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Will Sam Altman always win the OpenAI board fight in an AI agent simulation?
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A year ago today, Sam Altman returned to OpenAI after being fired just five days earlier. What really happened in the boardroom? Fable, a game and AI simulation company, built its AI Sim Francisco “war game” to find out why the behind closed doors board fight turned out the way it did.
It feels a bit weird to simulate a real-life event in this way, but Fable CEO Edward Saatchi is interested in whether a different set of decisions could have led to a different outcome for this company at the center of the generative AI revolution.
The simulation pits different board members and personalities against each other in a “multi-agent competition,” where each AI player is trying to come out on top. Here’s the war game research paper being released today that came from this experiment.
The SIM-1 framework for AI decision making is basically a simulation of the five days from when Sam Altman was removed as CEO of OpenAI to when he returned.
“Simulations offer a completely new way to explore AI decision making in rich environments — including in war game situations where predicting possible outcomes can be invaluable,” said Joshua Johnson, CEO of Tree, an AI startup which partnered with Fable on this research paper, said in a statement. “These aren’t simply chatbots. These AIs need to sleep and eat, and to balance many different physical, mental and emotional goals.”
SIM-1, in part using the new reasoning model GPT4o, gives its sense of what happened behind closed doors at OpenAI between Sam and Ilya, the hidden tactics of leading players such as Satya Nadella and Marc Andreessen, and what was said by the leading players as they grappled with an unprecedented crisis in the tech industry.
“It’s interesting to find out just how unlikely it was that Sam did return,” Saatchi said in an interview with GamesBeat. “That’s why people run war games in D.C. and beyond. How likely was it that a particular event happened? Then you can base decisions around that. This scenario showed that 16 out of 20 times, Sam did not return.”
Across 20 simulations, Sam Altman’s AI returned as CEO four times — showing just how unlikely this outcome was. In other outcomes, Mira Murati, the acting CEO remained CEO and in one, SIM-1 chose Elon Musk, Altman’s rival, to become the new CEO.
“Today, AI agents are defined by their personality. We wanted to show agents operating on decision making in a complex simulation,” said Saatchi, in a statement. “In the five days from November 17 to November 21, the world watched some of its most intelligent people — people like Satya Nadella, Sam Altman and Ilya Sutskever – forced to operate in a rapid Game of Thrones, high pressure, short timeframe scenario, where they had to use game theory and deception to come out on top. We felt this was a perfect scenario to test out SIM-1, GPT4o and Sim Francisco.”
For us, Sim Francisco has actual power and intelligence around a struggle and factions. It gives us the ability to start to think about season-long arcs of stories that come out of San Francisco, instead of just little, tiny vignettes, which is what we showed last year. It gives us the ability to kind of tell richer, more complex stories in San Francisco, or have the AI tell them for us. There are strong factional objectives so that you could plausibly start to make a Game of Thrones story.”
Fable has won a couple of Primetime Emmy Awards and it has gone through a rich history of experimental inventions with virtual reality, gaming and AI technologies. It built SIM-1 in an attempt to solve the mystery of what happened in the OpenAI boardroom fight.
How it works
Each of the 20 simulations starts with the announcement that Sam Altman has been removed as CEO. Across four turns a day, each agent has the ability to cajole, charm and manipulate their way into the top position — replacing Sam as CEO, funding his new venture, or hiring the staff of OpenAI away.
The different AI agents can choose a strategy, like deception, to try to pull ahead of the others and become anointed the new CEO.
“AI characters today are ‘nice but dull.’ We wanted to show agents that were aggressive, intelligent, able to manipulate and deceive but also confused about their own decisions and goals — like real people AI characters must be complex and contain what Jung has called ‘The Shadow,’” Saatchi said. “The five days from when Sam Altman was removed and returned to OpenAI were game theory at lightspeed.”
He said it was like watching a season of Game of Thrones play out in five days. The world watched as highly intelligent players vied to become the most powerful person in Silicon Valley, whether by hiring the entire staff of OpenAI, becoming the new CEO of OpenAI or funding Sam and Greg in a new venture for a chance at outsize investment returns.
“It was Game of Thrones in real life, and using AI to find out both what happened behind closed doors and to project different outcomes was an amazing challenge,” Saatchi said.
