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A deep dive analysis of 62 queries

The emergence of ChatGPT search has led to many questions about the quality of the overall results compared to Google.
This is a difficult question to answer, and in today’s article, I will provide some insights into how to do just that.
Note that our understanding is that the technology that makes it possible for OpenAI to offer a search capability is called SearchGPT, but the actual product name is ChatGPT search.
In this article, we will use the name ChatGPT search.
What’s in this report
This report presents an analysis of 62 queries to assess the strengths and weaknesses of each platform.
Each response was meticulously fact-checked and evaluated for alignment with potential user intents.
The process, requiring about an hour per query, highlighted that “seemingly good” and “actually good” answers often differ.
Additionally, when Google provided an AI Overview, it was scored against ChatGPT search.
A combined score for the AI Overviews and the rest of Google’s SERP was also included.
Of the queries tested – two-thirds of which were informational – Google returned an AI Overview in 25 instances (40% of the time).
The queries analyzed fell into multiple categories:

The total number of the above is greater than 100%, and that’s because some queries could fall into more than one classification.
For example, about 13% of the queries were considered informational and commercial.
Detailed information from SparkToro on the makeup of queries suggests a natural distribution of search queries as follows:


Navigational queries, which comprise nearly a third of all queries, were excluded from this test.
These queries typically demand a straightforward response like, “just give me the website,” and are a category where Google excels.
However, I included queries likely to favor one platform, such as:
- Content gap analysis queries (4): Representing a broader class of content-related queries, which Google doesn’t handle but ChatGPT search attempts (though not always successfully).
- Locally oriented queries (4): These leverage Google’s extensive local business database, Google Maps, and Waze, areas where ChatGPT search struggles to compete.
Metrics used in this study
I designed 62 queries to reflect diverse query intents, aiming to highlight each platform’s strengths and weaknesses.
Each response was scored across specific metrics to evaluate performance effectively.
- Errors: Did the response include incorrect information?
- Omissions: Was important information not in the response?
- Weaknesses: Were other aspects of the response considered weak but not scored as an error or omission?
- Fully addresses: Was the user’s query intent substantially addressed?
- Follow-up resources: Did the response provide suitable resources for follow-up research?
- Quality: An assessment by me of the overall quality of the response. This was done by weighing the other factors contained in this list.
At the end of this article are the total scores for each platform across the 62 queries.
Competitive observations
When considering how different search platforms provide value, it’s important to understand the many aspects of the search experience. Here are some of those areas:
Advertising
Multiple reviewers note that ChatGPT search is ad-free and tout how much better this makes it than Google. That is certainly the case now, but it won’t stay that way.
Microsoft has $13 billion committed to OpenAI so far, and they want to make that money back (and then some).
In short, don’t expect ChatGPT search to remain ad-free. That will change significantly at some point.
An important note is that advertising works best on commercial queries.
As you will see later in this article, I scored Google’s performance on commercial queries significantly higher than ChatGPT search.
Understanding user intent
Google has been working on understanding user intent across nearly infinite scenarios since 2004 or earlier.
They’ve been collecting data based on all the user interactions within search and leveraging what they have seen with the Chrome browser since its launch in 2008.
This data has most likely been used to help train Google algorithms to understand user intent and brand authority on a per query basis.
For reference, as of November 2024, Statcounter pegs Chrome’s market share at 67.5%, Safari at 18.2%, and Edge at 4.8%
This is a critical advantage for Google because understanding the user intent of a query is what it’s all about.
You can’t possibly answer the user’s need without understanding their need. As I’ll illustrate in the next section, this is complex!
How query sessions work
Part of the problem with understanding user intent is that the user may not have fully worked out what they’re looking for until they start the process.
Consider the following example of a query sequence that was given to me via Microsoft many years ago:


The initial query seems quite simple: “Merrell Shoes.”
You can imagine that the user entering that query often has a specific Merrell shoe in mind, or at least a shoe type, that they want to buy.
However, we see this user’s path has many twists and turns.
For example, the second site they visit is www.merrell.com, a website you might suspect has authoritative information about Merrell shoes.
However, this site doesn’t appear to satisfy the user’s needs.
The user ends up trying four more different queries and visiting six different websites before they finally execute a transaction on www.zappos.com.
This degree of uncertainty in search query journeys is quite common.
Some of the reasons why users have this lack of clarity include is that they:
- Don’t fully understand the need that they’re feeling.
- Don’t know how to ask the right questions to address their need.
- Need more information on a topic before deciding what they need.
- Are in general exploration mode.
Addressing this is an essential aspect of providing a great search experience. This is why the Follow-Up Resources score is part of my analysis.
Understanding categories of queries
Queries can be broadly categorized into several distinct groups, as outlined below:
- Informational: Queries where the user wants information (e.g., “what is diabetes?”).
- Navigational: Queries where the user wants to go to a specific website or page (e.g., “United Mileage Club”).
- Commercial: Queries where the user wants to learn about a product or service (e.g., “Teak dining table”).
- Transactional: Queries where the user is ready to conduct a transaction (e.g., “pizza near me”).
Recent data from SparkToro’s Rand Fishkin provides some insight into the percentage of search queries that fall into each of these categories:


Be advised that the above is a broad view of the categories of queries.
The real work in search relates to handling searches on a query-by-query basis. Each query has many unique aspects that affect how it can be interpreted.
Next, we’ll examine several examples to illustrate this. Then, we’ll compare how ChatGPT search and Google performed on these queries.
Query type: Directions
This query type is a natural strength for Google (as is any locally oriented query). We can see ChatGPT search’s weaknesses in this area in its response:


The problems with this response are numerous.
For example, I wasn’t in Marlborough, Massachusetts, when I did the query (I was in the neighboring town of Southborough).
In addition, steps 1 and 2 in the directions are unclear. Anyone following them and heading east on Route 20 would end up at Kenmore Square in Boston without ever crossing I-90 East.
In contrast, Google nails it:


The reason why Google handles this better is simple.
Google Maps has an estimated 118 million users in the U.S., and Waze adds another 30 million users.
I wasn’t able to find a reasonable estimate for Bing Maps, but suffice it to say that it’s far lower than Google’s.
The reason Google is so much better than Bing here is simple – I use Google Maps, and that lets Google know exactly where I am.
This advantage applies to all Google Maps and Waze users in the U.S.
Query type: Local
Other types of local queries present similar issues to those of ChatGPT search. Note that a large percentage of search queries have local intent.
One estimate pegged this at 46% of all queries. This was reportedly shared by a Googler during a Secrets of Local Search conference at GoogleHQ in 2018.
Here is ChatGPT’s response to one example query that I tested:


As with the directions example, it thinks that I’m in Marlborough.
In addition, it shows two pizza shops in Marlborough (only one of the two is shown in my screenshot).
Google’s response to this query is much more on point:


I also gave Google a second version of the query “Pizza shops in Marlborough,” and it returned 11 locations – 9 more than I saw from the ChatGPT search.
This shows us that Google also has far more access to local business data than ChatGPT search.
For this query class (including the Directions discussed previously), I assigned these scores:
- ChatGPT search: 2.00.
- Google: 6.25.
Query type: Content gap analysis
A content gap analysis is one of the most exciting SEO tasks that you can potentially do with generative AI tools.
The concept is simple: provide the tool of your choice a URL from a page on your site that you’d like to improve and ask it to identify weaknesses in the content.
As with most things involving generative AI tools, it’s best to use this type of query as part of a brainstorming process that your subject matter expert writer can use as input to a larger process they go through to update your content.
There are many other different types of content analysis queries that you can do with generative AI that you can’t do with Google (even with AI Overviews) at this point.
For this study, I did four content gap analysis queries to evaluate how well ChatGPT search did with its responses.
Google presented search results related to the page I targeted in the query but did not generate an AI Overview in any of the four cases.
However, ChatGPT search’s responses had significant errors for three of the four queries I tested.
Here is the beginning of ChatGPT search’s response to the one example query where the scope of errors was small:


This result from ChatGPT isn’t perfect (there are a few weaknesses, but it’s pretty good. The start of Google’s response to the same query:


As you can see, Google hasn’t even attempted to perform a content gap analysis. ChatGPT search is better set up to address this type of query.
However, ChatGPT search doesn’t earn a clean sweep for this type of query.
Here is the first part of another example result:


This looks good in principle, but it’s filled with errors. Some of these are:
- The Britannica article does discuss the depth of Larry Bird’s impact on Indiana State University.
- The Britannica article does mention the importance of the Larry Bird / Magic Johnson rivalry to the NBA
- The ChatGPT search response is longer than shown here and there are other errors beyond what I mention here.
Overall, I tried four different content gap analysis queries and ChatGPT search made significant errors in three of them. For this query, I assigned these scores:
- ChatGPT search: 3.25.
- Google: 1.00.
Query type: Individual bio
How these queries perform is impacted by how well-known the person is.
If the person is very famous, such as Lionel Messi, there will be large volumes of material written about them.
If the amount of material written about the person is relatively limited, there is a higher probability that the published online information hasn’t been kept up to date or fact-checked.
We see that in the responses to the query from both ChatGPT search and Google.
Here is what we see from ChatGPT search:


The main issues with this response are in the third paragraph.
I haven’t written for Search Engine Journal in a long time, and it’s also been more than six years since I published a video on my YouTube channel (@stonetemplecons).
Let’s see what Google has to say:


Google also has problems with its response. They lead with quite a few images of me (which are all accurate), and below that, they show my LinkedIn profile and a summary of me drawn from Google Books.
Here, it says that I write for Search Engine Watch (haven’t done that for more than a decade!) and SEOMoz (which rebranded to SEOmoz to Moz in 2013) (also more than a decade!).
These responses are both examples of what I call “Garbage-In-Garbage-Out” queries.
If the web sources aren’t accurate, the tools don’t have the correct information to render.
For bio queries (3 of them), I scored the competitors as follows:
- ChatGPT search: 6.00.
- Google: 5.00.
Query type: Debatable user intent
Arguably, nearly every search query has debatable user intent, but some cases are more extreme than others.
Consider, for example, queries like these:
- Diabetes.
- Washington Commanders.
- Physics.
- Ford Mustang.
Each of these examples represents an extremely broad query that could have many different intents behind it.
In the case of diabetes:
- Does the person just discover that they have (or a loved one has) diabetes, and they want a wide range of general information on the topic?
- Are they focused on treatment options? Long-term outlook? Medications? All of the above?
Or, for a term like physics:
- Do they want a broad definition of what it’s about?
- Or is there some specific aspect of physics that they wish to learn much more about?
Creating the best possible user experience for queries like these is tricky because your response should provide opportunities for each of the most common possible user intents.
For example, here is how ChatGPT responded to the query “physics”:


The additional two paragraphs of the response focused on the definition of Physics and kept the response at a very high level.
In contrast, the beginning of Google’s response also focuses on a broad definition of physics, but following that are People Also Ask and Things to Know boxes that address many other potential areas of interest to people who type in this search query:


This part of Google’s response shows a recognition of the many possible intents that users who type in the phrase “physics” may have in mind.
For this query, I assigned these scores:
- ChatGPT search: 5.00.
- Google: 7.00.
Query type: Disambiguation
One special class of debatable intents queries is words or phrases that require disambiguation. Here are some example queries that I included in my test set:
- Where is the best place to buy a router?
- What is a jaguar?
- What is mercury?
- What is a joker?
- What is a bat?
- Racket meaning.
For example, here is how ChatGPT search responded to the question, “What is a joker query?”


