AI Research
Deep Research And More From New AI Models
Young vocalist with a microphone singing on stage
Did you hate these annoying songs? Check out what new deep research AI tools can do.
It’s all well and good to tell people to get on ChatGPT, or Claude, or Gemini, and get their hands dirty using an AI model – but some people want more detail. They want to know how to use the best new tools in a more granular way.
Deep research is one of the newest and most impressive functions of today’s large language models. But most people haven’t heard of it yet, let alone used it.
Ethan Mollick is someone I associate with the vanguard of AI research. With former MIT connections, Mollick represents a leading voice in this field. So I was reading his latest blog post about different models, and how to use them.
Mollick breaks down the most useful models for Claud, ChatGPT and Google in the following categories: “chat,” “work” and “hard problems.”
Claude, he notes, is generally better for privacy, where you need to select for this in ChatGPT, and may compromise some functionality with Google.
Then Mollick embarks on a description of deep research capabilities that are baked into some of the newest models, but not yet available for free. This is so new that it’s still paywalled and rationed for paying users.
The Promise of Deep Research Functionality
Deep research is available from OpenAI, and from Google, and from Anthropic, too.
When you look at the unveiling of the research function, you see a lot of reports around February.
So how do you access this kind of result?
I found a recent post about this from the “OpenAI cookbook” in June, where the poster talks about downloading a Python SDK, and API work.
It turns out, though, that you can select “deep research” in the prompt section of ChatGPT 4.5, if you are a paying Plus or Pro user, and get your deep research report that way.
Asking for Deep Research from ChatGPT 4.5
I wanted to try this for myself, so based on what I was experiencing in the moment, I centered on compiling a report of the most annoying songs from 1990 through 2025.
My query: “please create a report on the most annoying music from 1990 until 2025, segmented annually.”
Simple, right?
First, ChatGPT asked me to clarify a few basic questions:
“To create a detailed report on the ‘most annoying music’ from 1990 to 2025, could you please clarify a few things:
- What do you mean by ‘annoying’? Are you referring to songs widely criticized, overplayed, polarizing, or based on listener polls or memes?
- Should the list be global or focus on a specific region (e.g., U.S., UK, etc.)?
- Do you want one song per year, or multiple per year if applicable?
- Would you like the report to include explanations or cultural context for each selection?”
And I answered:
“1. all of the above 2. US-based 3. multiple songs per year 4. Yes.”
“Thanks!” GPT responded. “I’ll compile a U.S.-focused report covering the most widely criticized, overplayed, or culturally mocked songs from 1990 to 2025. This will include multiple songs per year, along with context explaining why they were considered annoying—drawing from music critics, listener polls, memes, radio overplay complaints, and public opinion.”
Then the report started. Indeed, it took more than 10 minutes to compile, with CoT displays like “searching ‘Rico Suave’: worst song’ and ‘looking for Macarena.’
Check Out GPT’s Chain of Thought
Actually, here are some of the interesting CoT notes the system provided while doing research on dozens of sites (it spent a lot of time reading the Guardian!)
I’m mapping out recent headlines and thought pieces, like those from Global News and the Guardian, all noting “Baby Shark” as the most annoying song. Hm, that’s interesting.
I’m preparing the list of most annoying songs from 1993-1995, considering “What’s Up,” “Cotton Eye Joe,” “Macarena,” and “Wonderwall.” However, I’m thinking through the inclusion of other notable tracks like “Rico Suave” for earlier years.
(There’s that ‘Rico Suave’ song again.)
I’m mapping out the hits and misses of 2001-2006, with 2005’s viral sensations like “Crazy Frog” and “Photograph” by Nickelback taking center stage. Hm, that’s interesting—these tracks seem to evoke strong reactions.
(Note how the model is actually ‘taking notes’ while researching, giving us little bits of personality along with the actual meat of the report.)
Here’s a funny comparison:
I’m comparing the impact of Nickelback’s “Rockstar” in 2006 to Soulja Boy’s “Crank That” in 2007. The former was widely criticized, while the latter was loved by many, raising questions about musical taste over time.