In the Simulation of Sim Francisco, over the five days, agents representing tech luminaries like Sam Altman, Satya Nadella and Ilya Sutskever each have 4 turns a day, including one for sleep, and can react to each other’s behavior. An adjudicator agent — similar to a dungeon keeper — decides which agent wins each round, as well as the overall winner.
In the 20 simulations attempted, the Sam Altman agent returned just four times – the most but still only 20% of the time showing just how unlikely his return was. Across different simulations agents used different techniques to win including alliance building, direct confrontation and more passive pure information gathering. In some cases agents only gathered information and avoided taking any aggressive actions. In one case Mira Murati became the permanent CEO while allowing other agents to aggressively undermine each other.
Different agents were given different goals appropriate to their role. For example, Dario Amodei, the CEO of Anthropic, balanced a desire to recruit for Anthropic, taking the opportunity to fundraise, to push for his vision of safety, as well as decide whether to aim to become the new CEO of a combined entity.
The interesting part of the simulation is that the LLM knows who the different players are, given that they’re all relatively famous people. It can guess how they will behave in a given situation, and what could unfold turn by turn as they try to outwit each other in a boardroom fight.
“It’s like a video game in that turn by turn, they’re making choices across different axes, and then they’re reacting to each other,” Saatchi said. “A choice that someone makes in turn seven can lead others to react in turn eight. There’s an adjudicator agent, who is like a dungeon master. That agent decides who won each round and who’s ahead, and then who decides at the end, wins as the most effective agent in the war game.”
Humans have what we call internally “the shadow,” or the other side of themselves and their personalities. The characters can feature aggression, paranoia, ambition, deception and more. When you mix together a bunch of different personalities, you can get a variety of outcomes in the simulations.
“We noticed LLM design isn’t based on decision making, which is really important for gaming. It’s based more on personality. And if you want to have a strategy game, nobody really cares about your personality. They care about your decision making. How are you under pressure? What have you done over the last 20 years that would give you a feel for what they might do in the future?”
Are simulations the future of gaming?
Saatchi thinks that AI agents acting within simulations are the future of gaming.
“We are building on the shoulders of giants with Demis’ work on Republic The Revolution, Joon Park’s Generative Agents paper and the recent work of Altera in Minecraft” said Saatchi said.
“Our theory is that the future of games and storytelling is simulations. If you wanted to build both The Simpsons game and The Simpsons TV show, you would, in the future, build Springfield, and that would then generate for you episodes of The Simpsons that would generate for you games and places to explore within Springfield as a game.”
He added, “You can tell many different stories within tribulations, once you get those simulations properly working. And we’ve got an alpha where people are uploading themselves to San Francisco as characters, telling stories, telling their own story.”
And he said, “You would build Springfield, and then you can guide what might happen in Springfield and say what might happen in Springfield, or you could just let it generate itself. It’s a pretty big mind shift of how entertainment, games and shows will be made in the future.”
Saatchi noted that AI researcher Noam Brown did a fascinating experiment with the game Diplomacy. He and other researchers “obtained a dataset of 125,261 games of Diplomacy played online at web Diplomacy.net.” Of those, 40,408 games contained dialogue, with a total of 12,901,662 messages exchanged between players. Their aim was to train a human-level AI agent, capable of strategic reasoning, by playing games of Diplomacy.
“We were really inspired by how he did that. He had countries and we were adding into the mix different personalities with particular positions. We liked the idea of a very compressed timeline,” where the whole scenario would play out quickly and over and over again, Saatchi said.
There has been a rich history of work in simulations in both the games industry and beyond. Demis Hassabis, who founded Deepmind (acquired by Google) and who recently won the Nobel Prize in Chemistry 2024 for computational protein design, actually began as a video game AI designer. Hassabis worked extensively with Peter Molyneux on several games which include simulation elements such as Theme Park, Black & White and Syndicate.
Hassabis also started his own company to make Republic: The Revolution. It’s a political simulation game in which the player leads a political faction to overthrow the government of a fictional totalitarian country in Eastern Europe, using diplomacy, subterfuge, and violence. According to Hassabis, Republic: The Revolution charts the whole of a revolutionary power struggle from beginning to end.
Your job is to kind of take over the Soviet Republic as either a union boss or a politician or a police officer or a journalist, and it’s got full day-night cycles. It raises the question of how you have a 3D world where agents live and whether proximity to each other plays a role.