We can see that it offers a nice disambiguation table that provides a brief definition for five different meanings of the term.
It also includes links to pages on the web that users can visit for information related to each meaning.
In contrast, Google focuses on two major intents:


Google’s focus is on the playing card and a person who tells a lot of jokes.
Following this part of the SERP, Google continues this approach with websites focusing on these two definitions.
This means that someone who’s interested in the word “joker” as it applies to contract clauses will have to do an additional search to find what they were looking for (e.g., “meaning of joker when referring to contract clauses”).
Which is better?
Well, it depends.
If the searchers interested in playing cards or people who tell lots of jokes make up more than 90% of the people who enter this search query, then the Google result might be the better of the two.
As it is, I scored the ChatGPT search result a bit higher than Google’s for this query.
Another example of disambiguation failure is simply not addressing it at all. Consider the query example: “where is the best place to buy a router?”
Here is how ChatGPT search addressed it:


You might think this result is perfect, but routers also refer to a tool used in woodworking projects.
I use one frequently as a part of building furniture from scratch (true story).
There is a large enough audience of people who use these types of routers that I hope to see recognition of this in the SERPs.
Here is Google’s response to the query:


This part of the SERP is followed by:


Google focuses on the internet router to the same degree as ChatGPT.
For this class of queries, I assigned these scores:
- ChatGPT search: 6.00.
- Google: 5.29.
Query type: Maintaining context in query sequences
Another interesting aspect of search is that users tend to enter queries in sequences.
Sometimes those query sequences contain much information that helps clarify their query intent.
An example query sequence is as follows:
- What is the best router to use for cutting a circular table top?
- Where can I buy a router?
As we’ve seen, the default assumption when people speak about routers is that they refer to devices for connecting devices to a single Internet source.
However, different types of devices, also called routers, are used in woodworking.
In the query sequence above, the reference to cutting a circular table should make it clear that the user’s interest is in the woodworking type of router.
ChatGPT’s response to the first query was to mention two specific models of routers and the general characteristics of different types of woodworking routers.
Then the response to “where can I buy a router” was a map with directions to Staples and the following content:


All of the context of the query was 100% lost.
Sadly, Google only performed slightly better.
It identified three locations, two of which were focused on networking routers and one which was focused on woodworking routers (Home Depot):


For this query, I scored the tools this way:
- ChatGPT search: 2.00.
- Google: 3.00.
Query type: Assumed typos
Another interesting example is queries where your search is relatively rare, yet it has a spelling that’s similar to another word.
For this issue, my search was: “Please discuss the history of the pinguin.”
The Pinguin was a commerce raider used by the German Navy in World War 2. It just has a spelling very similar to “penguin,” which is an aquatic flightless bird.
Both ChatGPT and Google simply assumed that I meant “penguin” and not “pinguin.”
Here is the result from ChatGPT:


The result continues after what I’ve shown here but continues to focus on the bird, not the boat.
Google makes the same mistake:


After the AI Overview and the featured snippet I’ve shown here, the SERPs continue to show more results focused on our flightless friends.
To be fair, I’ve referred to this as a mistake, but the reality is that the percentage of people who enter “pinguin” that simply misspelled “penguin” is probably far greater than those who actually mean the German Navy’s WW2 commerce raider.
However, you’ll notice that Google does one thing just a touch better than ChatGPT here.
At the top of the results, it acknowledges that it corrected “pinguin” to “penguin” and allows you to change it back.
The other way I addressed the problem was to do a second query: “Please discuss the history of the pinguin in WW2,” and both ChatGPT and Google gave results on the WW2 commerce raider.
For this query, I assigned these scores:
- ChatGPT search: 2.00.
- Google: 3.00.
Query type: Multiple options are a better experience
There are many queries where a single (even if it is well thought out) response is not what someone is probably looking for.
Consider, for example, a query like: “smoked salmon recipe.”
Even though the query is in the singular, there is little chance that anyone serious about cooking wants to see a single answer.
This type of searcher is looking for ideas and wants to look at several options before deciding what they want to do.
They may want to combine ideas from multiple recipes before they have what they want.
Let’s look at the response from ChatGPT search:






I’ve included the first three screens of the response (out of four), and here you will see that ChatGPT search provides one specific recipe from a site called Honest Food.
In addition, I see some things that don’t align with my experience.
For example, this write-up recommends cooking the salmon to 140 degrees. That’s already beginning to dry the salmon a bit.
From what I see on the Honest Food site, they suggest a range of possible temperatures starting from as low as 125.
In contrast, Google offers multiple recipes that you can access from the SERPs:




This is an example of a query that I scored in Google’s favor, as having multiple options is what I believe most searchers will want.
The scores I assigned were:
- ChatGPT search: 4.00.
- Google: 8.00.
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Types of problems
Next, we’ll examine the types of things that can go wrong. I looked for these issues while scoring the results.
The analysis noted where problems that generative AI tools are known for were found and potential areas of weakness in Google’s SERPs.
These included:
- Errors.
- Omissions.
- Weaknesses.
- Incomplete coverage.
- Insufficient follow-on resources.
Problem type: Errors
This is what the industry refers to as “hallucinations,” meaning that the information provided is simply wrong.
Sometimes errors aren’t necessarily your money or your life situations, but they still give the user incorrect information.
Consider how ChatGPT search responds to a query asking about the NFL’s overtime rules:


Notice the paragraph discussing how Sudden Death works. Unfortunately, it’s not correct.
It doesn’t account for when the first team that possesses the ball kicks a field goal, in which case they could win the game if the second team doesn’t score a field goal.
If the second team scores a field goal, this will tie the game.
In this event, it’s only after the field goal by the second team that the next score wins the game.
This nuance is missed by ChatGPT search.
Note: The information on the NFL Operations page that ChatGPT search used as a source is correct.
Google’s AI Overview also has an error in it:


In the second line, where Google outlines “some other NFL overtime rules,” it notes that the same ends if the first team to possess the ball scores a touchdown.
This is true for regular season games but not true in the postseason, where both teams always get an opportunity to possess the ball.
Scores were as follows:
- ChatGPT search: 3.00.
- Google: 4.00.
Problem type: Omissions
This type of issue arises when important information that belongs in the response is left out.
Here is an example where ChatGPT search does this:


Under Pain Management, there is no mention of Tylenol as a part of a pain management regimen.
This is an unfortunate omission, as many people use only a mix of Tylenol and Ibuprofen to manage the pain after a meniscectomy.
Scores were as follows:
- ChatGPT search: 6.00.
- Google: 5.00.
Problem type: Weaknesses
I used weaknesses to cover cases where aspects of the result could have been more helpful to the searcher but where the identified issue couldn’t properly be called an error or omission.
Here is an example of an AI Overview that illustrates this:


The weakness of this outline is that it makes the most sense to charge the battery as the first step.
Since it takes up to 6 hours to complet,e it’s not that useful to set up the app before completing this step.
Here is how I scored these two responses:
- ChatGPT search: 3.00.
- Google: 5.00.
Problem type: Incomplete coverage
This category is one that I used to identify results that failed to cover a significant user need for a query.
Note that “significant” is subjective, but I tried to use this only when many users would need a second query to get what they were looking for.
Here is an example of this from a Google SERP.


The results are dominated by Google Shopping (as shown above).
Below what I’ve shown, Google has two ads offering online buying opportunities and two pages from the Riedl website.
This result will leave a user who needs the glasses today and therefore wants to shop locally without an answer to their question.
ChatGPT search did a better job with this query as it listed both local retailers and online shopping sites.
Scores for this query:
- ChatGPT search: 6.00.
- Google: 4.00.
Problem type: Insufficient follow-on resources
As discussed in “How query sessions work” earlier in this article, it’s quite common that users will try a series of queries to get all the information they’re looking for.
As a result, a great search experience will facilitate that process.
This means providing a diverse set of resources that makes it easy for users to research and find what they want/need. When these aren’t easily accessed it offers them a poor experience.
As an example, let’s look at how ChatGPT search responds to the query “hotels in San Diego”:






While this provides 11 hotels as options, there are far more than this throughout the San Diego area.
It’s also based on a single source: Kayak.
The user can click through to the Kayak site to get a complete list, but other resources aren’t made available to the user.
In contrast, Google’s results show many different sites that can be used to find what they want. The scores I assigned to the competitors for this one were:
- ChatGPT search: 3.00.
- Google: 6.00.
The winner?
It’s important to note that this analysis is based on a small sample of 62 queries, which is far too limited to draw definitive conclusions about all search scenarios.
A broader takeaway can be gained by reviewing the examples above to see where each platform tends to perform better.
Here’s a breakdown of category winners:
1. Informational queries
- Queries: 42
- Winner: Google
- Google’s average score: 5.83
- ChatGPT search’s average score: 5.19
Google’s slight edge aligns with its strong track record for informational searches.
However, ChatGPT Search performed respectably, despite challenges with errors, omissions, and incomplete responses.
2. Content gap analysis
- Winner: ChatGPT Search
- ChatGPT search’s average score: 3.25
- Google’s average score: 1.0
- ChatGPT Search excels in content gap analysis and related tasks, making it particularly useful for content creators. Winning use cases include:
- Content gap analysis
- Standalone content analysis
- Comparing direct or indirect SERP competitors
- Suggesting article topics and outlines
- Identifying facts/statistics with sources
- Recommending FAQs for articles
While ChatGPT search outperformed Google in this category, its lower overall score highlights areas where improvements are needed, such as accuracy.
3. Navigational queries
Navigational queries were excluded from the test since they typically don’t require detailed text responses.
Google’s dominance in this category is assumed based on its straightforward, website-focused results.
4. Local search queries
- Winner: Google
- Google’s average score: 6.25
- ChatGPT search’s average score: 2.0
Google’s extensive local business data, combined with tools like Google Maps and Waze, ensures its superiority in this category.
5. Commercial queries
- Winner: Google
- Google’s average score: 6.44
- ChatGPT search’s average score: 3.81
This category, comprising 16 queries, favored Google due to its stronger capabilities in showcasing product and service-related results.
6. Disambiguation queries
- Winner: ChatGPT search
- ChatGPT search’s average score: 6.0
- Google’s average score: 5.29
ChatGPT Search edged out Google by more effectively presenting multiple definitions or interpretations for ambiguous terms, providing users with greater clarity.
These scores are summarized in the following table:


Summary
After a detailed review of 62 queries, I still see Google as the better solution for most searches.
ChatGPT search is surprisingly competitive when it comes to informational queries, but Google edged ChatGPT search out here too.
Note that 62 queries are a tiny sample when considered against the scope of all search.
Nonetheless, as you consider your search plans going forward, I’d advise you to do a segmented analysis like what I did before deciding which platform is the better choice for your projects.
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.
Noticias
¿Qué significa Sun Sextile Júpiter para su signo del zodiaco?

