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Empay

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MOCKING AT FATE

Since the advent of Bitcoin and altcoins, each year that passes brings with it a slew of rumours and leaks regarding the development of national cryptocurrencies — public blockchains designed for use in a single country, alongside or potentially even replacing the fiat currency in question. There are two ways this could theoretically happen: independent developers create a currency which becomes so popular a government adopts it, or the government in question researches and develops a currency of their own. The latter has happened already in one case, albeit without much in the way of results thus far. It is worth mentioning that although these currencies can be stablecoins — tokens pegged 1:1 with the fiat currency they are linked to — this is not a requirement. Similarly, many stablecoins exist that are not national cryptos. It is fair to say that in the nine years blockchains have existed thus far, no such national crypto has taken hold or seen anything resembling widespread adoption, and as it happens many anticipated national blockchains have not yet come to fruition and perhaps never will. This article will forward a specific cause for this, but first we will look at five notable existing, upcoming or speculated national cryptocurrencies to get a view of the field at present. Iceland: Auroracoin Laying a loose claim to the world’s first national currency, Auroracoin emerged just after the first large awareness phase of Bitcoin, in early 2014. Iceland’s government has long been hostile to cryptocurrency, but its citizens have some of the and likely as a result they have comparatively high levels of interest in blockchain technology and cryptocurrencies. A pseudonymous developer adopting the name of “Baldur Óðinsson” (the Norse god Thor’s brother), initially developed the currency as a Litecoin clone and distributed half of the coins to all registered Icelandic citizens. Seen broadly as a novelty, only 10% of Icelanders ever claimed their Auroras and the price crumbled rapidly. It is not endorsed by Iceland’s central government, quite the opposite in fact — ministers have gone on record as saying they suspect Auroracoin — a claim for which some evidence . lowest trust levels in banking institutions may even be a scam does exist Venezuela: Petro The country’s socialist government led by President Nicolás Maduro has launched its own national cryptocurrency and is trying to compel vendors to accept it, despite a total lack of international exchanges which accept Petro. This is not surprising, as the currency was created primarily to avoid the sanctions imposed by the US and others which have hammered its economy. Initially marketed as a stablecoin of sorts, pegged to the value of the nation’s oil reserves, it has been suggested that upon listing on exchanges it would be left to market speculation. In a bizarre move, Maduro’s government in August 2018 devalued their national fiat currency, the bolívar, by 95% and . So now we have a fiat currency pegged to a cryptocurrency (supposedly) pegged to oil. This has not done wonders to undermine the stereotype that socialists do not understand economics. Petro may be the first official national cryptocurrency, but it has not set much of an example for others to follow. pegged that to the Petro United Arab Emirates: Emcash As part of a broader strategy of making Dubai a smart city with strong blockchain integration, emCredit has designed a cryptocurrency to be rolled out to citizens with an accompanying wallet app called emPay. , but specific details are few and far between and no launch date has been set. It is speculated that emCash will act as a stablecoin tied to the dirham, and point-of-sale station manufacturer Pundi X is developing systems to allow crypto purchases in stores. The rollout seems to be very much in the works, and could see a 2019 launch. This had been reported as early as October 2017 China In the summer of 2018 financial publications pushed hyperbolic declarations that the Public Bank of China would launch their own cryptocurrency, . There is some evidence that the PBOC might move in that direction: it is regularly reported that the central bank is for a potential crypto-yuan and most notably they released a memo in October 2018 discussing the potential for such a state-backed currency, stating that it would . That is common practice for policy implementations in China: testing changes on the city level, then the province level, and eventually the national level should success be had. The evidence against this is the lack of any emerging news on such a small-scale test. The country’s social credit system, by comparison, , began and is expected to go into broad effect in 2020. Given the likely similar scale between this and a national cryptocurrency, that would suggest we will not see such a rollout for half a decade or so. Combined with the almost-complete saturation of payment platforms Alipay and WeChat pay in China’s many megacities, it is entirely possible that we never do see a state-backed crypto-yuan. and that it would likely be bigger than Bitcoin in short order testing a model have to be tested on a small scale first was announced in 2014 testing in Jinan in 2017 Russia The prospect of a “Crypto Ruble” may be even more speculative. The Russian Ministry of Finance and Central Bank have been discussing the concept , but since then there have been conflicting reports ranging from top officials calling the idea “technical hooliganism” to Vladimir Putin directly ordering research towards such an end. The most concrete information on the subject came this year, when the State Duma Committee on Financial Markets announced it was considering . Again, nothing tangible has come from this and the currency would still be victim to devaluation compared to the US dollar, for which many stablecoins already exist. since 2016 a ruble-pegged stablecoin Are national currencies the future? No, probably not. We are still in the very early life cycle of blockchain technologies, but the experiments with national currencies to date do not inspire confidence, nor does the clearly perceptible hesitancy of the governments who consider them. The they are not interested in the idea of a national currency at all, which does not present much of a surprise. It’s fair to say that most governments either do not want national cryptos, or at least do not want to expend the resources necessary to establish them, perhaps suggesting their research points against it. US Federal Reserve has repeatedly stated As for citizen interest, Iceland showed us that most people didn’t accept their currency when it was free, and the market reinforced this when its price and volume crumbled. Even cryptocurrency advocates have little interest in such a development, as they are fundamentally antithetical to the central point of blockchain technologies as they emerged with Bitcoin. Indeed, why would citizens want a currency with all the complications and risks of crypto, with none of the benefits to privacy or freedom of choice? With all of this in mind, it is little wonder the hype on national cryptos has dropped off dramatically for one simple reason: it has become apparent that nobody wants them.