For the Sim Francisco OpenAI project, it illustrated the potential for a power struggle against AIs.
Saatchi said the above examples shows how game technology often serves as the breeding ground for radical new ideas and as a jumping off ground for AI research. For example, one of the leading engineers on Deepmind AlphaFold started their career as an AI programmer on The Sims.
Richard Evans’ GDC talk on The Sims 3 — the researcher went from programming AI for The Sims to Deepmind in a reversal of Demis Hassabis’ journey from games to founding Deepmind.
Evans GDC Talk, Modeling Individual Personalities in The Sims 3, is very influential talk. He went on to join Deepmind after working on The Sims. The gaming world and the AI world have significant overlap that is a potential area for further academic research, Saatchi said.
One of Saatchi’s options is to let players loose with the simulations, creating their own, and then uploading the stories that are told through the simulations.
Saatchi has done some other experiments with AI-generated South Park episodes and AI characters battling each other in a Westworld setting.
“It felt like six seasons of Game of Thrones in five days, because it was the most powerful position in the most powerful industry in the world,” Saatchi said. “There was also a lot of faith that this person would be guiding us into a new era of super intelligence. You could say it wsa the most important person in the history of the planet.”
President Trump and the Taiwan invasion
Next, Fable intends to run a Sim Washington DC-based simulation around a future President Trump’s responses to a Chinese invasion of Taiwan.
As a next project to test out SIM-1’s decision making framework, Fable intends to test out a one-week period of buildup and conflict between Taiwan, China and the United States under President Donald Trump.
Fable has interviewed several Pentagon war games organizers to get a feeling for the strengths and weaknesses of the current Taiwan scenario.
Fable is building agents representing Chinese leader Xi Jingping, Cai Qi (first ranked secretary to the secretariat of the Communist Party), Chinese defense leader Dong Jun, Chinese premier Li Qiang, Taiwan’s leader Lai Ching-Te, Japan’s leader Shigeru Ishiba, UK prime minister Keir Starmer, French President Emmanuel Macron, Russia’s Vladimir Putin, North Korean leader Kim Jong Un and Elon Musk.
With this set of characters, the simulation would determine whether the war would happen and how would each major player act during such a crisis. All of these characters are known personalities.
“It allows you to see how powerful AI has become at like projecting outcomes,” Saatchi said. “It moves us out of this boring world of dumping an LLM into an NPC. You can talk to the tab and keeper for 40 hours. Nobody wants to do that. What we want is highly sophisticated, aggressive agents that we could play against, but also that we can, like, watch and understand what’s going on in that world.”
Many of the war game simulations are aimed at how to avoid a war, perhaps through forming alliances or other maneuvers that drive up the cost of war.
“We think the more realistic we can make our AIs, the more entertaining they will be,” Saatchi said.
Key takeaways
- To generate crypto market insights via ChatGPT, collect accurate historical and real-time data on prices, trading volumes and market capitalization.
- Organize data into clear formats, such as tables with consistent date formats and labeled columns, to help ChatGPT identify patterns and trends.
- Use precise and focused prompts to guide ChatGPT in generating actionable insights, enhancing the relevance and clarity of its responses.
- Cross-check ChatGPT’s outputs with up-to-date information from reputable sources before making trading decisions to account for potential inaccuracies.
Predicting crypto market trends can feel like navigating a storm — unpredictable and fast-changing. Prices can spike or crash unexpectedly due to investor sentiment, regulatory changes or sudden events such as exchange hacks. For traders, staying ahead means finding reliable ways to analyze these movements and make informed decisions.
This is where ChatGPT can help.
By analyzing historical data and recognizing patterns, ChatGPT offers insights that can support better decision-making. But for AI tools to deliver meaningful results, especially when using ChatGPT for crypto investments, it’s essential to follow the right process. Combining well-structured data, clear prompts and effective risk management can improve the accuracy and usefulness of its insights.
This article explores practical ways of how to use ChatGPT for crypto market analysis — from collecting and organizing data to crafting effective prompts that help the model generate actionable insights.
How to harness ChatGPT for crypto market analysis
While predicting crypto trends will always have its challenges, using data-driven insights with ChatGPT can make market behavior easier to understand. With the right strategy, ChatGPT becomes a powerful tool to identify patterns, highlight emerging trends, and support smarter trading decisions.