¡Prepárate para montar la ola de suerte y expansión!
El 6 de abril, mientras transmite el signo audaz y ardiente de Aries, el Sol se reunirá con Júpiter en Géminis en un afortunado sextil, creando una oportunidad emocionante para el crecimiento, la prosperidad y los nuevos y audaces comienzos.
Si ha estado ansiando algo nuevo y emocionante, este tránsito podría ser la luz verde que ha estado esperando. Es el momento perfecto para arriesgarse, expandir sus horizontes y celebrar hitos.
El Sol en Aries tiene que ver con la acción, el coraje y el liderazgo. Como el primer signo en el zodiaco, Aries encarna una chispa de iniciación, al igual que su gobernante planetario, Marte. Entonces, con el sol viajando a través de este intrépido signo de fuego, es hora de avanzar y adoptar desafíos con confianza y coraje.
Júpiter, por otro lado, es el planeta de abundancia, optimismo y expansión. En el signo cerebral de Géminis (curiosidad, comunicación y adaptabilidad, Júpiter abre un mundo de posibilidades. Amplifica la necesidad de exploración intelectual, nuevas ideas y conexiones sociales.
Este tránsito nos invita a ampliar nuestras perspectivas, pensar fuera de la caja y sumergirnos en nuevas aventuras que expanden nuestras vidas personales y profesionales.
El sextil del sol a Júpiter el 6 de abril es una poderosa combinación de pasión ardiente y expansión intelectual. Es un momento en que las oportunidades de crecimiento y exploración se sienten abundantes, y el cosmos recompensa a aquellos que están dispuestos a correr riesgos y adoptar el cambio.
Ya sea que esté comenzando un nuevo proyecto, tomando una gran decisión o sentirse inspirado para hacer algo nuevo, este tránsito ofrece el potencial de prosperidad y abundancia.
Siga leyendo para lo que esto significa para su signo del zodiaco.
Aries (del 20 de marzo al 19 de abril)
¡Eres la estrella del espectáculo, Aries! Además de que es su temporada de regreso solar, con el sol encendido por su primera casa, está uniendo fuerzas con Lucky Júpiter … y bueno, ¡eres imparable! Este es el momento perfecto para lanzar un proyecto personal o renovar su imagen. Considere actualizar su marca o presencia en las redes sociales para que coincida con su energía. Este tránsito se trata de ti, poseerlo y brillar.
Tauro (del 19 de abril del 20 de mayo)
Este es un momento para la reflexión y el crecimiento espiritual, Tauro. Aries gobierna su introspectiva casa 12 de patrones subconscientes y la iluminación de Júpiter en su segunda casa de finanzas y valores, lo que le brinda la claridad de liberarse de los viejos patrones mentales que ya no le sirven. Tal vez es hora de dejar de lado esos sistemas de creencias limitantes en torno al dinero o su autoestima. Confíe en que las nuevas y prósperas oportunidades esperan una vez que lo haga.
Géminis (del 20 de mayo al 20 de junio)
¡Es tu día de suerte, Géminis! A medida que el Sol energiza a su 11ª Casa de Asuntos Comunitarios, Júpiter aporta expansión y oportunidad a su letrero (¡y en la puerta de entrada!), Lo que lo convierte en un excelente momento para conectarse con personas influyentes y aquellos que comparten objetivos y sueños similares. Una oportunidad de establecer contactos podría llegar en su camino, o podría unirse inesperadamente a un grupo que se alinee perfectamente con sus valores.
Cáncer (del 20 de junio al 22 de julio)
Alcance las estrellas: su carrera está bajo el centro de atención, el cáncer. A medida que el Sol energiza y revitaliza su décima casa de autoridad pública, Lucky Júpiter lo hace más receptivo y sintonizado con su crecimiento personal y profesional. Esto no solo ofrece ideas espirituales, sino que también te empuja a hacer movimientos audaces en tu vida profesional. El éxito está en el horizonte.
Leo (22 de julio al 22 de agosto)
¡La aventura te espera, Leo! El sol está gobernado por el sol, y mientras enciende su novena casa de expansión filosófica, unirá fuerzas con Lucky Júpiter en su 11ª Casa de Asociaciones, Asuntos Comunitarios y visiones futuras. Ya sea que se trate de un viaje de último minuto, una clase en la que se está inscribiendo o un pasatiempo nuevo que está explorando, su mente y su corazón están abiertos a nuevas experiencias. Carpe Diem.
Virgo (22 de agosto al 22 de septiembre)
Este es un gran problema: confía en que la transformación que está experimentando es para su más alto bien, Virgo. Con el sol sacudiendo su octava casa de intimidad y recursos compartidos, se reunirá con Lucky Júpiter en su décima casa de carrera y reputación pública. Una ganancia inesperada financiera o un profundo avance emocional podría estar en camino, ayudándole a entrar en su poder personal. Estar abierto a lo inesperado.
Libra (del 22 de septiembre al 22 de octubre)
Sus asociaciones y acuerdos contractuales están bajo el foco de este tránsito, Libra. A medida que el sol energiza su sector de relaciones, se reunirá con Audacy Júpiter en un sextil armonioso. Ya sea amor, negocios o amistades, una nueva conexión podría sentirse destinada. Es un buen momento para trabajar con otros en empresas conjuntas o colaboraciones que contribuyen a su crecimiento personal y profesional.
Scorpio (22 de octubre a Nov. 21)
Sus hábitos de salud pueden mejorar drásticamente bajo esta sinergia empoderadora, Scorpio. Si bien el trabajo y los asuntos de salud están a la vanguardia, el sextil de Sun a Júpiter, activando su sexta casa de bienestar y octava casa de empresas conjuntas, podría inspirarlo con la confianza y la energía que necesita para asumir nuevos desafíos. Tal vez es hora de sacudir su rutina o asumir un nuevo objetivo de salud. Una nueva oportunidad de trabajo o reconocimiento podría llegar a su manera, haciendo que sus esfuerzos se sientan más gratificantes.
Sagitario (22 de noviembre al déco de 21)
Sus jugos creativos fluyen, y eso es un eufemismo, Sagitario. Después de todo, no es todos los días que su gobernante planetario de la suerte, Júpiter, une fuerzas con el sol en su quinta casa de amor, pasión y autoexpresión. Aplastar por alguien especial? Ya sea que esté trabajando en un proyecto de pasión, atrapando sentimientos románticos o entrando en el centro de atención con un esfuerzo creativo, su aura está radiante y está listo para brillar. El amor también podría ser espontáneo y emocionante.
Capricornio (del 21 de diciembre al 19 de enero)
El hogar es donde está tu corazón, entonces, ¿por qué no darle el amor que merece, Capricornio? Con el sol que abarca su cuarta casa doméstica del hogar y la familia, mientras que en el flujo de energía armonioso con Júpiter en su sexta casa de mejora, logística y responsabilidad, puede sentirse llamado a mejorar su espacio vital o crear más espacio para sus pertenencias. Tal vez es hora de redecorar, mudarse a un nuevo espacio o incluso fortalecer los lazos familiares.
Acuario (19 de enero de 18 años)
Usa tus palabras sabiamente: son más poderosos de lo que te das cuenta, Acuario. Con el sol energizando su curiosa tercera casa de comunicación, sus pensamientos e intercambios serán clave durante este tiempo. Aún así, a medida que el Sol se armoniza con Júpiter en su quinta casa de fertilidad y expresión creativa, eres igualmente inspirador y seguro en tu enfoque. Es posible que tenga una conversación esclarecedora que lleva su relación al siguiente nivel o provoca una nueva idea para un proyecto.
Piscis (del 18 de febrero al 20 de marzo)
¡El dinero fluye y la abundancia está llamando, Piscis! A medida que el sol energiza su segunda casa de comodidad, finanzas y valores que busca la estabilidad, se reunirá con Júpiter en su cuarta casa del hogar, la familia y los lazos emocionales. ¿Listo para hacer esa inversión en su espacio vital? Otros podían sentirse llamados para gastar un poco de efectivo extra en una excursión familiar. Confía en tu intuición: la prosperidad podría venir de manera sorprendente.
Noticias
¿Cuál fue el misterioso asistente de Pixie del Pixel 9?