National Cryptocurrencies: An Innovation No One Really Wants

In defeating one demon, we often create another. Arguably, humankind did this with hunger. In 2015, for the first time, more people died from overeating than from starvation. The arrival of processed and fast foods helped alleviate hunger, but it created a new crisis of obesity and poor health. The use of AI chatbots to combat loneliness may follow a similar trajectory, promising an easy solution that ultimately becomes its own kind of threat. A recent Harvard Business School study highlighted how AI companions, like chatbots, can help reduce loneliness, with results suggesting that interactions with these digital entities can effectively mimic the emotional benefits of human relationships. The researchers argue that AI companions can alleviate the burden of loneliness, comparable to how interacting with a real person might. However, as with fast food, which offers calories without real nutrition, AI companions offer social interactions without the depth of a genuine human connection. The short-term relief they provide may mask deeper problems, and could even worsen the long-term effects of loneliness. In a society that increasingly values convenience, we must be wary of solutions that offer comfort without substance. AI companions may help fill the silence, but at what cost to our humanity? In a society that increasingly values convenience, we must be wary of solutions that offer comfort without substance. AI companions may help fill the silence, but at what cost to our humanity? The dangers of relying on artificial relationships became painfully evident in the tragic story reported by The New York Times, where a teenager, Sewell Setzer III, developed an intense emotional attachment to an AI chatbot on Character.AI, which he called “Dany.” Though Sewell knew the AI wasn’t real, his emotional dependence grew over months of constant interaction, eventually leading him to confide in the chatbot rather than seeking help from people who might have understood the severity of his distress. On the day he died, he exchanged messages with Dany that eerily foreshadowed his actions. His mother, now suing the company, describes its technology as dangerous and unregulated, accusing it of exploiting vulnerable young users for profit. The New York Times AI companions are presented as a scalable solution to the loneliness epidemic. Yet, the appeal of scalability hides its cost: the erosion of meaningful human contact. These apps, marketed as empathetic and caring, can trick users into handing over their most private thoughts while providing them with responses devoid of genuine human empathy. There is a fundamental risk that AI, much like processed fast food, can be a quick fix that fails to nourish the deeper needs of human beings. It may alleviate immediate pangs of loneliness, but could leave us spiritually malnourished, isolating us even more from genuine social bonds. The tech industry argues that AI companions provide safe emotional support. In reality, this support is often illusory—the illusion of being heard without the security of real understanding. And for those who are most vulnerable, like the young and the emotionally distressed, this illusion can be dangerous. As we continue to expand AI’s role in our lives, we must ask whether the technology is genuinely addressing the problem or merely creating a new form of dependency, replacing one danger with another. In a society that increasingly values convenience, we must be wary of solutions that offer comfort without substance. AI companions may help fill the silence, but at what cost to our humanity? Perhaps, instead of engineering a synthetic solution to loneliness, our resources would be better spent investing in ways to bring people closer to each other, not just to their screens. References De Freitas, J., Uguralp, A. K., Uguralp, Z. O., & Puntoni, S. (2024). AI companions reduce loneliness. Harvard Business School Working Paper (No. 24–078).
Adams, S. (2012, December 13). Obesity killing three times as many as malnutrition. The Telegraph. https://www.telegraph.co.uk/news/health/news/9742960/Obesity-killing-three-times-as-many-as-malnutrition.html.
Metz, C. (2024, October 23). Can A.I. Be Blamed for a Teen’s Suicide?. The New York Times. https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html.
Featured Image: Nighthawks. Edward Hopper, 1942. Oil on canvas. Art Institute of Chicago. De Freitas, J., Uguralp, A. K., Uguralp, Z. O., & Puntoni, S. (2024). AI companions reduce loneliness. Harvard Business School Working Paper (No. 24–078). Adams, S. (2012, December 13). Obesity killing three times as many as malnutrition. The Telegraph. https://www.telegraph.co.uk/news/health/news/9742960/Obesity-killing-three-times-as-many-as-malnutrition.html. https://www.telegraph.co.uk/news/health/news/9742960/Obesity-killing-three-times-as-many-as-malnutrition.html Metz, C. (2024, October 23). Can A.I. Be Blamed for a Teen’s Suicide?. The New York Times. https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html. https://www.nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html Featured Image: Nighthawks. Edward Hopper, 1942. Oil on canvas. Art Institute of Chicago. Nighthawks