Using ChatGPT effectively for crypto analysis involves four key steps:
- Step 1: Gathering data for analysis
- Step 2: Formatting data for analysis via ChatGPT
- Step 3: Writing clear and effective prompts
- Step 4: Caution! Verify ChatGPT insights before drawing conclusions
Step 1: Gathering data for analysis
When it comes to predicting crypto trends, data is everything. Without reliable data, even the most advanced tools like ChatGPT can deliver unreliable insights. Crypto markets are notoriously volatile, and understanding the patterns behind price movements, whale activity and investor sentiment requires trustworthy information from the right sources.
The type of data required depends on the kind of analysis being performed. For example:
- Price analysis requires accurate records of past prices, volume and market cap trends.
- Whale activity analysis focuses on large investor movements and wallet behavior.
- Sentiment analysis relies on tracking social media discussions, influencer mentions and crowd sentiment shifts.
Did you know? A study found that higher X post engagement generally correlates negatively with cryptocurrency prices, indicating that increased social media activity may precede price declines.
Step 2: Formatting data for analysis via ChatGPT
To predict crypto trends with ChatGPT, data must be structured in a way that highlights patterns, trends and key events. Poorly formatted data can lead to incomplete or incorrect outputs, so investing time in proper organization is crucial.
Structuring data for analysis
When formatting price data, focus on key points that reflect market trends. Include the date open price, close price and volume in chronological order to capture market movement. This article uses the Bitcoin (BTC) price data below to illustrate the process.
Gaps in data are common, especially in volatile markets. Filling missing entries with estimated values, such as moving averages, can improve continuity and make analysis more accurate.
For technical indicators, like the relative strength index (RSI) or the moving average convergence divergence (MACD), aligning the data with consistent timestamps is key.
Sentiment data tends to be unstructured, which can make it challenging to analyze. To improve its clarity, combine sentiment scores with key dates and relevant events. For example:
Data cleaning and preparation
To maximize the accuracy of ChatGPT insights, take these steps:
- Ensure date formats are consistent (e.g., YYYY-MM-DD) to prevent misalignment.
- Remove duplicates to avoid skewed data patterns.
- Fill missing values by interpolating trends or forward-filling where necessary.
- Label data clearly to provide the necessary context for ChatGPT’s interpretation.
Did you know? A study found that ChatGPT’s sentiment analysis of news headlines can effectively predict daily stock returns, outperforming traditional methods.
Creating well-structured prompts is key to unlocking meaningful insights from ChatGPT, especially for ChatGPT crypto analysis. Poorly written prompts can confuse the model, resulting in incomplete or irrelevant responses. Clear prompts guide ChatGPT in focusing on the right data points and generating actionable insights.
Step 3: Writing clear and effective prompts
Effective prompts are built around three core principles: clarity, purpose and focus. The illustrations and prompts used in this article were experimented with using ChatGPT-4o.
Also, please note that ChatGPT outputs only show trimmed versions for illustration purposes. The original outputs are too long to display in full, but they provide detailed insights into each RSI dip, including exact price movements, duration and trader takeaways.
- Clarity: Use precise language that defines exactly what is needed. Avoid vague requests like:
“Is Bitcoin bullish?”
Instead, provide clear instructions with relevant details: “Analyze Bitcoin’s RSI and MACD data between December 2024 and January 2025. Identify points where both indicators aligned with bullish breakouts.”
- Purpose: Be specific about the outcome you expect. For example:
“Summarize how Bitcoin’s social sentiment changed in December 2024 and highlight its impact on price movement.”
- Focus: Include relevant conditions, such as timeframes, data sources or key indicators, to ensure the analysis is targeted and relevant. For instance:
“Identify instances where Bitcoin’s RSI dipped below 50 between December 2024 and January 2025. Describe how long each dip lasted and explain the resulting price movement.”
Prompt examples for crypto market trend analysis
Here are examples of effective prompts tailored for different types of crypto insights:
- Technical analysis prompt: “Analyze Bitcoin’s RSI dips below 30 from 2024 onward. Identify how long it typically took for the price to recover.”
- Sentiment analysis prompt: “Summarize Bitcoin sentiment trends on Reddit and Twitter throughout 2024. Identify patterns linked to price surges.”
