpenAI’s cutting-edge AI-powered text-to-video generator designed to create high-quality, photorealistic videos from simple text prompts. Unlike earlier AI video tools that struggled with realism and fluidity, Sora aims to revolutionize the space by generating smooth, lifelike animations with complex backgrounds, multiple characters, and natural movements—all within a single video.
At its core, Sora takes text input and transforms it into dynamic video content using deep learning models trained on vast amounts of visual and motion data. The tool can generate videos up to 20 seconds long, ensuring consistency in characters, environments, and even lighting across different scenes.
While Sora is still not widely available to the public (unavailable in the UK and Europe), the AI-powered video generator has impressed industry experts with the ability to produce high-quality, cinematic videos, setting a new benchmark in AI-powered video generation.
How does Sora work?
Sora operates using a combination of deep learning, computer vision, and natural language processing (NLP) to convert text prompts into fully animated video sequences.
The process can be broken down into several key steps:
1. Understanding the prompt
When you input a text description (e.g., “A futuristic city at sunset with flying cars and neon signs”), Sora’s AI model first breaks it down into key components:
- Objects and characters: Identifying subjects, such as people, animals, or objects.
- Scenes and backgrounds: Recognizing the environment (urban, natural, fantasy, etc.).
- Movements and interactions: Defining how elements should behave in motion.
2. Generating visual and motion data
Unlike traditional video editing, where footage is pieced together from existing clips, Sora creates video content from scratch using its deep learning models. These models have been trained on massive datasets of videos and animations to learn realistic motion patterns and visual aesthetics.
3. Rendering the video
Sora applies its advanced diffusion models to transform raw AI-generated visuals into a polished, high-quality video. This includes:
- Generating frame-by-frame continuity to ensure smooth transitions.
- Adding realistic lighting, shadows, and textures for photorealism.
- Ensuring character consistency so that faces, clothing, and objects remain the same across different frames.
4. Editing and enhancing
Once the initial video is created, Sora allows users to refine it using tools like:
- Remix: Tweaks specific parts of the video while keeping the core structure intact.
- Re-cut: Adjusts pacing and transitions for a smoother flow.
- Storyboard: Helps structure longer narratives by generating multiple shots in sequence.
Why I tested OpenAI’s Sora
The hype surrounding OpenAI’s Sora was impossible to ignore. AI-driven video generation has been evolving rapidly, and with each new tool comes the promise of effortless, high-quality content creation. But does Sora live up to the buzz, or is it just another AI experiment that looks great in demos but falls short in real-world applications?
I wanted to find out for myself.
What was my goal?
I wasn’t just here for a quick test; I wanted to push Sora to its limits and see if it could truly revolutionize video creation for content creators, marketers, and video professionals. My focus was on three key areas:
- Ease of use: Can anyone, regardless of skill level, create impressive videos with minimal effort?
- Features and capabilities: Does Sora offer enough creative control and flexibility for professionals?
- Real-world application: Is this just a fun AI experiment, or can businesses and creators genuinely rely on it for content production?
With these questions in mind, I dove into Sora, experimenting with different prompts, testing its storytelling abilities, and analyzing the final output.
Here’s what I discovered.
My first impressions: What makes Sora special?
Sora isn’t the first AI-powered video generator to be invented. Heavyweights like Runway Gen-3 and Kling have already set high standards, proving that AI-generated videos can be more than just stitched-together animations. But OpenAI’s approach with Sora isn’t about reinventing the wheel, it’s about refining it.
Instead of throwing another complex tool into the mix, OpenAI seems to have studied what works (and what doesn’t) in AI video generation. The result? A more intuitive, user-friendly experience is housed at sora.com.
For someone like me, who’s spent years wrestling with traditional video editing software, Sora was a breath of fresh air. No need to navigate endless menus or tweak a hundred settings just to get a simple video. Sora does the heavy lifting for you, making high-quality AI video generation accessible to both beginners and professionals alike.
Getting started: Onboarding and user experience.