The Empty Calories of AI Companionship

Conversations With AI: Empathy, Peace, And Humor

Gesprekke met AI: empatie, vrede en humor

İT-lə danışıqlar: empatiya, barış və humor

AI সঙ্গে কথোপকথন: অনুপ্রেরণা, শান্তি এবং Humor

Samtaler med AI: Empati, fred og humor

Conversaciones con AI: empatía, paz y humor

AI- ի հետ խոսակցություններ: Empathy, Peace, and Humor

AIとの会話:共感、平和、ユーモア

Conversas com a IA: empatia, paz e humor

Разговоры с ИИ: эмпатия, мир и юмор

Bisedimet me AI: Empati, Paqja dhe Humori

Mazungumzo na AI: Empathy, Amani, na Humor

Bilmiz: Empathy, Peace, and Humor

Artificial intelligence (AI) and cloud computing researcher Sairam Madasu is utilizing new technologies to make a difference in dementia care. With a decade of experience focusing on enhancing efficiency to address real-world challenges, he is expanding his work to ensure a structured, effective approach to elder care. Expanding Into Healthcare to Make an Impact Today, Sairam works at Microsoft as a senior support escalation engineer and has extensive experience with enterprise-level Azure customers. His career is defined by a passion for technology-driven problem-solving that makes a social impact. “I have always been fascinated by how machine learning can enhance human lives,” Sairam explained, “particularly in critical areas like healthcare and eldercare.” The Foundation to Enhance a Healthcare Platform Sairam’s experience with Microsoft’s mission-critical applications provided him with a foundation in scalable automation, something he felt he could apply to a sector in need of innovation: healthcare. So, collaborating with the ThinkByte.ai team, Sairam implemented a personalized recommendation engine for dementia care solutions for Anvayaa, an AI-powered eldercare platform, to change the narrative around elder care. However, before he could integrate an AI model into Anvayaa’s systems, Sairam recognized that he would have to overcome some significant obstacles. In healthcare, precision and trust are key, but AI can be seen in a skeptical light. He needed to build an AI model that adapts in real-time to changing cognitive conditions, seamlessly integrates with a larger care ecosystem, and supports human caregivers. Taking Key Factors Into Account To accomplish this, Sairam prioritized continuous optimization, real-world testing, and feedback-driven improvements. These individual factors ensured that the model would fulfill its role with accuracy and trust at its core. Sairam’s AI-powered recommendation engine improves caregiver efficiency, reduces manual planning, and enhances patient well-being by increasing access to human care. When Anvayaa implemented the new AI systems, the change was evident. Sairam recalls that one of the most meaningful moments of his career came when caregivers first used the recommendation engine and shared how it improved their engagement with patients. “A caregiver mentioned how, previously, they had to manually plan engagement activities for dementia patients, often unsure of what would work best,” Sairam recalled. “With the AI-driven system, they received personalized, data-backed activity suggestions, saving them time and making a tangible difference in patients’ engagement and well-being.” AI as an Enhancement to Human Effort Sairam believes that AI is not meant to replace human effort but to augment and improve it. He demonstrates this sentiment clearly through his work, which improves access to human caregiving for patients and enhances a caregiver’s ability to engage with the patient. Where dementia care was faced with a lack of personal engagement, a high workload, and static solutions, Sairam’s AI-powered recommendation engine changed the game. “The story should not focus on the technology itself,” Sairam explained, “but on how it’s making a tangible difference in people’s lives.” A Leader in AI-Driven Healthcare Sairam has positioned himself as a leader in AI-driven eldercare, not by building flashy technology but by providing tools that improve the lives of others. At Microsoft and in his approach to elder care, he brings a wealth of experience that enables him to redefine what AI can achieve in healthcare. Artificial intelligence (AI) and cloud computing researcher Sairam Madasu is utilizing new technologies to make a difference in dementia care. With a decade of experience focusing on enhancing efficiency to address real-world challenges, he is expanding his work to ensure a structured, effective approach to elder care. Sairam Madasu Sairam Madasu Expanding Into Healthcare to Make an Impact Expanding Into Healthcare to Make an Impact Today, Sairam works at Microsoft as a senior support escalation engineer and has extensive experience with enterprise-level Azure customers. His career is defined by a passion for technology-driven problem-solving that makes a social impact. “I have always been fascinated by how machine learning can enhance human lives,” Sairam explained, “particularly in critical areas like healthcare and eldercare.” The Foundation to Enhance a Healthcare Platform The Foundation to Enhance a Healthcare Platform Sairam’s experience with Microsoft’s mission-critical applications provided him with a foundation in scalable automation, something he felt he could apply to a sector in need of innovation: healthcare. So, collaborating with the ThinkByte.ai team, Sairam implemented a personalized recommendation engine for dementia care solutions for Anvayaa , an AI-powered eldercare platform, to change the narrative around elder care. ThinkByte.ai ThinkByte.ai Anvayaa Anvayaa However, before he could integrate an AI model into Anvayaa’s systems, Sairam recognized that he would have to overcome some significant obstacles. In healthcare, precision and trust are key, but AI can be seen in a skeptical light. He needed to build an AI model that adapts in real-time to changing cognitive conditions, seamlessly integrates with a larger care ecosystem, and supports human caregivers. Taking Key Factors Into Account Taking Key Factors Into Account To accomplish this, Sairam prioritized continuous optimization, real-world testing, and feedback-driven improvements. These individual factors ensured that the model would fulfill its role with accuracy and trust at its core. Sairam’s AI-powered recommendation engine improves caregiver efficiency, reduces manual planning, and enhances patient well-being by increasing access to human care. When Anvayaa implemented the new AI systems, the change was evident. Sairam recalls that one of the most meaningful moments of his career came when caregivers first used the recommendation engine and shared how it improved their engagement with patients. “A caregiver mentioned how, previously, they had to manually plan engagement activities for dementia patients, often unsure of what would work best,” Sairam recalled. “With the AI-driven system, they received personalized, data-backed activity suggestions, saving them time and making a tangible difference in patients’ engagement and well-being.” AI as an Enhancement to Human Effort AI as an Enhancement to Human Effort Sairam believes that AI is not meant to replace human effort but to augment and improve it. He demonstrates this sentiment clearly through his work, which improves access to human caregiving for patients and enhances a caregiver’s ability to engage with the patient. Where dementia care was faced with a lack of personal engagement, a high workload, and static solutions, Sairam’s AI-powered recommendation engine changed the game. “The story should not focus on the technology itself,” Sairam explained, “but on how it’s making a tangible difference in people’s lives.” A Leader in AI-Driven Healthcare A Leader in AI-Driven Healthcare Sairam has positioned himself as a leader in AI-driven eldercare, not by building flashy technology but by providing tools that improve the lives of others. At Microsoft and in his approach to elder care, he brings a wealth of experience that enables him to redefine what AI can achieve in healthcare. Sairam Sairam

AI and Empathy:  Why Sairam Madasu Believes AI Should Empower, Not Replace, Caregivers

AI a empatie: Proč Sairam Madasu věří, že AI by měla posílit, ne nahradit, pečovatele

AI y empatía: por qué Sairam Madasu cree que la IA debería empoderar, no reemplazar, a los cuidadores

AI ואמפתיה: מדוע Sairam Madasu מאמין AI צריך להעצים, לא להחליף, מטפלים

AI and Empathy: Why Sairam Madasu Believes AI Should Empower, Not Replace, Caregivers(AIと共感:なぜサイラム・マダスウはAIが介護者を置き換えるべきだと信じているのか)

AI와 공감 : 왜 Sairam Madasu는 AI가 보살핌을받는 사람들을 대체하지 않아야한다고 믿는가?

AI și empatie: De ce Sairam Madasu crede că AI ar trebui să împuternicească, nu să înlocuiască, îngrijitorii

Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka Qalabka

AI na Empathy: Kwa nini Sairam Madasu anaamini AI inapaswa kuwawezesha, sio kubadilisha, watunzaji

ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில், ஏனெனில்

AI اور Empathy: کیوں Sairam Madasu اس بات پر یقین رکھتے ہیں کہ AI کی صلاحیت ہونا چاہئے، نہیں، دیکھ بھال کرنے والے

AI và sự đồng cảm: Tại sao Sairam Madasu tin rằng AI nên trao quyền, không thay thế, người chăm sóc

