ChatGPT Can’t Keep Up: Google Handles 373x More Traffic and Keeps Growing

According to new analysis by SparkToro, AI chatbots and search engines are popular, but they are still unable to beat traditional Google search. The analysis found that Google Search saw 373 times more traffic than ChatGPT and its traffic has also increased year-over-year. Many users, marketers, and other analysts claimed that AI chatbots and search engines are competing with Google Search, but the research shows that Google is still very much dominant.

The research also shows that ChatGPT’s market share will still be less than 1% if it is receiving 1 billion search-related queries daily. Semrush study found that only 30% of queries on ChatGPT fall in the traditional search category, while ChatGPT only uses search for 46% of queries. Google gets about 14 billion queries per day, which makes its market share 93.57%. According to Google, it saw more than 5 trillion searches in 2024. ChatGPT has 0.25% market share with 37.5% traditional search related queries per day. Yahoo has 1.35% market share, Microsoft Bing 4.10% share, and DuckDuckGo has 0.73% market share in the search market, which shows that AI chatbots like ChatGPT are still much behind.

• Also read: AI Search Is Lying to You, And It’s Getting Worse

According to data by Datos, there was a 21.64% increase in Google search from 2023 to 2024. Google’s CEO, Sundar Pichai, also says that one of the reasons why Google is seeing a surge in searches is because of AI Overviews, as many users are using this feature. Even though many users are still using Google, it doesn't mean that websites are getting more traffic or clicks. The analysis also found that 60% of the Google searches didn't end up with a click on a website, which makes about 3 trillion searches in 2024 without any clicks.

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by Arooj Ahmed via Digital Information World

New Report Found that Only 4% of the Global Populations Hold a Bitcoin

According to a new report from a BTC financial services company called River, only 4% of the world population holds a Bitcoin despite its growing popularity. In the US, 14% of the individuals hold a Bitcoin, which makes America the top country with the highest concentration of Bitcoin ownership. America also has the highest adoption rate for Bitcoin currency, while the country with the lowest adoption rate for Bitcoin is Africa at 1.6%. The study also highlights that Bitcoin constitutes 0.2% of global wealth. Its total addressable market is estimated at $225 trillion, assuming it captures 50% of store-of-value assets.

The report by River says that Bitcoin has only achieved 3% of its maximum adoption potential, which means that its adoption is still at early stages. Developed countries are more open to using Bitcoins than developing countries. The 3% metric was calculated by analyzing individual as well as institutional ownership. Bitcoin also became a US government reserve asset, but there are still a lot of hurdles that are on the way of Bitcoin mass adoption globally.

The things which are stopping Bitcoin’s mass adoption are technical and financial education. There are a lot of misconceptions about Bitcoin and most people think of it as a Ponzi Scheme or scam. Digital currencies are highly volatile, which is good for short-term traders but isn't that good for daily transactions. The high volatility rates affect the developing countries the hardest, and they have to turn to the US dollar stablecoins for lower transaction fees and stability.

Read next: AI Search Is Lying to You, And It’s Getting Worse
by Arooj Ahmed via Digital Information World

AI Search Is Lying to You, And It’s Getting Worse

Facts matter. Trust matters. But in the race to reinvent search, both are getting trampled. A recent Columbia Journalism Review study reveals a hard truth — machines, built to deliver answers in an instant, are often serving up fiction with a straight face. Instead of guiding users to reliable sources, search engines now deal in confidence, not accuracy, replacing verifiable facts with AI-generated guesswork. The promise was a smarter way to find information; the reality is a flood of misinformation, dressed up as truth, delivered without a second thought.

The study highlights a growing issue with AI search tools scraping online content to generate responses. Instead of directing users to the original sources, these systems often provide instant answers, significantly reducing website traffic. A separate, unrelated study also found that click-through rates from AI-generated search results and chatbots were substantially lower than those from Google Search. The situation becomes even more problematic when these AI tools fabricate citations, misleading users by linking to non-existent or broken URLs.

An analysis of multiple AI search models found that over half of the citations generated by Google’s Gemini and xAI’s Grok 3 led to fabricated or inaccessible webpages. More broadly, chatbots were found to deliver incorrect information in more than 60% of cases. Among the evaluated models, Grok 3 had the highest error rate, with 94% of its responses containing inaccuracies. Gemini fared slightly better but only provided a fully correct answer once in ten attempts. Perplexity, though the most accurate of the models tested, still returned incorrect responses 37% of the time.