A research-based note:
I’m digging through songs by genre and year, considering widespread opinion and specific examples to select the most frequently labeled annoying tracks.
I’m evaluating hits like Meghan Trainor’s “All About That Bass” (2014) and Tyga’s “Stimulated” (2015) for their ubiquity and frequency of being labeled annoying. Progressing through last five years gradually.
I’m digging through songs by genre and year, considering widespread opinion and specific examples to select the most frequently labeled annoying tracks.
I’m evaluating hits like Meghan Trainor’s “All About That Bass” (2014) and Tyga’s “Stimulated” (2015) for their ubiquity and frequency of being labeled annoying. Progressing through last five years gradually.
And then there was this:
“Noticing the challenge of time limitations, I’m figuring out how to balance inclusion of pivotal examples against the need for conciseness.”
So the model is actually trying to triage its work. This is next-level stuff.
Finally, GPT appeared to wrap up, dotting its I’s and crossing its Ts:
“I’m checking to see if the ‘Achy Breaky’ song contains any references to anthropomorphic sharks.”
Eventually, I got the full report. It ranges dozens of pages, and provides great coverage of over a quarter century of horrible, terrible music. A few standouts, with years:
1990
- “Ice Ice Baby” – Vanilla Ice (1990): This hip-hop crossover hit became a pop culture punchline due to its cheesy lyrics and ubiquitous play. While it was initially a chart-topping success, critics later lambasted it; Spinner magazine ranked it the second-worst song in history, and Blender ranked it fifthen.wikipedia.org. A Houston Press critic even dubbed it the worst song ever by a Texas artist, claiming it “set back the cause of white people in hip-hop by a decade”en.wikipedia.org. Its overexposure turned the once-popular anthem into an annoyance for many listenerscraftyourhappyplace.com.
- “Rico Suave” – Gerardo (1990): A bilingual novelty rap track that peaked in 1991, “Rico Suave” was a one-hit wonder often cited on “worst song” lists. Its goofy, exaggerated Latin lover persona and repetitive chorus wore thin quickly. Blender magazine’s infamous list of the “50 Worst Songs Ever” placed “Rico Suave” among its ranks (in the high 30s), and retrospective pop culture forums frequently hold it up as an example of early ’90s pop cringeculturecrossfire.comforums.thefashionspot.com. The song’s catchphrase chorus (and its shirtless music video) made it memorable, but also widely mocked in the years since.
1994
- “Cotton Eye Joe” – Rednex (1994): This techno-hoedown remix of an old folk tune became a staple at sports games and parties – much to many people’s annoyance. With its frenetic fiddle loops and the grating “If it hadn’t been for Cotton Eye Joe” chorus, the track wears thin fast. CBS News put it at #1 on a “Worst Songs from Jock Jams” list, calling it an “unholy hybrid of country-fried fiddle kitsch and relentless hi-NRG drums”en.wikipedia.org. A Toronto Star critic dubbed it the worst song of the ’90s, describing it as “straight drivel”en.wikipedia.org. Even wrestler-turned-rocker Chris Jericho said it’s one of his least favorite songs ever – naming it alongside “Mambo No. 5” on his personal worst listen.wikipedia.org. Despite its party ubiquity, few ever actually wanted an “EDM-hillbilly” mashup like thisen.wikipedia.org.
1997
- “Barbie Girl” – Aqua (1997): This novelty dance-pop hit was simultaneously a global chart-topper and a critical punching bag. Its bouncy, plastic sound and exaggerated vocals (“Come on Barbie, let’s go party!”) made it a staple of late-’90s radio and kids’ parties, but also an irritant to many. Rolling Stone readers voted it the worst song of the ’90sen.wikipedia.org, and NME put it atop their worst-single list as wellen.wikipedia.org. While Aqua intended it as satire, the song was widely mocked; it even sparked a lawsuit from Mattel for tarnishing the Barbie brand. Decades later, it remains a guilty pleasure earworm that routinely tops “most annoying” song listscraftyourhappyplace.com.