Jumping into a new AI tool can sometimes feel daunting, but Sora’s onboarding process is refreshingly simple. From signing up to generating your first video, the entire experience is designed to be seamless, even if you’ve never used an AI video generator before.
Sign-up process and initial setup
Registering for Sora is straightforward. If you’re already a ChatGPT Plus or Pro subscriber, you automatically get access to Sora—no extra fees, no separate sign-ups. For new users, the process involves:
- Logging into your OpenAI account (or creating one if you don’t have it).
- Subscribing to ChatGPT Plus or Pro if you haven’t already.
- Navigating to Sora.com and launching the tool directly.
The entire setup takes just a few minutes. There’s no complicated installation or software download, everything runs in the cloud.
How easy is it to use Sora?
Once you’re inside, getting started is as simple as typing a text prompt. Here’s a breakdown of how to use it:
- Enter a prompt. Describe the video you want (e.g., “A futuristic African city skyline at sunset, with flying cars zooming by”).
- Choose video length. Sora can generate clips up to 20 seconds long.
- Select style preferences. Options like cinematic, animation, or realistic can help fine-tune the aesthetic.
- Generate. Hit the create button and watch Sora bring your vision to life in seconds.
- Edit and refine: Use tools like Remix, Re-cut, and Storyboard (more details on these later in this review) to tweak your video further.
Unlike traditional video editing, you don’t need prior experience or have to adjust lighting, frame rates, or manual transitions.
User interface: Intuitive or overwhelming?
Sora’s UI is refreshingly simple. It’s designed to eliminate friction, allowing users to focus on creativity rather than complexity.
- Clean layout. The dashboard is minimalist and well-organized, making it easy to navigate.
- Preset options. Ready-to-use templates help beginners get started quickly.
- Drag-and-drop editing. You can make adjustments without needing professional editing skills.
For beginners, it’s easy to dive in without feeling lost. For professionals, Sora’s advanced tools provide enough flexibility to refine and customize videos.
Features of Sora video generator
Sora is packed with powerful tools designed to streamline video creation, enhance storytelling, and maximize creative control.
Here’s what makes Sora stand out:
1. Remix – Modify without starting over
Tired of regenerating an entire video just to tweak one detail?
Remix lets you replace, remove, or reimagine specific elements without having to start from scratch. Whether it’s swapping a character’s outfit, adjusting a background element, or refining details, Remix gives you precise control over edits.
2. Re-cut – Seamless scene adjustments
Editing AI-generated videos can be tricky, but Re-cut makes it easy to isolate and extend keyframes for a smoother flow. If a scene feels too abrupt or needs an extra second of motion, Re-cut helps you fine-tune transitions without distorting the original video.
3. Storyboard — A built-in video sequencer
Think of Storyboard as a simplified version of Adobe Premiere Pro but without the steep learning curve.
It lets users:
- Arrange clips in a timeline interface.
- Specify inputs for each frame.
- Create cohesive narratives within the app.
This feature is especially useful for marketers, educators, and storytellers who want to structure their videos without using external editing software.
4. Loop – Generate seamless repeating clips
Need a smooth, endless animation?
Loop automatically trims and adjusts videos to create seamless repeating sequences, perfect for GIFs, animated backgrounds, and social media posts.
5. Blend – Merge two videos
Blend takes AI video generation a step further by seamlessly merging two clips into a single, cohesive video. If you’re looking to transition between scenes or combine different AI-generated elements, this tool ensures the visuals flow naturally.
6. Style Presets – Instantly apply aesthetic filters
Sora makes stylizing videos effortless with built-in presets that add a unique artistic touch:
- Film Noir – For a dramatic, cinematic look.
- Papercraft – Stop-motion-style animation.
- Abstract Visuals – Great for experimental, artsy content.
These presets make it easier to maintain a consistent theme across multiple videos.
7. Community feeds – Get inspired and share ideas
Sora integrates a social element through its Featured and Recent Feeds, where users can:
- Browse AI-generated videos for inspiration.