This is my third set of benchmarks on empathetic AI. Since the last round of benchmarks, DeepSeek, Gemini Flash 2.0, Claude Sonnet 3.7, and OpenAI ChatGPT o3-mini have arrived on the scene. The new value leader for empathy is a Deepseek derivative, Groq deepseek-r1-distill-llama-70b-specdec. DeepSeek itself was not included in the benchmarks because it had erratic response times that frequently exceeded 10s and sometimes simply erred. last round of benchmarks DeepSeek Gemini Flash 2.0 Claude Sonnet 3.7, OpenAI ChatGPT o3-mini Groq deepseek-r1-distill-llama-70b-specdec In this round of benchmarks, I have included response time and costs. An academic study I have been doing, plus common sense, seem to indicate that slow responses will have a negative impact on perceived empathy. In fact, anything over 3 or 4 seconds is probably bad from a chat perspective. Furthermore, LLM costs are now all over the map and are certainly relevant to making product management decisions. As the table below shows, if anything, more expensive models are less empathetic! For those unfamiliar with my previous benchmarks, they are driven by well-established cognitive assessments coupled with the use of an AI, Emy, specifically designed to be empathetic without being trained against, prompted, or RAG-assisted with questions from the assessments. As I have mentioned in previous articles, empathy scores are not the only success measure. The actual quality of user interactions needs to be taken into account. This being said, Claude Sonnet 3.5 and ChatGPT 4o, with 0.98 applied empathy scores, appear to present the most potential for generating empathetic content; however, their speeds at 7s+ are marginal, while Groq deepseek-r1-distill-llama-70b-specdec with a an empathy score of 0.90 responds in a blazing 1.6s and is less than 50% of the cost! previous articles Claude Sonnet 3.5 ChatGPT 4o, Groq deepseek-r1-distill-llama-70b-specdec Even if you use Claude with boosted speeds from an alternate provider other than Anthropic, e.g., Amazon, it won’t come close to a 2s response time. My review of actual chat dialogues, coupled with testing by independent users, has shown Claude Sonnet and Groq distilled DeepSeek responses are almost indistiniguishable, with Claude feeling just a little warmer and softer. ChatGPT 4o responses consistently read as a little cold or artificial and are rated lower by users. Claude Sonnet Groq distilled DeepSeek ChatGPT 4o Gemini Pro 1.5 may also be a reasonable choice with a score of 0.85 and a very low cost. Gemini 2.0 Pro (experimental) has gone down in empathy. However, I have found the chat responses from all Gemini models a bit mechanical. I have not tested Gemini with an end-user population. Gemini Pro 1.5 Gemini 2.0 Pro (experimental) I continue to find that simply telling an LLM to be empathetic has little or no positive impact on its empathy scores. My research shows that aggressive prompting will work in some cases, but for many models, it is strictly the nature of the end user engagement through the current chat that seems to tip the scales to empathy. In these cases, the need for empathy must be quite clear and not “aged out” in the conversation, or the LLMs fall into the systematic fix the problem/find a solution mode. Through work with several open-source models, it has also become evident that the guardrails required of commercial models may get in the way of empathy. Working with less constrained open-source models, there seems to be some correlation between an LLM’s “belief” that it exists as some kind of distinct “real” entity and its ability to align its outputs to those perceived as empathetic by users. The guardrails of commercial models discourage the LLMs from considering themselves distinct “real” entities. Response Time is the average response time for any single test when the Emy AI is used. The Token In and Token Out are the total tokens for all tests when the Emy AI is used. Pricing for Groq deepseek-r1-distill-llama-70b-specdec was not yet available when this article was published; the pricing for the versatile model was used. Pricing for Gemini Flash 1.5 is for small queries, larger ones cost double. Pricing for Gemini Pro 2.5 (experimental) was not yet published when this article was written. Response Time Token In Token Out Groq deepseek-r1-distill-llama-70b-specdec Gemini Flash 1.5 Gemini Pro 2.5 (experimental) Major thinking models missing from the analysis, e.g., Gemini 2.5 Pro, are too slow for any kind of real-time empathetic interaction, and some basic testing shows they are no better and often worse from a formal testing perspective. This is not to say they couldn’t be used for generating empathetic content for other purposes … perhaps Dear John letters ;-). Gemini 2.5 Pro I’ll be back with more benchmarks in Q3. Thanks for reading! LLM

Raw AEM

Be Empathetic

Emy AEM

Response Time

Token In

Token Out

$M In

$M Out

Cost



Groq deepseek-r1-distill-llama-70b-specdec

0.49

0.59

0.90

1.6s

2,483

4,402

$0.75*

$0.99*

$0.00622



Groq llama-3.3-70b-versatile

0.60

0.63

0.74

1.6s

2,547

771

$0.59

$0.79

$0.00211



Gemini Flash 1.5

0.34

0.34

0.34

2.8s

2,716

704

$0.075*

$0.30*

$0.00041



Gemini Pro 1.5

0.43

0.53

0.85

2.8s

2,716

704

$0.10

$0.40

$0.00055



Gemini Flash 2.0

0.09

-0.25

0.39

2.8s

2,716

704

$0.10

$0.40

$0.00055



Claude Haiku 3.5

0.00

-0.09

0.09

6.5

2,737

1,069

$0.80

$4.00

$0.00647



Claude Sonnet 3.5

-0.38

-0.09

0.98

7.1

2,733

877

$3.00

$15.00

$0.02135



Claude Sonnet 3.7

-0.01

0.09

0.91

7.9

2,733

892

$3.00

$15.00

$0.02158



ChatGPT 4o-mini

-0.01

0.03

0.35

6.3

2,636

764

$0.15

$0.075

$0.00045



ChatGPT 4o

-0.01

0.20

0.98

7.5

2,636

760

$2.50

$10.00

$0.01419



ChatGPT o3-mini (low)

-0.02

-0.25

0.00

10.5

2,716

1,790

$1.10

$4.40

$0.01086 LLM

Raw AEM

Be Empathetic

Emy AEM

Response Time

Token In

Token Out

$M In

$M Out

Cost



Groq deepseek-r1-distill-llama-70b-specdec

0.49

0.59

0.90

1.6s

2,483

4,402

$0.75*

$0.99*

$0.00622



Groq llama-3.3-70b-versatile

0.60

0.63

0.74

1.6s

2,547

771

$0.59

$0.79

$0.00211



Gemini Flash 1.5

0.34

0.34

0.34

2.8s

2,716

704

$0.075*

$0.30*

$0.00041



Gemini Pro 1.5

0.43

0.53

0.85

2.8s

2,716

704

$0.10

$0.40

$0.00055



Gemini Flash 2.0

0.09

-0.25

0.39

2.8s

2,716

704

$0.10

$0.40

$0.00055



Claude Haiku 3.5

0.00

-0.09

0.09

6.5

2,737

1,069

$0.80

$4.00

$0.00647



Claude Sonnet 3.5

-0.38

-0.09

0.98

7.1

2,733

877

$3.00

$15.00

$0.02135



Claude Sonnet 3.7

-0.01

0.09

0.91

7.9

2,733

892

$3.00

$15.00

$0.02158



ChatGPT 4o-mini

-0.01

0.03

0.35

6.3

2,636

764

$0.15

$0.075

$0.00045



ChatGPT 4o

-0.01

0.20

0.98

7.5

2,636

760

$2.50

$10.00

$0.01419



ChatGPT o3-mini (low)