- Strategy development prompt: “Create a trading strategy for Bitcoin using RSI, MACD, and whale accumulation data. Identify optimal entry and exit points.”
How to improve prompt quality
If ChatGPT’s response lacks detail or produces irrelevant insights, improving the prompt structure can enhance the outcome. Instead of rephrasing the same request, focus on adjusting the prompt’s depth, scope or context. Try these approaches for better results:
- Add more data references: Refer to RSI, MACD or other indicators to improve precision.
- Define the timeframe more clearly: Limiting the analysis period often provides sharper insights.
- Request comparative analysis: Asking ChatGPT to compare conditions across different timelines or trends can reveal more meaningful insights.
When tested on GPT-4o, a refined prompt produced significantly better results. The basic prompt, “Analyze Bitcoin RSI data,” returned vague and incomplete insights.
In contrast, an enhanced prompt — “Analyze Bitcoin’s RSI dips below 50 between December 2024 and January 2025. For each dip, identify the exact dates, duration, and the corresponding price movement. Explain whether the dips signaled trend reversals, corrections, or further declines. Additionally, provide insights in simple language, focusing on how traders can interpret these RSI movements for better decision-making in market entries and exits. Prepare a structured table summarizing each dip, including columns for date, RSI value, duration, price movement, and key insights for traders” — generated clear, actionable insights in contrast to previous output, as seen above.
The below table summarizes key differences in the outputs of Prompt 1 and Prompt 2:
As observed, taking the time to write clear, targeted prompts significantly improves ChatGPT’s ability to provide meaningful and actionable insights for crypto market analysis.
However, results may vary as ChatGPT may not yield the same outputs all the time due to differences in prompt wording, data interpretation and inherent variability in AI-generated responses. Also, traders should cross-check insights with real-time data and multiple sources for informed decision-making.
Step 4: Caution! Verify ChatGPT insights before drawing conclusions
Insights generated by ChatGPT can provide useful guidance, but verifying those insights is crucial before making investment decisions. Crypto markets are volatile, and relying solely on AI crypto market predictions without cross-referencing data may lead to poor outcomes.
Verifying ChatGPT insights
To confirm the accuracy and relevance of ChatGPT’s insights:
- Cross-check with trusted data sources: If ChatGPT highlights a bullish signal based on RSI trends, compare this finding with live data from platforms like TradingView, CoinGecko or Glassnode to confirm the signal’s validity.
- Review key market conditions: Market behavior often depends on broader economic events, news or geopolitical factors. If ChatGPT identifies a pattern, check if major events align with the prediction.
- Test insights on a demo account: Before applying any suggested strategy, test it in a risk-free environment using demo trading platforms to assess its effectiveness.
Applying verified insights
Once insights are verified, applying them effectively is essential:
- Set clear entry and exit points: If crypto trading with ChatGPT suggests a bullish breakout pattern, establish specific price points to minimize risk and secure profits.
- Use stop-loss orders: Protect investments by setting stop-loss points that limit potential losses if the trend reverses unexpectedly.
- Diversify approach: Even when ChatGPT identifies promising trends, combining insights from multiple data sources helps reduce reliance on a single prediction.
Did you know? A survey by Mercer Investments in 2024 revealed that 54% of investment managers have already integrated AI into their investment processes, while over 90% are either currently using or planning to adopt AI tools.
Limitations of using ChatGPT for crypto market predictions
While ChatGPT can be a valuable tool for analyzing market trends, it has several limitations:
- Lack of real-time data: ChatGPT does not have live access to market prices, trading volumes or real-time sentiment. External data sources are needed for up-to-date analysis.
- No predictive accuracy guarantee: ChatGPT analyzes historical patterns and sentiment but cannot predict future price movements with certainty. Market conditions can change rapidly due to unforeseen factors.
- Data quality dependence: The accuracy of insights depends on the quality of the input data. If outdated or biased information is provided, the analysis may be misleading.
- Limited understanding of market manipulation: ChatGPT cannot detect wash trading, pump-and-dump schemes or other forms of market manipulation that can influence crypto prices.
- No personal financial advice: ChatGPT does not provide personalized investment recommendations. Traders should combine AI-generated insights with technical analysis, fundamental research and risk management strategies.
