Operai recauda $ 40 mil millones

Esta semana, Openai finalizó su ronda de recaudación de fondos de $ 40 mil millones, dando a la compañía una valoración posterior al dinero de $ 300 mil millones. Es la ronda de financiamiento individual más grande jamás completada por una empresa privada. La ronda está dirigida por SoftBank, que cometió $ 10 mil millones por adelantado. Los $ 30 mil millones restantes dependen de la conversión de OpenAI de una perfil limitada a una empresa totalmente con fines de lucro.

Si bien $ 40 mil millones es una cantidad significativa de dinero, lo que también vale la pena señalar es que Operai ya ha declarado públicamente que espera perder mil millones de dólares en los próximos años y no proyecta rentabilidad hasta al menos 2029. Esto muestra cuánta creencia y tolerancia al riesgo, los inversores actualmente tienen para la inteligencia artificial (AI). Pero también refleja un nivel de irracionalidad del mercado; En cualquier otro sector, una compañía que proyecta pérdidas multimillonarias para la próxima media década probablemente no tener a los inversores entusiasmados de sacar sus chequeras.

Además de eso, lo que hace que este acuerdo sea aún más interesante es que SoftBank está estructurando el acuerdo con el financiamiento de la deuda, que es una vez más otra ruta interesante para ir con una compañía que aún no ha obtenido ganancias ni proyectando obtener ganancias durante varios años más, y eso es si todo va de acuerdo con sus planes y proyecciones.

Aunque Operai definitivamente está celebrando este trato, creo que más personas deberían encontrarlo preocupante. Claramente, existe una desconexión entre el gasto corporativo y la demanda real del consumidor de productos de IA. Lo he dicho antes en otras piezas de escritura, pero lo diré nuevamente: la demanda del usuario de IA no mantiene el ritmo de la inversión empresarial y la expansión en la IA. En todo caso, el público en general aún tiene que comprender o abrazar completamente los casos de uso de la IA en su vida diaria. Creo que necesitaremos una campaña educativa a gran escala para cerrar esta brecha si las empresas esperan ver algún retorno significativo de sus inversiones de IA.

Los expertos en IA sobreestiman el uso público

El Centro de Investigación de Pew publicó recientemente un informe titulado “Cómo los expertos en el público y la IA de EE. UU. Ven la inteligencia artificial”, y para aquellos de nosotros que hemos estado viendo este espacio durante un tiempo, y para aquellos que están de acuerdo con esos últimos párrafos sobre las tendencias de inversión en la AI que se volvieron problemáticas, los hallazgos no fueron seguidores.

Una estadística que saltó del informe fue que el 79% de los expertos en IA creen que el público en general interactúa con IA varias veces al día, mientras que solo el 27% del público informó haberlo hecho. Esto nuevamente resalta la idea de que los expertos en IA tienden a sobreestimar la familiaridad, la comodidad y el uso del público de la IA.

En muchos casos, la persona promedio todavía usa IA de manera relativamente primitiva, haciendo preguntas básicas a un chatbot como solía hacerlo con Google (NASDAQ: Googl). Mientras tanto, los tecnólogos que construyen y capacitan estos sistemas están rodeados de otros como ellos, operando en una cámara de eco de los primeros usuarios. El resultado es una percepción sesgada de cuán ampliamente adoptado y sofisticado es el uso de AI.

Tome otra estadística del informe: mientras que el 98% de los expertos en IA dicen que han usado un chatbot, solo el 33% del informe público en general es el mismo, de nuevo destacando la división entre los tecnólogos y el público en general.

Otra conclusión importante del informe de Pew fue la divergencia en las preocupaciones entre los expertos y el público en general. El principal temor del público en general es que AI se usará para hacerse pasar por personas. Para los expertos, la mayor preocupación es
Información errónea e inexactitud en salidas generadas por IA.

Sin embargo, la mayor diferencia en los niveles de preocupación se reduce a la pérdida de empleo. Solo el 25% de los expertos dijeron que estaban “muy preocupados” acerca de que la IA tomara empleos, mientras que el 56% del público informó que se sentía así. Una vez más, creo que esto se reduce a la exposición y la experiencia. Los expertos tienen una sensación mucho mejor de lo que la IA puede y no puede hacer, por lo que saben qué trabajos están en riesgo de manera realista y cuáles no. Mientras tanto, la persona promedio está siendo bombardeada con titulares sobre la IA que elimina millones de empleos durante la noche y hace que los trabajadores humanos sean redundantes.

Lo que este informe revela más que nada es que, como la mayoría de las piezas de tecnología, si no está utilizando las herramientas o la tecnología regularmente, es difícil separar la exageración de la realidad, y puede ser difícil determinar lo que el verdadero pulso de la gente está fuera de la industria.

AI Overbuild? Microsoft detiene la expansión del centro de datos

Microsoft (NASDAQ: MSFT) ha detenido o retrasado las negociaciones para los nuevos desarrollos de centros de datos en varios lugares, incluidos Indonesia, el Reino Unido, Australia, Illinois, Dakota del Norte y Wisconsin.

Aunque la compañía no entró en gran detalle, un portavoz ofreció una explicación calderera, diciendo: “Planeamos que la capacidad del centro de datos necesita con años de anticipación para garantizar que tengamos una infraestructura suficiente en los lugares correctos”.

Sin embargo, como observador, es difícil no buscar un significado y motivos más profundos en la decisión.

Algunos analistas, como TD Cowen, especulan que estos retrocesos provienen de un desajuste entre la demanda de IA proyectada y real.

“Continuamos creyendo en las cancelaciones de arrendamiento y las apuntantes de la capacidad del Centro de datos en exceso de oferta en relación con su pronóstico de demanda actual”, escribieron en una nota reciente. Otros especulan que la decisión proviene de retrasos en la construcción en los centros de datos que no habrían alineado con los plazos de lanzamiento de Microsoft.

Probablemente haya verdad en ambos lados. Probablemente haya algunos retrasos en la construcción que pueden haber hecho que algunos sitios no sean inviables. Pero al mismo tiempo, si la demanda de servicios de IA llegaba a los niveles que las empresas proyectaban hace solo un año, estos centros de datos aún estarían a toda velocidad. La reducción de la ampliación da mucho crédito a la idea de que el mercado está ajustando sus expectativas en términos de IA.

Joe Tsai de Alibaba Group (NASDAQ: BABAF) dijo recientemente que el auge del centro de datos podría ser una burbuja porque los nuevos proyectos parecen estar superando la demanda de servicios de IA. Si más empresas de IA siguen el ejemplo de Microsoft y comienzan a retirar la infraestructura, podría marcar un punto de inflexión para la industria de la IA.

Para que la inteligencia artificial (IA) trabaje en la ley y prospere frente a los crecientes desafíos, necesita integrar un sistema de cadena de bloques empresarial que garantice la calidad y la propiedad de la entrada de datos, lo que permite mantener los datos seguros al tiempo que garantiza la inmutabilidad de los datos. Echa un vistazo a la cobertura de Coingeek sobre esta tecnología emergente para aprender más Por qué Enterprise Blockchain será la columna vertebral de AI.

Reloj: blockchain & ai desbloqueo posibilidades

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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

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

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.

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

Claude 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

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!

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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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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).

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Ilustración: Dom Guzman