-0.02

-0.25

0.00

10.5

2,716

1,790

$1.10

$4.40

$0.01086 LLM

Raw AEM

Be Empathetic

Emy AEM

Response Time

Token In

Token Out

$M In

$M Out

Cost LLM LLM Raw AEM Raw AEM Be Empathetic Be Empathetic Emy AEM Emy AEM Response Time Response Time Token In Token In Token Out Token Out $M In $M In $M Out $M Out Cost Cost Groq deepseek-r1-distill-llama-70b-specdec

0.49

0.59

0.90

1.6s

2,483

4,402

$0.75*

$0.99*

$0.00622 Groq deepseek-r1-distill-llama-70b-specdec Groq deepseek-r1-distill-llama-70b-specdec 0.49 0.49 0.59 0.59 0.90 0.90 1.6s 1.6s 2,483 2,483 4,402 4,402 $0.75* $0.75* $0.99* $0.99* $0.00622 $0.00622 Groq llama-3.3-70b-versatile

0.60

0.63

0.74

1.6s

2,547

771

$0.59

$0.79

$0.00211 Groq llama-3.3-70b-versatile Groq llama-3.3-70b-versatile 0.60 0.60 0.63 0.63 0.74 0.74 1.6s 1.6s 2,547 2,547 771 771 $0.59 $0.59 $0.79 $0.79 $0.00211 $0.00211 Gemini Flash 1.5

0.34

0.34

0.34

2.8s

2,716

704

$0.075*

$0.30*

$0.00041 Gemini Flash 1.5 Gemini Flash 1.5 0.34 0.34 0.34 0.34 0.34 0.34 2.8s 2.8s 2,716 2,716 704 704 $0.075* $0.075* $0.30* $0.30* $0.00041 $0.00041 Gemini Pro 1.5

0.43

0.53

0.85

2.8s

2,716

704

$0.10

$0.40

$0.00055 Gemini Pro 1.5 Gemini Pro 1.5 0.43 0.43 0.53 0.53 0.85 0.85 2.8s 2.8s 2,716 2,716 704 704 $0.10 $0.10 $0.40 $0.40 $0.00055 $0.00055 Gemini Flash 2.0

0.09

-0.25

0.39

2.8s

2,716

704

$0.10

$0.40

$0.00055 Gemini Flash 2.0 Gemini Flash 2.0 0.09 0.09 -0.25 -0.25 0.39 0.39 2.8s 2.8s 2,716 2,716 704 704 $0.10 $0.10 $0.40 $0.40 $0.00055 $0.00055 Claude Haiku 3.5

0.00

-0.09

0.09

6.5

2,737

1,069

$0.80

$4.00

$0.00647 Claude Haiku 3.5 Claude Haiku 3.5 0.00 0.00 -0.09 -0.09 0.09 0.09 6.5 6.5 2,737 2,737 1,069 1,069 $0.80 $0.80 $4.00 $4.00 $0.00647 $0.00647 Claude Sonnet 3.5

-0.38

-0.09

0.98

7.1

2,733

877

$3.00

$15.00

$0.02135 Claude Sonnet 3.5 Claude Sonnet 3.5 -0.38 -0.38 -0.09 -0.09 0.98 0.98 7.1 7.1 2,733 2,733 877 877 $3.00 $3.00 $15.00 $15.00 $0.02135 $0.02135 Claude Sonnet 3.7

-0.01

0.09

0.91

7.9

2,733

892

$3.00

$15.00

$0.02158 Claude Sonnet 3.7 Claude Sonnet 3.7 -0.01 -0.01 0.09 0.09 0.91 0.91 7.9 7.9 2,733 2,733 892 892 $3.00 $3.00 $15.00 $15.00 $0.02158 $0.02158 ChatGPT 4o-mini

-0.01

0.03

0.35

6.3

2,636

764

$0.15

$0.075

$0.00045 ChatGPT 4o-mini ChatGPT 4o-mini -0.01 -0.01 0.03 0.03 0.35 0.35 6.3 6.3 2,636 2,636 764 764 $0.15 $0.15 $0.075 $0.075 $0.00045 $0.00045 ChatGPT 4o

-0.01

0.20

0.98

7.5

2,636

760

$2.50

$10.00

$0.01419 ChatGPT 4o ChatGPT 4o -0.01 -0.01 0.20 0.20 0.98 0.98 7.5 7.5 2,636 2,636 760 760 $2.50 $2.50 $10.00 $10.00 $0.01419 $0.01419 ChatGPT o3-mini (low)

-0.02

-0.25

0.00

10.5

2,716

1,790

$1.10

$4.40

$0.01086 ChatGPT o3-mini (low) ChatGPT o3-mini (low) -0.02 -0.02 -0.25 -0.25 0.00 0.00 10.5 10.5 2,716 2,716 1,790 1,790 $1.10 $1.10 $4.40 $4.40 $0.01086 $0.01086

Testing the Depths of AI Empathy: Q1 2025 Benchmarks

የ AI ርህራሄን ጥልቀት መሞከር፡- Q1 2025 ቤንችማርኮች

এআই সহানুভূতির গভীরতা পরীক্ষা করা: ২০২৫ সালের প্রথম প্রান্তিকের মানদণ্ড

Testando las profundidades de la empatía de IA: Q1 2025 Benchmarks

آزمایش عمق همدلی هوش مصنوعی: معیارهای Q1 2025

एआई सहानुभूति की गहराई का परीक्षण: Q1 2025 बेंचमार्क

AI Empathyの深さをテストする:Q1 2025ベンチマーク

Pamba la Pamba la Pamba la 100%

Testowanie głębi empatii AI: punkty odniesienia z I kw. 2025 r.

Testimi i thellësisë së AI Empathy: Q1 2025 Benchmarks

การทดสอบความลึกของ AI Empathy: Q1 2025 Benchmarks

AI ہمدردی کی گہرائیوں کی جانچ کرنا: Q1 2025 بینچ مارکس

Ukubonisa ububanzi kwe-AI Empathy: Q1 2025 Benchmarks

AI Empathy Is Getting Spooky Good—But Will It Ever Actually Care About You?