- View the exact prompts used to create each clip.
- Remix and modify community-shared content
This makes learning and experimenting with AI video generation more accessible, as users can build on existing ideas instead of starting from scratch.
Sora pricing
Sora is bundled with ChatGPT subscriptions:
| Plans | Monthly cost | Video generations | Resolution | Duration | Watermark |
| ChatGPT Plus | $20 | Up to 50 | Up to 720p | 5 sec | Yes |
| ChatGPT Pro | $200 | Up to 500 | Up to 1080p | 20 sec | No |
Note: No additional cost if you’re a ChatGPT Plus/Pro user.
My hands-on testing experience
I tested Sora for creating a promotional video, a short social media clip, and a tutorial.
Here’s what I liked and didn’t like:
What I liked about Sora
1. High-quality video output
Sora produces stunning, photorealistic videos that capture natural motion, lighting, and scene composition. Unlike many AI generators that struggle with consistency, Sora maintains visual coherence most of the time.
2. Intuitive controls for beginners and professionals
Sora is designed to be accessible for first-time users while still offering enough depth for experienced creators. The drag-and-drop interface, clear tooltips, and well-organized settings make it easy to get started without a steep learning curve.
3. Rapid prototyping
The speed of video generation, particularly at lower resolutions, makes Sora perfect for quick iterations. A 480p video takes less than 20 seconds to generate, allowing creators to experiment with different ideas, styles, and scripts rapidly before finalizing their projects.
4. Natural Language Processing capabilities
Sora understands context, sentiment, and meaning in text prompts, enabling it to create more accurate visual representations. If you describe a dynamic action scene or a serene landscape, Sora interprets the details effectively, leading to better prompt accuracy than many competitors.
5. Customization options
Sora allows users to tweak visual styles, animation effects, color schemes, and typography, ensuring that every video aligns with their unique branding or creative vision. This flexibility makes it a powerful tool for content creators and marketers alike.
6. Collaboration tools
For teams working on marketing, educational content, or creative storytelling, Sora includes real-time editing, version control, and commenting features. This streamlines workflow and ensures seamless collaboration between multiple stakeholders.
7. Accessibility and ease of use
Sora’s user-friendly interface makes video creation accessible to both professionals and casual users. By leveraging text prompts instead of complex animation controls, it removes technical barriers, making high-quality video creation more inclusive and effortless.
What I didn’t like about Sora video generator
1. Limited customization for complex edits
While Sora’s editing tools are impressive, they still lack the depth of professional video software like Premiere Pro or After Effects. Fine-tuning intricate transitions or advanced visual effects isn’t possible as of yet.
2. Watermark on videos
If you’re using the Plus plan of Sora, expect a watermark on all generated videos. Only Pro subscribers get clean, unbranded exports, which might be a dealbreaker for users who want professional-looking output without a subscription.
3. Ethical concerns
The rise of AI-generated videos raises ethical issues, including misinformation, deepfakes, and digital trust. OpenAI has implemented transparency measures like watermarks, but as the tech evolves, ethical oversight will be crucial.
4. Unrealistic physics
Sora somewhat struggles with physics consistency. Objects may move unnaturally, disappear, or fail to interact realistically. For instance, characters might hold an item in one scene, only for it to vanish in the next, breaking immersion.
5. High cost
Sora’s Pro plan costs $200 per month, which might be expensive for casual users or small businesses. Competing AI tools like Fliki or Runway offer cheaper or pay-as-you-go alternatives, making them more budget-friendly options.
6. Regional restrictions
Sora is not yet available in several major markets, including most of Europe and the UK. This limits its adoption, making it inaccessible to a significant portion of global content creators.
7. No manual motion control
While Sora excels at natural movement, there’s no direct way to animate characters with specific actions beyond what the AI interprets. This makes precision storytelling a bit tricky.
How does Sora compare to other AI video tools?