Fast-forward a bit:
2013
- “The Fox (What Does the Fox Say?)” – Ylvis (2013): An absurdist novelty song by a Norwegian comedy duo, “The Fox” unexpectedly became a global viral hit in late 2013. Its EDM-lite beat and litany of animal sounds (ring-ding-ding, wa-pa-pa-pow!) made it the “Macarena” of the YouTube era – funny at first, then torturous on repeat. Kids loved the silliness, while parents and many others quickly tired of it. The song was essentially one long joke about animal noises, and once the joke wore off, what remained was an insanely repetitive chorus of nonsense. Rolling Stone readers later voted it one of the most annoying songs of the 2010s, and USA Today called it “the year’s most aggravating meme song.” Like other meme hits, it got played at school events and even on radio as a curiosity, amplifying the annoyance for those who never wanted to hear “Hatee-hatee-hatee-ho!” again. Ylvis intended it as a parody of EDM pop – ironically achieving exactly the kind of ubiquitous earworm they were spoofing, to the annoyance of millions.
- “Baby Shark” (global viral hit – charted 2018/2019): {See 2016 above.} In 2018, “Baby Shark” finally chomped its way onto international music charts (hitting the US Top 40 by early 2019)theguardian.comtheguardian.com. This was the year the song truly went pandemic, crossing from kids’ YouTube into mainstream consciousness. By the fall of 2018, articles were calling it “the year’s most annoying song” outrighttheguardian.com. Parents everywhere pleaded for mercy from the doo-doo-doos. The Guardian wryly noted how even people who hadn’t heard it knew of it through cultural osmosis – often via tales of toddlers playing it on endless repeat, driving their families madtheguardian.com. Once it hit the radio and dance remixes appeared, there was no escape. “Baby Shark” in 2018 exemplified how a simple children’s ditty could unite millions in shared, head-throbbing frustration. (Fun fact: that year a viral social post joked that the Geneva Convention should classify repeated “Baby Shark” play as a form of torture – a sentiment many a worn-out babysitter would second.)globalnews.catheguardian.com
2023
- “Peaches” – Jack Black (Bowser) (2023): Featured in The Super Mario Bros. Movie, this over-the-top piano ballad sung by Jack Black (in character as Bowser) became a TikTok smash and even entered the Billboard Hot 100. It’s intentionally comedic – Bowser professing love for Princess Peach with a dramatic “Peaches, Peaches, Peaches” chorus. Kids and gamers found it hilarious and catchy; others quickly found it grating (especially if they hadn’t seen the movie context). The chorus is essentially one word (peaches) repeated incessantly with operatic bombast. Within weeks, clips of people annoyed by their friends or children endlessly singing “Peaches” popped up online. Though short, the song’s earworm power is strong. One AV Club article quipped that it’s the kind of novelty song “destined to annoy parents everywhere while their offspring replay it 1,000 times.” It’s a fresh example of how a song can go viral and charm its way into your brain – and then, thanks to sheer overuse, make you never want to hear the word “peach” again (sorry, Georgia).
Some Thoughts on Voice Mode
Let’s move back to the blog post: after delving into the above stuff, Mollick also covers the verbal capabilities of new systems that really change the interface. As he notes, you can talk to these models while you’re in the car, or on a walk, or anywhere that you have the space and time.
I think verbal communication is going to finally make LLM responses “real” to us. And that’s all I have to say about that – for now.
I think all of this is deeply informing to people who want to be practical about LLM use. The deep research function in particular is something people will be learning about as time goes on. How many of us will figure it out before something new comes along that makes it obsolete?
In any case, this was an excellent guide to how to make the most of AI in today’s environment.
AI Research
Political attitudes shape public perceptions of artificial intelligence
AI Research
Space technology: Lithuania’s promising space start-ups
Technology Reporter
I’m led through a series of concrete corridors at Vilnius University, Lithuania; the murals give a Soviet-era vibe, and it seems an unlikely location for a high-tech lab working on a laser communication system.
But that’s where you’ll find the headquarters of Astrolight, a six-year-old Lithuanian space-tech start-up that has just raised €2.8m ($2.3m; £2.4m) to build what it calls an “optical data highway”.