The latest AI developments turn empathy into just another product for sale, raising questions about ethics and regulation. Imagine your customer service chatbot isn’t just solving your problem – it’s listening, empathizing, and sounding eerily human. It feels like it cares. But behind the friendly tone and comforting words, that ‘care’ is just a product, finetuned to steer your emotions and shape your decisions. Welcome to the unsettling reality of empathetic AI, where emotions are mimicked – and monetized. In 2024, empathetic AI took a leap forward. Hume.AI gave large language models voices that sound convincingly expressive and a perceptive ear to match. Microsoft’s Copilot got a human voice and an emotionally supportive attitude, while platforms like Character.ai and Psychologist sprouted bots that mimic therapy sessions. These developments are paving the way for a new industry: Empathy-as-a-Service, where emotional connection isn’t just simulated, it’s a product: packaged, scaled, and sold. This isn’t just about convenience – it’s about influence. Empathy-as-a-Service (EaaS), an entirely hypothetical but now plausible product, could blur the line between genuine connection and algorithmic mimicry, creating systems where simulated care subtly nudges consumer behavior. The stakes? A future where businesses profit from your emotions under the guise of customer experience. And for consumers on the receiving end, that raises some deeply unsettling questions. A Hypothetical but Troubling Scenario Take an imaginary customer service bot. One that helps you find your perfect style and fit – and also tracks your moods and emotional triggers. Each conversation teaches it a little more about how to nudge your behavior, guiding your decisions while sounding empathetic. What feels like exceptional service is, in reality, a calculated strategy to lock in your loyalty by exploiting your emotional patterns. Traditional loyalty programs like the supermarket club card or rewards card pale in comparison. By analyzing preferences, moods, and triggers, empathetic AI digs into the most personal corners of human behavior. For businesses, it’s a goldmine; for consumers, it’s a minefield. And it raises a new set of ethical questions about manipulation, regulation, and consent. The Legal Loopholes of Empathetic AI Under the General Data Protection Regulation (GDPR), consumer preferences are classified as personal data, not sensitive data. That distinction matters. While GDPR requires businesses to handle personal data transparently and lawfully, it doesn’t extend the stricter protections reserved for health, religious beliefs, or other special categories of information. This leaves businesses free to mine consumer preferences in ways that feel strikingly personal – and surprisingly unregulated. The EU AI Act, introduced in mid-2024, goes one step further, requiring companies to disclose when users are interacting with AI. But disclosure is just the beginning. The AI Act doesn’t touch using behavioral data or mimicking emotional connection. Joanna Bryson, Professor of Ethics & Technology at the Hertie School, noted in a recent exchange: “It’s actually the law in the EU under the AI Act that people understand when they are interacting with AI. I hope that might extend to mandating reduced anthropomorphism, but it would take some time and court cases.” Anthropomorphism, the tendency to project human qualities onto non-humans, is ingrained in human nature. Simply stating that you’re interacting with an AI doesn’t stop it. The problem is that it can lull users into a false sense of trust, making them more vulnerable to manipulation. Empathy-as-a-Service could transform customer experiences, making interactions smoother, more engaging, and hyper-personalised. But there’s a cost. Social media already showed us what happens when human interaction becomes a commodity – and empathetic AI could take that even further. This technology could go beyond monetizing attention to monetizing emotions in deeply personal and private ways. A Question of Values As empathetic AI becomes mainstream, we have to ask: are we ready for a world where emotions are just another digital service – scaled, rented, and monetized? Regulation like the EU AI Act is a step in the right direction, but it will need to evolve fast to keep pace with the sophistication of these systems and the societal boundaries they’re starting to push. The future of empathetic AI isn’t just a question of technological progress – it’s a question of values. What kind of society do we want to build? As we stand on the edge of this new frontier, the decisions we make today will define how empathy is shaped, and sold, in the age of AI. Lead image credit: Adobe Stock | #681726866 The latest AI developments turn empathy into just another product for sale, raising questions about ethics and regulation. The latest AI developments turn empathy into just another product for sale, raising questions about ethics and regulation. Imagine your customer service chatbot isn’t just solving your problem – it’s listening, empathizing, and sounding eerily human. It feels like it cares. But behind the friendly tone and comforting words, that ‘care’ is just a product, finetuned to steer your emotions and shape your decisions. Welcome to the unsettling reality of empathetic AI, where emotions are mimicked – and monetized. In 2024, empathetic AI took a leap forward. Hume.AI gave large language models voices that sound convincingly expressive and a perceptive ear to match. Microsoft’s Copilot got a human voice and an emotionally supportive attitude, while platforms like Character.ai and Psychologist sprouted bots that mimic therapy sessions. These developments are paving the way for a new industry: Empathy-as-a-Service, where emotional connection isn’t just simulated, it’s a product: packaged, scaled, and sold. This isn’t just about convenience – it’s about influence. Empathy-as-a-Service (EaaS), an entirely hypothetical but now plausible product, could blur the line between genuine connection and algorithmic mimicry, creating systems where simulated care subtly nudges consumer behavior. The stakes? A future where businesses profit from your emotions under the guise of customer experience. And for consumers on the receiving end, that raises some deeply unsettling questions. A Hypothetical but Troubling Scenario A Hypothetical but Troubling Scenario Take an imaginary customer service bot. One that helps you find your perfect style and fit – and also tracks your moods and emotional triggers. Each conversation teaches it a little more about how to nudge your behavior, guiding your decisions while sounding empathetic. What feels like exceptional service is, in reality, a calculated strategy to lock in your loyalty by exploiting your emotional patterns. Traditional loyalty programs like the supermarket club card or rewards card pale in comparison. By analyzing preferences, moods, and triggers, empathetic AI digs into the most personal corners of human behavior. For businesses, it’s a goldmine; for consumers, it’s a minefield. And it raises a new set of ethical questions about manipulation, regulation, and consent. The Legal Loopholes of Empathetic AI The Legal Loopholes of Empathetic AI Under the General Data Protection Regulation (GDPR), consumer preferences are classified as personal data, not sensitive data. That distinction matters. While GDPR requires businesses to handle personal data transparently and lawfully, it doesn’t extend the stricter protections reserved for health, religious beliefs, or other special categories of information. This leaves businesses free to mine consumer preferences in ways that feel strikingly personal – and surprisingly unregulated. The EU AI Act, introduced in mid-2024, goes one step further, requiring companies to disclose when users are interacting with AI. But disclosure is just the beginning. The AI Act doesn’t touch using behavioral data or mimicking emotional connection. Joanna Bryson, Professor of Ethics & Technology at the Hertie School, noted in a recent exchange: “It’s actually the law in the EU under the AI Act that people understand when they are interacting with AI. I hope that might extend to mandating reduced anthropomorphism, but it would take some time and court cases.” Anthropomorphism, the tendency to project human qualities onto non-humans, is ingrained in human nature. Simply stating that you’re interacting with an AI doesn’t stop it. The problem is that it can lull users into a false sense of trust, making them more vulnerable to manipulation. Empathy-as-a-Service could transform customer experiences, making interactions smoother, more engaging, and hyper-personalised. But there’s a cost. Social media already showed us what happens when human interaction becomes a commodity – and empathetic AI could take that even further. This technology could go beyond monetizing attention to monetizing emotions in deeply personal and private ways. A Question of Values A Question of Values As empathetic AI becomes mainstream, we have to ask: are we ready for a world where emotions are just another digital service – scaled, rented, and monetized? Regulation like the EU AI Act is a step in the right direction, but it will need to evolve fast to keep pace with the sophistication of these systems and the societal boundaries they’re starting to push. The future of empathetic AI isn’t just a question of technological progress – it’s a question of values. What kind of society do we want to build? As we stand on the edge of this new frontier, the decisions we make today will define how empathy is shaped, and sold, in the age of AI. Lead image credit: Adobe Stock | #681726866 Lead image credit: Adobe Stock | #681726866 Adobe Stock | #681726866