The study’s authors noted that multiple AI models appeared to disregard the Robot Exclusion Protocol, a standard that allows websites to restrict automated content scraping. This disregard raises ethical concerns about how AI search engines collect and repurpose online information. Their findings align with a previous study published in November 2024 that examined ChatGPT’s search capabilities, revealing consistent patterns of confident but incorrect responses, misleading citations, and unreliable information retrieval.

Experts have warned that generative AI models pose significant risks to information transparency and media credibility. Critics such as Chirag Shah and Emily M. Bender have raised concerns that AI search engines remove user agency, amplify bias in information access, and frequently present misleading or toxic answers that users may accept without question.

The study analyzed 1,600 queries to compare how different generative AI search models retrieved article details such as headlines, publishers, publication dates, and URLs. The evaluation included ChatGPT Search, Microsoft CoPilot, DeepSeek Search, Perplexity along with its Pro version, xAI’s Grok-2 and Grok-3 Search, and Google Gemini. The models were tested using direct excerpts from ten randomly selected articles sourced from 20 different publishers. The results underscore a significant challenge for AI-driven search, showing that despite their growing integration into digital platforms, these tools still struggle with accuracy and citation reliability.

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• Engagement Trends Show Threads Growing, X’s Virality Strength, and Bluesky’s Slowdown
by Arooj Ahmed via Digital Information World

Engagement Trends Show Threads Growing, X’s Virality Strength, and Bluesky’s Slowdown

BufferApp analyzed 1.7 million posts from X, Threads, and Bluesky and found that these three platforms have a common median engagement rate, that is four interactions per post. This may tell us that these platforms are similar in terms of engagements, but that isn’t the case because they have different patterns, dynamics, audience behavior, and consistency when it comes to posts. A data scientist for Buffer analyzed posts from 56,000 users to see the trends on X, Threads, and Bluesky. Engagements mean the total number of reactions a post receives, which can include likes, comments, and reposts.

The study highlights that posts on Threads have higher engagement, but some data shows that posts on X have as many engagements as posts on Threads. Engagement rate means percentage of people who interact with a post (i.e. like, comment etc.), while total engagements count all the interactions. It is important to know engagement rate on a post if you want to see how much a post can engage the audience, while total engagements can tell the overall interaction on the platform.

In 2024, the posts on X, Threads and Bluesky had the same number of engagements, with a median of four engagements per post. But if we look at February 2025 data, we get to know that posts on Threads received a median 5 engagements, X remained at 4 engagements, while engagements on Bluesky reduced to 3. This may not seem like much of a difference, but this shows that each platform is developing its own distinct identities.

Median engagements show how a post performs, but it doesn’t show any viral content. The gap between median and average engagement shows if a post has gone viral. X gets 328 average engagements, Threads get 58, and Bluesky gets 21 average engagements on posts. The standard deviation on X gets more than 5,000, which means that it is highly unpredictable, while Threads and Bluesky have lower engagements, but they are consistent. If a platform has high standard deviation, it means that it has great viral potential, while lower standard deviation means predictable engagements.

Because of all these factors, X is the platform with the most viral potential. Even though posts on X have a median four engagements, a post can go to extreme levels of virality if it takes off. Threads has moderate engagement, but it is stabilizing quickly. The potential to go viral on Threads is random, but it has steadier audience growth. Bluesky has a small engagement spread and it is more community driven than viral reach.

Read next: Even with Reduced Expectations for Ratings, Consumers Actively Contribute Reviews on Google and Social Media
by Arooj Ahmed via Digital Information World

OpenAI is Rolling Out New Responses API Tool That Can Search Through Large Volumes of Online Data

The future of AI includes chatbots or agents, and that’s why the makers of ChatGPT are trying their best to assist developers design one of their own.

The organization is releasing a New Responses API tool that offers building blocks so that developers can benefit. In other words, it's saying hello to agents that can go through huge volumes of online data while carrying out numerous tasks on the PC, just so the user does not need to.

As per the head of Deep Research and Operator, some agents the company can design themselves, but knowing that the internet is so complex, many industries and use cases require a foundation. Based on that, developers can design efficient agents as per their needs.