As the saying goes, “Past performance is not indicative of future results.” AI tools like ChatGPT can support decision-making, but they should never replace critical thinking. Thus, always cross-check AI-driven insights with reliable market research before making any trading decisions.
The future of ChatGPT in predicting crypto market trends
As AI technology continues to evolve, using ChatGPT for crypto forecasting is expected to become more refined and integrated with real-time data platforms. Future developments could include:
- Enhanced data integration: While ChatGPT cannot access live market data directly, integrating it with financial data providers like Finnhub or Polygon.io via APIs may allow real-time data retrieval.
- Improved prediction models: AI models are rapidly improving their ability to identify complex patterns, potentially enhancing prediction accuracy.
- Automated trading strategies: Future updates may enable traders to automate strategies based on ChatGPT insights, with alerts for optimal entry and exit points.
While ChatGPT is already a valuable tool, its capabilities will likely expand further as AI continues to develop, providing crypto traders with even more effective analysis and strategic insights
This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.
Google Slides es una herramienta poderosa, pero crear una presentación completa puede llevar mucho tiempo. Recientemente, Google introdujo la integración de Gemini en diapositivas y todas las aplicaciones del espacio de trabajo. Ahora, solo necesita indicaciones de texto para crear presentaciones atractivas e imágenes de alta calidad para sus diapositivas. Tuve que verlo yo mismo, y decidí experimentar con Géminis y lo encargué con la construcción de una presentación completa.
En esta publicación, comparto mi viaje y revelo cómo Gemini manejó el desafío y si ofrece la promesa de presentaciones sin esfuerzo.
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Google Gemini: Todo lo que necesita saber sobre la IA multimodal de próxima generación de Google
Google Gemini está aquí, con un enfoque completamente nuevo para la IA multimodal
Acceso a Géminis en las diapositivas de Google: requisitos
Antes de encender las hojas de Google en la web, repasemos los requisitos. Si bien varios modelos Gemini son gratuitos de descargar y usar, pagará más para desbloquear el asistente de IA en las aplicaciones de productividad de Google.
Debe comprar el plan avanzado de Gemini a $ 20 por mes. Después de eso, la opción Géminis aparece en Docs, Hojas, Gmail, Google Drive y Slides. Google también ofrece un mes de prueba gratuita para usuarios elegibles.
Dado que Google Slides es una solución web, puede explorar la integración de Gemini en escritorios de Windows, Mac y Chromebooks.
Explorando Géminis en las diapositivas de Google
Genere diapositivas utilizando un mensaje de texto
Después de habilitar Gemini en Google Slides, es hora de verificarlo en acción. En el siguiente ejemplo, crearé una presentación sobre los beneficios de un estilo de vida saludable. Mi objetivo es cubrir los beneficios de la nutrición, el ejercicio regular, el bienestar mental y el manejo del estrés. Siga los pasos a continuación.
-
Inicie las diapositivas de Google en la web e inicie sesión con los detalles de su cuenta de Google. Comience con una presentación en blanco.
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Abra Géminis desde la esquina superior derecha y escriba un aviso.
Escribir un aviso es una parte crucial de su proceso de presentación. Dado que es un tema amplio y adaptable, sea lo más descriptivo posible. En nuestro caso, escribiré un aviso a continuación para mi diapositiva de introducción.
Genere una diapositiva con el título “Los beneficios de un estilo de vida saludable”. Agregue una definición breve de un estilo de vida saludable, enfatizando el equilibrio del bienestar físico, mental y nutricional.
Esto es lo que se le ocurrió a Géminis. Puede volver a intentarlo si no está satisfecho con los resultados y haga clic en Insertar para agregarlo.
Ahora, haga clic + + Para agregar una nueva diapositiva y continuar escribiendo indicaciones para generar nuevas diapositivas para su presentación.
Cree una diapositiva titulada “Nutrición: alimentar su cuerpo”. Agregue información sobre la importancia de las frutas y verduras.
A diferencia de Copilot en PowerPoint, no puede crear múltiples diapositivas a la vez. Debes describir cada diapositiva por separado. Por lo tanto, asegúrese de planificar el esquema de su presentación.
Después de eso, creé cuatro diapositivas nuevas utilizando las indicaciones de texto a continuación.