Sora stands out in the AI video space, but how does it stack up against competitors like Runway, Fliki, Pika Labs, and Synthesia?
1. Seamless OpenAI integration
Unlike other AI video tools, Sora is natively integrated into OpenAI’s ecosystem, ensuring smoother workflow for users familiar with ChatGPT and DALL·E. This makes it an attractive choice for those already using OpenAI’s products.
2. Faster video generation
Sora generates high-quality videos in seconds, outperforming most AI video tools in speed. For example, a 480p video takes under 20 seconds, whereas competitors like Runway might take several minutes to produce similar results.
3. Advanced AI scene understanding
Sora understands prompts with impressive accuracy, generating complex scenes with multiple characters, natural motion, and consistent environments. Many other AI tools struggle with object permanence and realistic movement, but Sora handles these aspects better than most.
4. Editing flexibility: Room for improvement
While Sora is great for AI-powered automation, it lacks the manual editing precision that exists in tools like Runway and Pika Labs. It’s ideal for quick prototyping but not for fine-tuned professional edits.
5. Video length: A major limitation
Compared to Runway and Fliki, which offer longer videos (up to several minutes), Sora is limited to 20 seconds per clip (The Plus plan, making it less suitable for long-form content creators.
6. Cost considerations
Sora’s Pro plan at $200 per month is significantly higher than Fliki or Pika Labs, which offer lower-cost subscriptions or pay-as-you-go models. Budget-conscious users may find better value elsewhere.
Verdict
Sora is great for AI-powered video creation, excelling in speed, AI automation, and scene comprehension. However, its editing constraints, short video length, and high price tag make it less appealing for professionals needing full-scale video production tools. If you need quick, high-quality AI videos, Sora is a top choice, but for detailed editing and longer videos, competitors like Runway or Pika Labs may be better suited.
Who should use the Sora video generator?
Sora is designed for anyone looking to create AI-generated videos quickly and efficiently. Content creator, marketer, educator, or business owner, Sora provides a fast and intuitive way to generate engaging video content.
1. Content creators
YouTubers, TikTok influencers, and social media creators can use Sora to produce visually stunning videos without expensive equipment or editing software. Its AI-powered automation allows for rapid prototyping and creative experimentation, making it ideal for short-form content.
2. Marketers
Brands and advertisers can leverage Sora to create eye-catching promotional videos, social media ads, and explainer content. The ability to generate high-quality clips in seconds makes it a great tool for A/B testing and campaign iterations.
3. Educators
Teachers and online course creators can use Sora to produce interactive and engaging video lessons. With text-to-video generation and storyboard sequencing, educators can simplify complex topics into digestible visual content.
4. Businesses
Companies can streamline video production for product demos, training materials, and corporate presentations. Sora’s AI-driven tools allow businesses to generate professional-looking videos without needing a dedicated video editing team.
Potential use cases for Sora
Sora’s AI-driven video generation opens up a variety of creative and professional applications. Here are some key areas where it shines:
1. Marketing and branding
- Generate sleek, professional-looking video ads in seconds.
- Produce branded short clips for TikTok, Instagram, and YouTube without hiring a production team.
- Showcase products with smooth transitions and AI-enhanced visuals.
2. Education and training
- Develop engaging video lessons or explainer videos for online courses.
- Animate complex concepts to improve understanding and retention.
- Create interactive training videos for employee onboarding and corporate learning.
3. Content creation
- Create shot clips for entertainment or artistic expression.
- Remix existing content to provide fresh perspectives and variations.
- Produce cinematic storytelling clips without the need for professional filming equipment.
4. Gaming and virtual worlds
- Design cinematic cutscenes for game trailers or promotional content.
- Generate concept animations for characters, environments, and storytelling elements.
- Quickly iterate game design ideas with AI-generated previews.
How to optimally prompt Sora for the best results
Crafting effective prompts is the key to unlocking Sora’s full potential. Unlike traditional video editing tools, Sora relies entirely on natural language inputs to generate visually stunning outputs.