The new tool will be built into web search on the same exact model that ChatGPT utilizes when searching files. This gives developers the chance to get data in real-time and citations from the internet while utilizing GPT-4o and 4o mini. It also entails another feature for use on computers only, like its own Operation model, so users can allow it to perform tasks on their behalf.

The goal here is to provide assistance to agents working to provide the best customer support. They can go through FAQs or even serve to find age-old cases if working as a legal agent.

In other news, the AI giant shared its Agents SDK, which it calls a means for developers to display the AI agents workflow. Several of these agents can work as a unit to solve even the most difficult tasks. This should make it so much simpler for developers to manage agents and make sure they are working to a single goal.

The launch of the latest Responses API and Agents SDK is built on previous tools that the company rolled out to developers. Common examples include Chat Completions API. This provides developers the chance to design AI tools that provide replies to user queries. In the same way, the company is making plans to get rid of the Assistants API with this latest invention by the middle of next year. As per OpenAI, it’s added plenty of key improvements into it, after considering feedback from developers.

Read next: Hidden Threat: Even One Breath in These Cities Could Be Life-Threatening
by Dr. Hura Anwar via Digital Information World

Hidden Threat: Even One Breath in These Cities Could Be Life-Threatening

New report reveals that nearly every country in the world has air dirtier than what is recommended by doctors for breathing, with only seven countries meeting WHO’s guidelines for tiny toxic particles which are also known as PM2.5.

IQAir, a Swiss air quality technology company, found that New Zealand, Australia, Estonia, Iceland, and some other small island states are the only ones which have no more than 5 micrograms of tiny toxic particles per cubic meter (μg/m³).

IQAir also named the most polluted countries in the world with Bangladesh, Chad, Pakistan, India, and Congo being among the top five. In these countries, the PM2.5 levels are 10 times higher than the guidelines in 2024. Chad has about 18 times more PM2.5 levels than average levels in the guidelines.

Doctors say that there are no set levels of PM2.5 to determine if it's safe or not because once they enter the bloodstream, they can damage our organs, which can lead to ultimate death. The number one leading cause of death in the world is high blood pressure, while the second is dirty air or air pollution. The CEO of IQAir says that air pollution takes two to three decades to show its effects, which can impact our health dangerously. That's the reason most people don't take dirty air seriously and when they see the consequences, it's too late.

The report also showed the PM2.5 improvements seen in some countries, with some even improving in PM2.5 standards by 7% in 2023 to 17% in 2024. India showed a 7% improvement in PM2.5 levels between 2023 and 2024 and it is home to six of the ten dirtiest cities in the world. China also saw some improvement in its air quality, and the air quality in Beijing became the same as Sarajevo.

Here's the full list:

Rank	Country/Region	2024 PM2.5 (μg/m³)	2023 PM2.5 (μg/m³)	2022 PM2.5 (μg/m³)	Population
1	Chad	91.8	--	89.7	17,179,740
2	Bangladesh	78	79.9	65.8	169,356,251
3	Pakistan	73.7	73.7	70.9	231,402,117
4	DR Congo	58.2	40.8	15.5	95,894,118
5	India	50.6	54.4	53.3	1,407,563,842
6	Tajikistan	46.3	49	46	9,750,064
7	Nepal	42.8	42.4	40.1	30,034,989
8	Uganda	41	27.3	39.6	45,853,778
9	Rwanda	40.8	36.8	44	13,461,888
10	Burundi	40.3	--	--	14,047,800
11	Nigeria	40.1	23.9	36.9	213,401,323
12	Egypt	39.8	42.4	46.5	109,262,178
13	Iraq	38.4	43.8	80.1	43,533,592
14	Ghana	35.8	33.2	30.2	32,833,031
15	Indonesia	35.5	37.1	30.4	273,753,191
16	Gambia	35.2	28.5	--	2,639,916
17	United Arab Emirates	33.7	43	45.9	9,365,145
18	Bahrain	31.8	39.2	66.6	1,463,265
19	Uzbekistan	31.4	28.6	33.5	34,915,100
20	Qatar	31.3	37.6	42.5	2,688,235
21	China	31	32.5	30.6	1,412,360,000
22	Kuwait	30.2	39.9	55.8	4,250,114
23	Vietnam	28.7	29.6	27.2	97,468,029
24	Cameroon	27.6	24	--	27,198,628
25	Laos	27.5	29.6	27.6	7,275,556
26	Turkmenistan	26.5	--	21.6	6,341,855
27	Togo	26	16.3	--	8,644,829
28	Mongolia	25.6	22.5	29.5	3,347,782
29	Bosnia Herzegovina	25.3	27.5	33.6	3,270,943
30	Myanmar	25.2	28.2	24.3	53,798,084
31	Saudi Arabia	25.1	26.5	41.5	35,950,396
32	Zimbabwe	24.8	33.3	--	15,993,524
33	Ivory Coast	24.6	16.6	22.5	29,389,150
34	Armenia	24.4	26.4	31.4	2,790,974
35	North Macedonia	23.3	25.2	25.6	2,065,092
36	Libya	22.3	30.4	--	6,735,277
37	Senegal	22.3	28.2	20.4	16,876,720
38	Ethiopia	22.2	27	31.3	120,283,026
39	Zambia	22	24.1	24.6	19,473,125
40	Cambodia	21.9	22.8	8.3	16,589,023
41	Kyrgyzstan	21.1	33.1	31.1	6,691,800
42	Palestine	21.1	18.6	--	3,000,021
43	Madagascar	20.5	20.6	23.7	28,915,653
44	Serbia	20.2	20.5	24.7	6,834,326
45	Thailand	19.8	23.3	18.1	71,601,103
46	Guatemala	18.8	18.7	18.6	17,109,746
47	South Africa	18.8	19.9	23.4	59,392,255
48	Malaysia	18.3	22.5	17.7	33,573,874
49	Azerbaijan	18.3	18.8	18.9	10,137,750
50	Montenegro	18	21.3	15.7	619,211
51	Sri Lanka	17.9	19.3	20.7	22,156,000
52	Macao SAR	17.7	16.2	15.4	686,607
53	Guyana	17.5	17.1	12.6	804,567
54	Taiwan	17.5	20.2	13.4	23,816,775
55	Mexico	17.4	20.1	19.5	126,705,138
56	El Salvador	17.3	19.5	14.2	6,314,167
57	Israel	17.2	17.8	18.8	9,364,000
58	Peru	17.1	18.8	23.5	33,715,471
59	South Korea	17	19.2	18.3	51,744,876
60	Djibouti	16.8	--	--	1,168,720
61	Mozambique	16.7	--	--	34,631,800
62	Chile	16.6	18.8	22.2	19,493,184
63	Hong Kong SAR	16.3	15.6	14.5	7,413,100
64	Paraguay	15.9	--	--	6,929,150
65	Algeria	15.4	13.8	17.8	44,177,969
66	Romania	15.3	15.7	17.2	19,119,880
67	Turkey	15.3	20.3	21.1	84,775,404
68	Slovenia	15.2	14.9	15.1	2,108,079
69	Honduras	15.2	15.1	10.2	10,278,345
70	Gabon	15.2	16.9	25	2,341,179
71	Kazakhstan	15.1	22.2	23	19,000,988
72	Georgia	15.1	16.4	17	3,708,610
73	Brazil	14.9	12.6	12.2	214,326,223
74	Philippines	14.8	13.5	14.9	113,880,328
75	Poland	14.8	14.1	16.3	37,747,124
76	Nicaragua	14.8	15.7	8.9	6,850,540
77	Moldova	14.7	15.7	22.6	2,615,199
78	Albania	14.5	16.7	14.5	2,811,666
79	Kenya	14.3	10.6	11.5	53,005,614
80	Italy	14.2	15	18.9	59,109,668
81	Croatia	13.8	13.8	23.5	3,899,000
82	Colombia	13.8	14.1	15.7	51,516,562
83	Slovakia	13.6	13.1	14.5	5,447,247
84	Hungary	12.9	12	12.6	9,709,891
85	Kosovo	12.9	12.1	14.7	1,786,038
86	Cyprus	12.8	14.3	15.6	1,244,188
87	Czech Republic	12.7	11.5	13.4	10,505,772
88	Suriname	12.5	10.6	7	612,985
89	Bulgaria	12.4	13.2	18.3	6,877,743
90	Maldives	12.2	15.3	10.9	521,457
91	Malta	11.7	12	11.7	518,536
92	Greece	11.5	17.4	19	10,641,221
93	Uruguay	11.5	11.7	11.3	3,426,260
94	Singapore	11.4	13.4	13.3	5,453,566
95	Austria	10.2	9.6	10.6	8,955,797
96	Lithuania	10.1	10.4	13.2	2,800,839
97	Panama	10.1	9.6	9	4,351,267
98	Bolivia	10	12.6	7.3	12,079,472
99	Russia	9.8	10	11.2	143,449,286
100	Ecuador	9.7	7.4	--	17,797,737
101	Ukraine	9.2	8.6	9.7	43,792,855
102	Latvia	9.2	8	10.1	1,884,490
103	Germany	9	9	11	83,196,078
104	Netherlands	8.9	8.7	11	17,533,044
105	Belgium	8.9	9.4	10.8	11,592,952
106	Argentina	8.7	9.2	7.7	45,808,747
107	Spain	8.7	9.9	10.9	47,415,750
108	Japan	8.6	9.6	9.1	125,681,593
109	Cayman Islands	8.5	--	--	74,457
110	France	8.1	9.5	11.5	67,749,632
111	Luxembourg	7.5	8.9	7.4	640,064
112	Anguilla	7.4	7.4	--	15,094
113	United Kingdom	7.4	7.7	8.9	67,326,569
114	Switzerland	7.3	8.9	10	8,703,405
115	Denmark	7.2	7.7	8.6	5,856,733
116	USA	7.1	9.1	8.9	331,893,745
117	Costa Rica	7	6.1	7.9	5,153,957
118	Portugal	6.8	6.8	8.1	10,325,147
119	Canada	6.7	10.3	7.4	38,246,108
120	Norway	6.6	6.3	7	5,408,320
121	Liechtenstein	6.5	7.2	8.3	39,039
122	Ireland	6.5	6.3	7.5	5,033,165
123	Trinidad and Tobago	6.1	5.8	5.1	1,525,663
124	Andorra	6	7.9	5.4	79,034
125	Sweden	5.3	5.1	6.2	10,415,811
126	Finland	5.2	4.9	5	5,541,017
127	Estonia	4.6	4.7	4.9	1,330,932
128	Australia	4.5	4.5	4.2	25,688,079
129	New Zealand	4.4	4.3	4.8	5,122,600
130	Iceland	4	4	3.4	372,520
131	Grenada	3.2	4.1	3.8	124,610
132	Barbados	3.1	--	--	282,467
133	Montserrat	2.7	--	--	4,389
134	Puerto Rico	2.7	4.5	4.3	3,263,584
135	U.S. Virgin Islands	2.6	--	2.9	105,870
136	French Polynesia	2.5	3.2	2.5	304,032
137	Bermuda	2.5	4.1	3	63,867
138	Bahamas	2.3	5.2	--	407,906
Note:	0-5: Meets WHO guideline