Cree una diapositiva titulada, “Ejercicio: moverse para un usted más saludable”. Agregue información sobre la cantidad recomendada de ejercicio por semana.
Crea una diapositiva titulada, “Bienestar mental: encontrar tu paz interior”. Agregue puntos de bala en buenos hábitos de sueño.
Genere una diapositiva que enumere los beneficios de un estilo de vida saludable, que incluye un aumento de la energía, un mejor estado de ánimo y un mejor sueño.
Cree una diapositiva de conclusión con pasos prácticos para adoptar un estilo de vida más saludable. Incluir puntos de bala orientados a la acción.
Hubo algunos casos en los que no estaba satisfecho con los resultados. Entonces, le pedí a Gemini que recreara esas diapositivas. Además, no te sorprenderá con diseños de diapositivas llamativas y animaciones. Debe agregarlos manualmente y completar su presentación.
En cualquier momento, puede escribir @Nombre del archivo Y solicite a Gemini que se refiera a un documento de su cuenta de Google Drive. Por ejemplo, si escribió una dieta vegetariana en un documento, puede pedirle a Gemini que se refiera a ella para sus diapositivas de presentación.
Estás usando diapositivas generadas por AI. La precisión puede recibir un éxito cuando se trata de temas complejos como IA, fotografía computacional, aprendizaje automático y más. Compruebe dos veces antes de compartir la presentación con otros.
Relacionado
Google Gemini: 5 maneras de usar el asistente a día a día de Google con IA
Puede hacer que muchas tareas cotidianas sean mucho más fáciles
Crear e insertar imágenes con Gemini
No tenía idea de que Géminis podía crear imágenes basadas en indicaciones de texto. Es un gran ahorro de tiempo, ya que no necesita buscar imágenes en la web para obtener imágenes adecuadas para su presentación. Generé un par de imágenes relevantes utilizando las indicaciones de texto a continuación.
Una imagen de una placa equilibrada con proteína magra, granos integrales y verduras.
Una fotografía de primer plano de un vaso de agua con rebanadas de limón y pepino.
Gemini le ofrece cuatro opciones de imagen para sus diapositivas. Puede verlos e insertarlos en sus diapositivas.
Géminis hizo mis diapositivas
Géminis en Google Slides abrió mis ojos al potencial de la IA en la creación de presentación. Si bien no es un reemplazo perfecto para la creatividad humana y el pensamiento estratégico, es una herramienta poderosa para racionalizar el proceso, especialmente para elaborar borradores iniciales y imágenes llamativas.
Aún así, la supervisión humana es crucial, pero si tiene plazos ajustados o desea explorar nuevas formas de crear diapositivas atractivas, pruebe a Gemini. Gemini Advanced desbloquea el asistente de IA de Google en otras aplicaciones de productividad como Google Sheets. Así es como puedes aumentar tus hojas de cálculo con Gemini.
Google Assistant está evolucionando a Géminis, trayendo potentes nuevas capacidades de IA pero también descontinuando algunas características favoritas. Si usa el Asistente de Google para establecer temporizadores, reproducir música o controlar su hogar inteligente, prepárese para algunas interrupciones significativas a medida que la compañía comienza a reemplazar al asistente de nueve años con su chatbot Gemini más nuevo, más potente y alimentado por IA. Este artículo describirá los cambios clave que puede esperar, ayudándole a prepararse para la transición y comprender lo que será diferente.
Gemini representa un salto gigante en la capacidad en comparación con el Asistente de Google. Podrá chatear con Gemini de manera similar a la forma en que hablas con Google Assistant ahora, pero como se basa en modelos de lenguaje grande (LLM) con AI, Gemini puede ser mucho más conversacional y útil, capaz de realizar tareas más desafiantes y capaz de adaptarle sus respuestas específicamente a usted. Google ya ha comenzado la transición a Gemini. Los teléfonos inteligentes son los primeros en cambiar y serán seguidos por altavoces inteligentes, televisores, otros dispositivos domésticos, dispositivos portátiles y automóviles en los próximos meses. Los teléfonos inteligentes, con algunas excepciones importantes, se habrán mudado a Gemini por completo a fines de 2025, ya que “el asistente clásico de Google ya no se puede acceder en la mayoría de los dispositivos móviles o disponible para nuevas descargas en tiendas de aplicaciones móviles”, según Google.
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