To get the most out of this AI video generator, follow these expert-level prompting techniques:
1. Be specific and descriptive
The more detailed and vivid your prompt, the better Sora will understand and generate your video. AI models rely on clarity. So, instead of a generic request, be explicit about the scene, style, mood, and action.
Instead of just writing “A futuristic city at night,” good would be “A cyberpunk city at night, neon signs glowing, light rain falling, pedestrians in futuristic outfits walking past high-tech billboards, a robotic taxi driving down the street, cinematic and immersive lighting.”
2. Define the motion and camera style
Since Sora generates full scenes with motion, describing the camera angles and movements can greatly impact the final video. It could be as simple as adding “A sweeping aerial shot over…” to your prompt.
3. Use clear action commands
Sora can generate dynamic, multi-step actions if you specify them. Instead of just describing a setting, explain what’s happening within the scene.
For example, rather than say “A scientist working in a lab,” write “A scientist in a white lab coat mixes colorful chemicals in a glass beaker, which starts bubbling and changes color from blue to red. She smiles in excitement as a robot assistant hands her another beaker. The scene is bright and futuristic, shot in a modern laboratory.”
4. Set the lighting, atmosphere, and depth
Lighting dramatically affects how a scene feels. Whether it’s soft golden hour sunlight, neon cyberpunk glow, or moody shadows, describing lighting and depth enhances realism.
5. Avoid overloading
While detail is important, don’t overstuff your prompt with too many conflicting ideas. AI works best when it has a clear structure to follow.
6. Tweak
Don’t be afraid to tweak and refine your prompt if the output isn’t perfect. Try:
- Adjusting wording for better clarity.
- Adding more visual detail if the scene is too simple.
- Removing unnecessary elements if the video feels cluttered.
Final verdict: Is the Sora video generator worth the hype?
Sora is a groundbreaking AI video generator with immense potential, but it’s not without its limitations. It excels in speed, automation, and ease of use, making it ideal for creators who want to generate high-quality videos with minimal effort. However, compared to traditional video editing software, it still lacks advanced customization, precise frame control, and detailed post-production tools.
Sora is cool but it’s not a full replacement for professional video editing tools…yet. If OpenAI continues refining it, Sora could redefine how we create video content in the future.
I’ll rate it a 7.5/10.
FAQs for OpenAI’s Sora video generator
Is Sora free to use?
No. Sora is included in OpenAI’s ChatGPT Plus and Pro subscriptions, $20 and $200 respectively.
What types of videos can Sora generate?
Sora can create 5 – 20 seconds of promotional videos, animations, explainer videos, and more, making it useful for content creators, marketers, and educators.
Can OpenAI Sora produce video and sound at the same time?
Currently, Sora does not generate audio alongside video. Users must add voiceovers, background music, or sound effects separately in post-production.
Can you make a full-feature film using Sora?
No, Sora is not yet capable of producing full-length films. While it can generate short, high-quality clips, a complete movie requires complex storytelling, scene transitions, and extensive post-production work.
What are the main drawbacks of using Sora?
The main limitations include limited video duration (capped at 20 seconds), lack of advanced manual editing tools found in professional software, and expensive subscription costs.
Can I use Sora for professional video production?
Yes, but it depends on the project’s requirements. Sora is best suited for quick content generation, marketing materials, and prototyping, rather than fully polished cinematic productions.
Does Sora support different aspect ratios and resolutions?
Yes, but resolution and video length depend on your subscription plan. ChatGPT Pro users can generate videos up to 1080p quality.
How will OpenAI Sora change the traditional video editing workflow?
Sora streamlines video creation by eliminating the need for manual editing and complex animation work. While it won’t replace professional editors, it offers a faster, AI-driven approach to generating high-quality visuals.
Conclusion
Sora is an impressive AI-powered video generator that simplifies content creation. While it excels in speed and automation, it still has limitations in manual editing, audio integration, and video length. As OpenAI continues improving the tool, Sora has the potential to reshape the future of video production.
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