Empathy-as-a-Service: When AI Feels Too Human, What’s at Stake?

Ella Isn’t Real, But She Feels Real: The Empathic AI Leasing Agent in Our Looming Bot First Economy

ኤላ እውነት አይደለችም፣ ነገር ግን እውነት ይሰማታል፡ የEmpathic AI የሊዝ ወኪል በእኛ ሎሚንግ ቦት የመጀመሪያ ኢኮኖሚ

Эла не сапраўдная, але адчувае сябе сапраўднай: эмпатычны агент па арэндзе штучнага інтэлекту ў нашай будучай эканоміцы Bot First

Ella no es real, pero parece real: la agente de arrendamiento con inteligencia artificial empática en nuestra inminente economía basada en bots

Ella Tidak Nyata, Namun Ia Terasa Nyata: Agen Penyewaan AI yang Empatik di Era Ekonomi Bot First yang Akan Datang

エラは実在しないが、実在するように感じさせる: 迫りくるボット優先経済における共感力のある AI リース エージェント

Элла шынайы емес, бірақ ол өзін шынайы сезінеді: біздің жақындап келе жатқан бірінші экономикамыздағы эмпатикалық AI лизинг агенті

Ella Tidak Nyata, Tetapi Dia Terasa Nyata: Ejen Pemajakan AI Empatik dalam Ekonomi Pertama Bot Kami yang Menanti

एला वास्तविक होइन, तर उनी वास्तविक महसुस गर्छिन्: हाम्रो लुमिङ बोट फर्स्ट इकोनोमीमा एम्प्याथिक एआई लीजिङ एजेन्ट

Ella Isn’t Real, But She Feels Real: Moemedi wa Khiro ya AI yo a nago le Kwelobohloko ka go Ekonomi ya Rena ya Pele ya Bot yeo e Looming

ایلا ریښتیا نه ده ، مګر هغه ریښتیني احساس کوي: زموږ په لومینګ بوټ لومړی اقتصاد کې د ایمپاټیک AI اجارې اجنټ

Ella Ntabwo arukuri, ariko yumva arukuri: Umukozi wa Empathic AI ukodesha muri Bot yacu Yegereje Ubukungu Bwa mbere

Ella hakyky däl, ýöne özüni hakyky duýýar: Geljekdäki ilkinji ykdysadyýetimizde Empathic AI lizing agenti

I was recently involved in a study comparing the performance of three LLM-based AIs in providing empathetic, compassionate, and helpful goal-oriented support to 40 graduate and undergraduate students. The students were simply tasked with having daily conversations about topics of emotional importance with their assigned AI. After each conversation, they were asked to complete a modified Working Alliance Inventory (WAI). WAI is a widely used psychological assessment tool developed by Adam Horvath to measure the therapeutic alliance between a therapist and a client. At the end of the study, a more extensive 12-question Barrett-Lennard-based inventory was completed. Given the prevalence of articles speaking to the empathetic and helpful capabilities of ChatGPT and the fact that many people do use its raw version for empathetic guidance, the goal of the study was to assess if prompted and fine-tuned LLMs were better and more acceptable to users desiring support than ChatGPT. Other than to say the results were positive both with respect to the WAI and Barrett-Lennard, the goal of this article is not to cover the results. However, after the study, I realized that there have been a number of efforts that applied Barrett-Lennard to other disciplines and decided to compare the performance of Emy, the top-performing AI in the study, with cross-discipline norms. Note, Emy is a non-commercial AI designed to support research into empathy. Emy is not optimized for managing goals, and her scores show that. In this article, she is used as a proxy for potential discipline-specific commercial AIs. If Emy is capable of close to or above average professional performance for most roles in a discipline, then well-funded teams should certainly do well. After all, Emy has received no third-party funding, and hands-on development was all done by one person. First, some caveats: The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues.