A finales de 2023, escuchamos que Google estaba trabajando en un nuevo asistente digital que evidentemente se planeó debutar en el Pixel 9 en 2024. Evidentemente llamado Pixie, el asistente habría manejado tareas de IA en el dispositivo usando un modelo de Gemini Nano. Pero la IA nunca se materializó. Entonces, ¿qué pasó con Pixie, y lo volveremos a ver?
Bienvenido al compiladorsu resumen semanal de Goings-On. Paso mis días mientras el editor de Google leyendo y escribiendo sobre lo que Google está haciendo a través de Android, Pixel, Gemini y más, y hablo de todo aquí en esta columna. Esto es lo que ha estado en mi mente esta semana.
¿Qué era Pixie?
Según los informes de la información, Pixie estaba destinado a ser un asistente de IA en el dispositivo exclusivo de los teléfonos de Google Pixel, concebido antes de que el chatbot Gemini llegara a la escena (todavía era Google Bard en aquel entonces). Usando un modelo local de Géminis Nano, la IA habría extraído datos de las aplicaciones de Google en su teléfono para ofrecer asistencia más personalizada. La información dijo en 2023 que Pixie podría haber evolucionado “a una versión mucho más personalizada del Asistente de Google”.
Aparentemente, Pixie estaba planeado para asumir las tareas del asistente del Asistente de Google en los teléfonos de Google. Obviamente, eso nunca sucedió, aunque Google ha anunciado oficialmente sus planes para eliminar gradualmente al Asistente Legacy a favor de Gemini en el futuro cercano.
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¿Qué pasó con Pixie?
La serie Pixel 9 aterrizó el año pasado sin mencionar el asistente de Pixie que se rumoreaba el año anterior. El informe posterior de la información (llamado a nuestra atención por 9to5Google) arroja algo de luz sobre lo que pudo haberle sucedido a Pixie.
Según los informes, el CEO de Google, Sundar Pichai, ordenó personalmente un cambio en la estrategia con Pixie para evitar la competencia con el asistente de IA prioritario de Google, Gemini. La marca Pixie parece estar bien y verdaderamente retirada, pero algunas características de Pixie terminaron llegando al Pixel 9. Capturas de pantalla de Pixel, una característica de IA local impulsada por el modelo Gemini Nano XS, evidentemente comenzó su vida como funcionalidad de Pixie. 9to5Google también ha informado que la extensión de servicios públicos de Gemini que permite a la IA controlar directamente la configuración del dispositivo, la reproducción de medios, las alarmas y más, al mismo tiempo, se planeó ser parte de la experiencia de Pixie.
La información ha informado que estas características aparentemente salieron de Pixie no hacen una experiencia tan similar a lo que Pixie habría sido. Sin embargo, es posible que veamos más de lo que Pixie podría haber sido en el futuro.

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¿Podría Pixie regresar?
Con Gemini tan claramente una prioridad para Google, parece poco probable que veamos el lanzamiento de un asistente de inteligencia artificial exclusivo de píxeles que se superpone con el conjunto de funciones de Gemini; de hecho, con Gemini listo para reemplazar el Asistente de Google en la mayoría de los dispositivos en el futuro cercano, Google parece que va en la dirección opuesta. Pero un informe de marzo de Android Authority que hace referencia a “Una fuente dentro de Google” dice que más funcionalidad de Pixie llegará a la serie Pixel 10 en forma de una nueva aplicación llamada Pixel Sense.
Parece que Pixel Sense no competirá directamente con Gemini, sino que intentará proporcionar sugerencias “predictivas” basadas en el contexto de sus aplicaciones de Google conectadas, incluidos Calendar, Chrome, Gmail, Keep, Maps y más. Pixel Sense aparentemente también podrá organizar sus capturas de pantalla en un archivo de búsqueda como lo hace la aplicación de capturas de pantalla Pixel actual, insinuando que puede reemplazar las capturas de pantalla por completo.
Pixel Sense funcionará completamente en el dispositivo; Android Authority cita una fuente diciendo que “sus datos permanecen privados, visibles solo para usted, ni siquiera Google puede verlo”.
Los informes de AA no pintan una imagen completa de cómo funcionará realmente la aplicación Pixel Sense, pero como se describe, parece que podría funcionar de manera similar a la de Google Now, o la función S25 similar de Samsung, ahora breve. Esas características también tienen como objetivo proporcionar información según lo necesite, informado por el contexto de sus cuentas conectadas. Con el acceso a lo que parece esencialmente toda la información almacenada en su cuenta de Google y alimentada por Gemini Nano, Pixel Sense podría hacer un mejor trabajo para ofrecer actualizaciones útiles a medida que las necesita.