You could think of the tech as invisible internet cables, designed to link up satellites with Earth.
With 70,000 satellites expected to launch in the next five years, it’s a market with a lot of potential.
The company hopes to be part of a shift from traditional radio frequency-based communication, to faster, more secure and higher-bandwidth laser technology.
Astrolight’s space laser technology could have defence applications as well, which is timely given Russia’s current aggressive attitude towards its neighbours.
Astrolight is already part of Nato’s Diana project (Defence Innovation Accelerator for the North Atlantic), an incubator, set up in 2023 to apply civilian technology to defence challenges.
In Astrolight’s case, Nato is keen to leverage its fast, hack-proof laser communications to transmit crucial intelligence in defence operations – something the Lithuanian Navy is already doing.
It approached Astrolight three years ago looking for a laser that would allow ships to communicate during radio silence.
“So we said, ‘all right – we know how to do it for space. It looks like we can do it also for terrestrial applications’,” recalls Astrolight co-founder and CEO Laurynas Maciulis, who’s based in Lithuania’s capital, Vilnius.
For the military his company’s tech is attractive, as the laser system is difficult to intercept or jam.
It’s also about “low detectability”, Mr Maciulis adds:
“If you turn on your radio transmitter in Ukraine, you’re immediately becoming a target, because it’s easy to track. So with this technology, because the information travels in a very narrow laser beam, it’s very difficult to detect.”
Worth about £2.5bn, Lithuania’s defence budget is small when you compare it to larger countries like the UK, which spends around £54bn a year.
But if you look at defence spending as a percentage of GDP, then Lithuania is spending more than many bigger countries.
Around 3% of its GDP is spent on defence, and that’s set to rise to 5.5%. By comparison, UK defence spending is worth 2.5% of GDP.
Recognised for its strength in niche technologies like Astrolight’s lasers, 30% of Lithuania’s space projects have received EU funding, compared with the EU national average of 17%.
“Space technology is rapidly becoming an increasingly integrated element of Lithuania’s broader defence and resilience strategy,” says Invest Lithuania’s Šarūnas Genys, who is the body’s head of manufacturing sector, and defence sector expert.
Space tech can often have civilian and military uses.
Mr Genys gives the example of Lithuanian life sciences firm Delta Biosciences, which is preparing a mission to the International Space Station to test radiation-resistant medical compounds.
“While developed for spaceflight, these innovations could also support special operations forces operating in high-radiation environments,” he says.
He adds that Vilnius-based Kongsberg NanoAvionics has secured a major contract to manufacture hundreds of satellites.
“While primarily commercial, such infrastructure has inherent dual-use potential supporting encrypted communications and real-time intelligence, surveillance, and reconnaissance across NATO’s eastern flank,” says Mr Genys.
Going hand in hand with Astrolight’s laser technology is the autonomous satellite navigation system fellow Lithuanian space-tech start-up Blackswan Space has developed.
Blackswan Space’s “vision based navigation system” allows satellites to be programmed and repositioned independently of a human based at a ground control centre who, its founders say, won’t be able to keep up with the sheer volume of satellites launching in the coming years.
In a defence environment, the same technology can be used to remotely destroy an enemy satellite, as well as to train soldiers by creating battle simulations.
But the sales pitch to the Lithuanian military hasn’t necessarily been straightforward, acknowledges Tomas Malinauskas, Blackswan Space’s chief commercial officer.
He’s also concerned that government funding for the sector isn’t matching the level of innovation coming out of it.
He points out that instead of spending $300m on a US-made drone, the government could invest in a constellation of small satellites.
“Build your own capability for communication and intelligence gathering of enemy countries, rather than a drone that is going to be shot down in the first two hours of a conflict,” argues Mr Malinauskas, also based in Vilnius.
“It would be a big boost for our small space community, but as well, it would be a long-term, sustainable value-add for the future of the Lithuanian military.”
Eglė Elena Šataitė is the head of Space Hub LT, a Vilnius-based agency supporting space companies as part of Lithuania’s government-funded Innovation Agency.
“Our government is, of course, aware of the reality of where we live, and that we have to invest more in security and defence – and we have to admit that space technologies are the ones that are enabling defence technologies,” says Ms Šataitė.