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by Arooj Ahmed via Digital Information World

Americans Waste 2 Hours Daily on Phones, Here’s What’s Stealing Their Focus!

Clarify Capital conducted a survey to find out what are the top distractions on phones for people during work hours. 1,000 employed Americans were surveyed and it was found that 3 in 4 respondents spend an average 2 hours a day working from their phones. 65% of the workers said that the top phone distraction during work hours is sending text messages while 53% said that it's browsing social media. 45% of the respondents said that they get distracted by browsing the internet for non-work-related content during work. Other phone distractions by the respondents were listening to music/podcast (44%) and making personal calls (42%).

Respondents were also asked what social media apps are the most distracting at work, and 32% named Facebook as the most distracting app. Other distracting social media apps for respondents during work were Instagram (32%), YouTube (27%), Reddit (27%) and TikTok (23%). The survey also found that 1 in 4 Americans get distracted by personal notifications at work, with iPhone users being 10% more likely to get distracted.

The survey also asked respondents what strategies they apply to manage their notification distractions during work hours. Most of the respondents (43%) said that they turn off their notifications to silent or vibrate mode. 33% use Focus Mode/Do Not Disturb mode while 32% turn off their notifications for specific apps. 25% said that they have started checking their notifications only at specific hours while 13% said that they yage scheduled their focus periods where they just focus on work without phone use.

Take a look at the charts below for more insights:

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by Arooj Ahmed via Digital Information World