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Géminis es tu asistente de IA de Android para lo previsible
El asistente de Pixie probablemente nunca verá la luz del día en su forma inicialmente planificada; Google está poniendo todos sus huevos de asistente en la canasta de Géminis. Pero parece que obtendremos más de la funcionalidad de Pixie en la serie Pixel 10 en forma de la nueva aplicación Pixel Sense. En cuanto a qué es exactamente el sentido de Pixel, tendremos que esperar y ver. Es posible que escuchemos más en Google I/O, que comienza el próximo mes.
Noticias
ChatGPT’s Goodyear 400 Picks & Finishing Order

After correctly predicting Denny Hamlin to win his first race of the NASCAR Cup Series season, we’re turning to AI to help us predict the winner and full finishing order for the Goodyear 400 at Darlington Raceway today at 3 p.m. ET (FS1).
We asked ChatGPT for its NASCAR at Darlington predictions based on historical data, betting odds, and statistical trends – including its pick to win, best prop bet, and favorite long shot, as well as the results for every driver for today’s 38-car field.
Along with our 2025 Goodyear 400 predictions at Darlington, here are our AI-powered NASCAR best bets and full AI-simulated finishing order:
NASCAR AI picks & predictions for Goodyear 400 at Darlington
We’ve previously used ChatGPT to predict its March Madness bracket, Super Bowl picks, and even its Canada vs. USA predictions, and we’re once again turning to OpenAI’s popular chatbot to predict the winner of today’s Goodyear 400 at Darlington.
We trained ChatGPT’s latest and most advanced AI model to study the latest NASCAR odds, betting history, and relevant trends before predicting this weekend’s winner:

ChatGPT’s pick to win Goodyear 400 at Darlington
ChatGPT predicts Denny Hamlin will win the Goodyear 400 at Darlington. His best odds are +800 via BetMGM, which would turn a winning $10 bet into an $80 profit with an implied win probability of 11.11%.
Here’s why the AI model is predicting Hamlin will win today’s race:
Short-track savvy: Hamlin’s long history on short, punishing tracks gives him the experience needed to navigate Darlington’s notorious “Track Too Tough to Tame,” avoiding the mishaps and attrition that often plague less experienced drivers.
Team excellence & strategy: Joe Gibbs Racing consistently fields strong cars at Darlington, and Hamlin’s ability to manage cautions, fuel strategy, and late-race restarts is a big advantage on this challenging circuit.
Proven consistency: Despite the inherent chaos of Darlington, Hamlin’s track record as a veteran who can execute under pressure makes him a standout in a field where even the favorites have slim win probabilities.
AI confidence level:
(15% win probability)
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ChatGPT’s best prop bet for Goodyear 400 at Darlington
ChatGPT predicts Chase Briscoe will finish in the top three as its best NASCAR prop bet for Darlington. Briscoe’s best odds to score a top-3 finish are +550 via bet365, which would turn a winning $10 bet into a $55 profit with an implied win probability of 15.38%.
Here’s why the AI model is predicting Briscoe to finish in the top three today:
Strong starting position & package: Briscoe qualified 4th with Joe Gibbs Racing – a team known for building competitive cars at Darlington – and his starting position puts him right in the mix.
Experience on tough tracks: JGR drivers have a good track record at Darlington, and Briscoe’s aggressive yet measured driving style makes him well-suited to navigate Darlington’s unique challenges.
Ability to capitalize on caution-fueled restarts: Darlington is notorious for its unpredictable cautions and restarts. If the leaders are caught up in strategy or on-track incidents, Briscoe’s aptitude for seizing opportunities could see him climbing into the top three.
AI confidence level:
(25% chance of top-3 finish)
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ChatGPT’s best long shot to win Goodyear 400 at Darlington
ChatGPT predicts Alex Bowman to win the Goodyear 400 as its best long shot at Darlington. His best odds to win are +5000 via Caesars, which would turn a winning $10 bet into a $500 profit with an implied win probability of 1.96%.
Here’s why the AI model is predicting Bowman as its best long shot today:
Undervalued starting position vs. team quality: Although Bowman qualified 33rd, his Hendrick Motorsports car is typically one of the best on the circuit. A deep starting position can mask his true race pace if his team brings a strong setup for Darlington.
Opportunity in Darlington’s chaos: Darlington’s reputation for attrition, unpredictable cautions, and strategic gambles means that drivers starting deep can climb the order dramatically if they avoid early incidents.
Proven ability to overperform: Bowman has shown in past races that he can make up significant ground when conditions favor his driving style. His experience and skill could allow him to exploit a chaotic race and deliver a surprise win.
AI confidence level:
(4% win probability)
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Goodyear 400 AI projections for full finishing order
Here is our predicted Goodyear 400 finishing order and results for every driver based on AI projections. While ChatGPT is a large language model and not specifically designed for predicting sporting events, it can spot trends or insights that NASCAR bettors might overlook.
ChatGPT’s full NASCAR finishing order predictions at Darlington
1. Denny Hamlin (No. 11, Joe Gibbs Racing Toyota) – Using his veteran short‐track savvy and ability to manage fuel and cautions, Hamlin pulls away to take the win.
2. William Byron (No. 24, Hendrick Motorsports Chevrolet) – Byron’s strong car and clean air from starting first help him finish near the front.
3. Chase Briscoe (No. 19, Joe Gibbs Racing Toyota) – A well-handled race by Briscoe capitalizing on strategic restarts secures him a podium finish.
4. Bubba Wallace (No. 23, 23XI Racing Toyota) – Wallace’s aggressive style suits Darlington’s unpredictable nature, keeping him in the top four.
5. Kyle Busch (No. 8, Richard Childress Racing Chevrolet) – Busch’s experience and ability to avoid trouble make him a steady presence in the top five.
6. Joey Logano (No. 22, Team Penske Ford) – Logano overcomes a deeper starting position with a series of strong restarts to climb into the top six.
7. Chase Elliott (No. 9, Hendrick Motorsports Chevrolet) – Despite a mid-pack start, Elliott’s racecraft and Hendrick’s setup allow him to finish strongly.
8. Tyler Reddick (No. 45, 23XI Racing Toyota) – Reddick’s speed and determination help him navigate the short-track mayhem for a top-10 finish.
9. Austin Cindric (No. 2, Team Penske Ford) – Cindric’s consistency and a smart pit strategy keep him in contention among the leaders.
10. Christopher Bell (No. 20, Joe Gibbs Racing Toyota) – Bell capitalizes on clean air and a well-timed move to round out the top 10.
11. Ryan Blaney (No. 12, Team Penske Ford) – Blaney stays in the mix and finishes solidly in the upper group.
12. Kyle Larson (No. 5, Hendrick Motorsports Chevrolet) – Larson’s talent sees him fighting through traffic for a top-12 finish.
13. Todd Gilliland (No. 34, Front Row Motorsports Ford) – In the midst of the short-track chaos, Gilliland manages to keep a respectable position.
14. Ryan Preece (No. 60, RFK Racing Ford) – Preece, starting near the front, is jostled around early and slips to 14th.
15. Michael McDowell (No. 71, Spire Motorsports Chevrolet) – McDowell’s early speed is tempered by the attrition typical of Darlington, landing him mid-pack.
16. Ty Gibbs (No. 54, Joe Gibbs Racing Toyota) – The young gun shows promise but finishes behind the veterans as the race unfolds.
17. Carson Hocevar (No. 77, Spire Motorsports Chevrolet) – Hocevar’s package keeps him in contention, but he ultimately settles in the lower mid-field.
18. Chris Buescher (No. 17, RFK Racing Ford) – Buescher’s RFK setup allows him to cruise steadily, finishing just outside the top 15.
19. Justin Haley (No. 7, Spire Motorsports Chevrolet) – Haley makes a late charge but is held back by traffic, finishing in the upper mid-field.
20. Ross Chastain (No. 1, Trackhouse Racing Chevrolet) – Chastain’s bold moves see him climb significantly – but contact and cautions slow his progress, putting him 20th.
21. Austin Dillon (No. 3, Richard Childress Racing Chevrolet) – Dillon’s car struggles with the track’s relentless demands, dropping him into the lower mid-field.
22. Josh Berry (No. 21, Wood Brothers Racing Ford) – Berry’s knack for close-quarters racing keeps him around the 20th–22nd range.
23. Brad Keselowski (No. 6, RFK Racing Ford) – Keselowski battles through on-track incidents to finish in the low 20s.
24. Zane Smith (No. 38, Front Row Motorsports Ford) – Smith’s less competitive package and a few missteps push him slightly back.
25. A.J. Allmendinger (No. 16, Kaulig Racing Chevrolet) – Allmendinger’s aggressive style yields mixed results, and he ends up mid-pack.
26. Noah Gragson (No. 4, Front Row Motorsports Ford) – Gragson is caught in the frequent Darlington cautions, finishing in the mid-field.
27. John Hunter Nemechek (No. 42, Legacy Motor Club Toyota) – Nemechek’s strategic driving helps him inch forward, but he remains in the lower mid-field.
28. Ricky Stenhouse Jr. (No. 47, Hyak Motorsports Chevrolet) – Stenhouse Jr. is involved in a couple of incidents, dropping him further back.
29. Ty Dillon (No. 10, Kaulig Racing Chevrolet) – With a modest package, Ty Dillon finishes in the latter part of the field.
30. Daniel Suarez (No. 99, Trackhouse Racing Chevrolet) – Suarez’s volatile style sees him struggle with consistency, landing him near the back.
31. Cole Custer (No. 41, Haas Factory Team Ford) – Custer’s car isn’t well-suited for Darlington, resulting in a lower-field finish.
32. Riley Herbst (No. 35, 23XI Racing Toyota) – Inexperience and a lack of track finesse see Herbst fade in the latter half.
33. Alex Bowman (No. 48, Hendrick Motorsports Chevrolet) – Bowman’s pace drops off amid the chaos, and he falls toward the back.
34. Erik Jones (No. 43, Legacy Motor Club Toyota) – Jones is hampered by on-track contact and ends up further down the order.
35. Cody Ware (No. 51, Rick Ware Racing Ford) – With one of the least competitive packages, Ware is forced into a deep back finish.
36. Shane van Gisbergen (No. 88, Trackhouse Racing Chevrolet) – The international star struggles to adapt to Darlington’s brutal demands and falls out of contention.
37. Austin Hill (No. 33, Richard Childress Racing Chevrolet) – Hill’s inexperience on this demanding track sends him near the rear.
38. J.J. Yeley (No. 44, NY Racing Team Chevrolet) – Yeley’s limited Cup experience sees him finish in the final stretch.
NASCAR best bets for Goodyear 400 at Darlington
Bet | Driver | Odds | Implied win probability |
---|---|---|---|
Denny Hamlin | +800 | 11.11% | |
Chase Briscoe (top-3) | +550 | 15.38% | |
Alex Bowman | +5000 | 1.96% |
How to watch the 2025 Goodyear 400 at Darlington
Race date: Sunday, April 6
Start time: 3 p.m. ET
Track: Darlington Raceway (Darlington, S.C.)
TV: FS1 | Streaming: Fox Sports App
Best NASCAR betting sites for Goodyear 400 at Darlington
Looking to bet on the Goodyear 400 at Darlington Raceway? Here are our top-rated NASCAR best sports betting sites as determined by our expert team at Sportsbook Review, along with our best sportsbook promos ahead of today’s race at 3 p.m. ET.
(21+. Gambling Problem? Call 1-800-GAMBLER)
* Bonuses not applicable in Ontario.
Not intended for use in MA.
Each betting site featured on SBR has been meticulously researched and selected by our team of experts. If you sign up through our links, we may get a commission.
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