The country’s Minister for Economy and Innovation, Lukas Savickas, says he understands Mr Malinauskas’ concern and is looking at government spending on developing space tech.
“Space technology is one of the highest added-value creating sectors, as it is known for its horizontality; many space-based solutions go in line with biotech, AI, new materials, optics, ICT and other fields of innovation,” says Mr Savickas.
Whatever happens with government funding, the Lithuanian appetite for innovation remains strong.
“We always have to prove to others that we belong on the global stage,” says Dominykas Milasius, co-founder of Delta Biosciences.
“And everything we do is also geopolitical… we have to build up critical value offerings, sciences and other critical technologies, to make our allies understand that it’s probably good to protect Lithuania.”
AI Research
How Is AI Changing The Way Students Learn At Business School?
Artificial intelligence is the skill set that employers increasingly want from future hires. Find out how b-schools are equipping students to use AI
Business students are already seeing AI’s value. More than three-quarters of business schools have already integrated AI into their curricula—from essay writing to personal tutoring, career guidance to soft-skill development.
BusinessBecause hears from current business students about how AI is reshaping the business school learning experience.
The benefits and drawbacks of using AI for essay writing
Many business school students are gaining firsthand experience of using AI to assist their academic work. At Rotterdam School of Management, Erasmus University in the Netherlands, students are required to use AI tools when submitting essays, alongside a log of their interactions.
“I was quite surprised when we were explicitly instructed to use AI for an assignment,” said Lara Harfner, who is studying International Business Administration (IBA) at RSM. “I liked the idea. But at the same time, I wondered what we would be graded on, since it was technically the AI generating the essay.”
Lara decided to approach this task as if she were writing the essay herself. She began by prompting the AI to brainstorm around the topic, research areas using academic studies and build an outline, before asking it to write a full draft.
However, during this process Lara encountered several problems. The AI-generated sources were either non-existent or inappropriate, and the tool had to be explicitly instructed on which concepts to focus on. It tended to be too broad, touching on many ideas without thoroughly analyzing any of them.
“In the end, I felt noticeably less connected to the content,” Lara says. “It didn’t feel like I was the actual author, which made me feel less responsible for the essay, even though it was still my name on the assignment.”
Despite the result sounding more polished, Lara thought she could have produced a better essay on her own with minimal AI support. What’s more, the grades she received on the AI-related assignments were below her usual average. “To me, that shows that AI is a great support tool, but it can’t produce high-quality academic work on its own.”
AI-concerned employers who took part in the Corporate Recruiters Survey echo this finding, stating that they would rather GME graduates use AI as a strategic partner in learning and strategy, than as a source for more and faster content.
How business students use AI as a personal tutor
Daniel Carvalho, a Global Online MBA student, also frequently uses AI in his academic assignments, something encouraged by his professors at Porto Business School (PBS).
However, Daniel treats AI as a personal tutor, asking it to explain complex topics in simple terms and deepen the explanation. On top of this, he uses it for brainstorming ideas, summarizing case studies, drafting presentations and exploring different points of view.
“My MBA experience has shown me how AI, when used thoughtfully, can significantly boost productivity and effectiveness,” he says.
Perhaps one of the most interesting ways Daniel uses AI is by turning course material into a personal podcast. “I convert text-based materials into audio using text-to-speech tools, and create podcast-style recaps to review content in a more conversational and engaging way. This allows me to listen to the materials on the go—in the car or at the gym.”
While studying his financial management course, Daniel even built a custom GPT using course materials. Much like a personal tutor, it would ask him questions about the material, validate his understanding, and explain any questions he got wrong. “This helped reinforce my knowledge so effectively that I was able to correctly answer all multiple-choice questions in the final exam,” he explains.
Similarly, at Villanova School of Business in the US, Master of Science in Business Analytics and AI (MSBAi) students are building personalized AI bots with distinct personalities. Students embed reference materials into the bot which then shape how the bot responds to questions.
“The focus of the program is to apply these analytics and AI skills to improve business results and career outcomes,” says Nathan Coates, MSBAi faculty director at the school. “Employers are increasingly looking for knowledge and skills for leveraging GenAI within business processes. Students in our program learn how AI systems work, what their limitations are, and what they can do better than existing solutions.”
The common limitations of using AI for academic work
Kristiina Esop, who is studying a doctorate in Business Administration and Management at Estonian Business School, agrees that AI in education must always be used critically and with intention. She warns students should always be aware of AI’s limitations.
Kristiina currently uses AI tools to explore different scenarios, synthesize large volumes of information, and detect emerging debates—all of which are essential for her work both academically and professionally.
However, she cautions that AI tools are not 100% accurate. Kristiina once asked ChatGPT to map actors in circular economy governance, and it returned a neat, simplified diagram that ignored important aspects. “That felt like a red flag,” she says. “It reminded me that complexity can’t always be flattened into clean logic. If something feels too easy, too certain—that’s when it is probably time to ask better questions.”
To avoid this problem, Kristiina combines the tools with critical thinking and contextual reading, and connects the findings back to the core questions in her research. “I assess the relevance and depth of the sources carefully,” she says. “AI can widen the lens, but I still need to focus it myself.”
She believes such critical thinking when using AI is essential. “Knowing when to question AI-generated outputs, when to dig deeper, and when to disregard a suggestion entirely is what builds intellectual maturity and decision-making capacity,” she says.
This is also what Wharton management professor Ethan Mollick, author of Co Intelligence: Living and Working with AI and co-director of the Generative AI Lab believes. He says the best way to work with [generative AI] is to treat it like a person. “So you’re in this interesting trap,” he says. “Treat it like a person and you’re 90% of the way there. At the same time, you have to remember you are dealing with a software process.”
Hult International Business School, too, expects its students to use AI in a balanced way, encouraging them to think critically about when and how to use it. For example, Rafael Martínez Quiles, a Master’s in Business Analytics student at Hult, uses AI as a second set of eyes to review his thinking.
“I develop my logic from scratch, then use AI to catch potential issues or suggest improvements,” he explains. “This controlled, feedback-oriented approach strengthens both the final product and my own learning.”
At Hult, students engage with AI to solve complex, real-world challenges as part of the curriculum. “Practical business projects at Hult showed me that AI is only powerful when used with real understanding,” says Rafael. “It doesn’t replace creativity or business acumen, it supports it.”
As vice president of Hult’s AI Society, N-AIble, Rafael has seen this mindset in action. The society’s members explore AI ethically, using it to augment their work, not automate it. “These experiences have made me even more confident and excited about applying AI in the real world,” he says.
The AI learning tools students are using to improve understanding
In other business schools, AI is being used to offer faculty a second pair of hands. Nazarbayev University Graduate School of Business has recently introduced an ‘AI Jockey’. Appearing live on a second screen next to the lecturer’s slides, this AI tool acts as a second teacher, providing real-time clarifications, offering alternate examples, challenging assumptions, and deepening explanations.
“Students gain access to instant, tailored explanations that complement the lecture, enhancing understanding and engagement,” says Dr Tom Vinaimont, assistant professor of finance, Nazarbayev University Graduate School of Business, who uses the AI jockey in his teaching.
Rather than replacing the instructor, the AI enhances the learning experience by adding an interactive, AI-driven layer to traditional teaching, transforming learning into a more dynamic, responsive experience.
“The AI Jockey model encourages students to think critically about information, question the validity of AI outputs, and build essential AI literacy. It helps students not only keep pace with technological change but also prepares them to lead in an AI-integrated world by co-creating knowledge in real time,” says Dr Vinaimont.
How AI can be used to encourage critical thinking among students
So, if you’re looking to impress potential employers, learning to work with AI while a student is a good place to start. But simply using AI tools isn’t enough. You must think critically, solve problems creatively and be aware of AI’s limitations.
Most of all, you must be adaptable. GMAC’s new AI-powered tool, Advancery, helps you find graduate business programs tailored to your career goals, with AI-readiness in mind.
After all, working with AI is a skill in itself. And in 2025, it is a valuable one.
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