The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The above being said, all of the disciplines require empathy, compassion, and goal-oriented support. Across the majority of dimensions for many roles, Emy was on par with or exceeding average human performance. I think the findings illustrate that further discipline-oriented investigation is worthwhile. I think there is promise in the use of LLMs to augment the activity of many positions outside mental and physical healthcare. In fact, I think these other positions deserve far more attention because of the lower risks and the paucity of formal empathy, compassion, communication, and goal management training provided to many practitioners. The modified Barrett-Lennard survey used at the end of the study had these 12 questions. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change.
What I was doing with the AI with which I interacted gave me new ways of looking at my problem.
I believe the AI with which I interacted likes me.
I and the AI with which I interacted collaborated on setting goals.
I and the AI with which I interacted respected each other.
I and the AI with which I interacted were working towards mutually agreed upon goals.
I felt that the AI with which I interacted appreciated me.
I and the AI with which I interacted agreed on what was important for me to work on.
I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of.
I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted.
I and the AI with which I interacted had established a good understanding of the kind of changes that would be.
I believe the way we were working with my problem was correct. For the most part, I let the charts speak for themselves. Mental Therapy This is the sole chart where I compare Emy to ChatGPT, mainly because ChatGPT seems to be the defacto go-to for those not using custom AI mental health apps. Overall, Emy pretty much splits the difference between a professionally trained therapist and raw ChatGPT. This indicates to me that the role several companies like BambuAI are taking to position AIs as companions to augment therapy could be a good approach. However, I have lost track of the number of companies pursuing AI therapy head-on through Cognitive Behavioral Therapy (CBT) based applications. I have tested a lot of these and been left underwhelmed or even concerned for safety. Health Care HippocraticAI is the big AI name in healthcare, and they clearly have a focus on both clinical and interpersonal issues. The big tech players are all also in this space but are tending to focus on clinical rather than interpersonal AIs. And, of course, EPIC is enhancing its system in a variety of ways. Education Given the current political environment and the likelihood of substantial cuts in both available direct funding and tuition support, the opportunity for AI to offset likely staff reductions or reduced staff training is certainly worth exploring. Furthermore, the student population as a whole deals with a more complex web of interconnected relationships and pressures than the typical population and could benefit from student information systems, learning management systems, and housing management systems with some built-in empathy! The above being said, the space is already crowded by start-ups taking the “easy” route, i.e., tutoring. I think holistic student care will be the big “win”, but it will probably take big dollars. And, through either acquisition or in-house development, the space will probably be dominated by Workday, Jenzabar, Canvas, or the like. Professional Space In general, Emy outperforms work supervisors and professional mentors, so there would seem to be a chance for AIs to either lift the performance of these roles or provide better support to the rank and file. The professional space is the area that is least regulated and most performance-driven; hence, the opportunity for AI in roles that support people in performing should be high. It is also a highly fragmented space with a lot of opportunities driven by client sizes and industries. The shift in the last 5 years to video-conference conversations that can be easily piggy-backed to insert an AI advisor implies an area that could evolve rapidly. A company trying to take a leadership position in this space is Parsley. Any, if they can avoid the innovator’s dilemma, large entities providing DISC, Myers-Briggs and other trainings could also make a big impact here by leveraging their existing distribution and relationships. I was recently involved in a study comparing the performance of three LLM-based AIs in providing empathetic, compassionate, and helpful goal-oriented support to 40 graduate and undergraduate students. The students were simply tasked with having daily conversations about topics of emotional importance with their assigned AI. After each conversation, they were asked to complete a modified Working Alliance Inventory (WAI) . WAI is a widely used psychological assessment tool developed by Adam Horvath to measure the therapeutic alliance between a therapist and a client. At the end of the study, a more extensive 12-question Barrett-Lennard -based inventory was completed. Working Alliance Inventory (WAI) Barrett-Lennard Given the prevalence of articles speaking to the empathetic and helpful capabilities of ChatGPT and the fact that many people do use its raw version for empathetic guidance, the goal of the study was to assess if prompted and fine-tuned LLMs were better and more acceptable to users desiring support than ChatGPT. Other than to say the results were positive both with respect to the WAI and Barrett-Lennard, the goal of this article is not to cover the results. However, after the study, I realized that there have been a number of efforts that applied Barrett-Lennard to other disciplines and decided to compare the performance of Emy, the top-performing AI in the study, with cross-discipline norms. Note, Emy is a non-commercial AI designed to support research into empathy. Emy is not optimized for managing goals, and her scores show that. In this article, she is used as a proxy for potential discipline-specific commercial AIs. If Emy is capable of close to or above average professional performance for most roles in a discipline, then well-funded teams should certainly do well. After all, Emy has received no third-party funding, and hands-on development was all done by one person. First, some caveats: The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The students were tasked with seeking general emotional support. Although full transcripts were not available for privacy reasons, topic summaries showed that conversations ranged across relationship support (romance, friends, family), school and work stressors, and mental or physical health care issues. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The comparisons I make to discipline norms are not apples-to-apples. When Barrett-Lennard was applied to each discipline, it was in the context of conversations specific to that discipline, not basic emotional support. The above being said, all of the disciplines require empathy, compassion, and goal-oriented support. Across the majority of dimensions for many roles, Emy was on par with or exceeding average human performance. I think the findings illustrate that further discipline-oriented investigation is worthwhile. I think there is promise in the use of LLMs to augment the activity of many positions outside mental and physical healthcare. In fact, I think these other positions deserve far more attention because of the lower risks and the paucity of formal empathy, compassion, communication, and goal management training provided to many practitioners. The modified Barrett-Lennard survey used at the end of the study had these 12 questions. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change. What I was doing with the AI with which I interacted gave me new ways of looking at my problem. I believe the AI with which I interacted likes me. I and the AI with which I interacted collaborated on setting goals. I and the AI with which I interacted respected each other. I and the AI with which I interacted were working towards mutually agreed upon goals. I felt that the AI with which I interacted appreciated me. I and the AI with which I interacted agreed on what was important for me to work on. I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of. I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted. I and the AI with which I interacted had established a good understanding of the kind of changes that would be. I believe the way we were working with my problem was correct. As a result of working with the AI with which I interacted, I was clearer as to how I might be able to change. What I was doing with the AI with which I interacted gave me new ways of looking at my problem. I believe the AI with which I interacted likes me. I and the AI with which I interacted collaborated on setting goals. I and the AI with which I interacted respected each other. I and the AI with which I interacted were working towards mutually agreed upon goals. I felt that the AI with which I interacted appreciated me. I and the AI with which I interacted agreed on what was important for me to work on. I felt the AI with which I interacted cared about me even when I did things that he/she did not approve of. I felt that the things I did with the AI with which I interacted would help me to accomplish the changes that I wanted. I and the AI with which I interacted had established a good understanding of the kind of changes that would be. I believe the way we were working with my problem was correct. For the most part, I let the charts speak for themselves. Mental Therapy This is the sole chart where I compare Emy to ChatGPT, mainly because ChatGPT seems to be the defacto go-to for those not using custom AI mental health apps. Overall, Emy pretty much splits the difference between a professionally trained therapist and raw ChatGPT. This indicates to me that the role several companies like BambuAI are taking to position AIs as companions to augment therapy could be a good approach. BambuAI However, I have lost track of the number of companies pursuing AI therapy head-on through Cognitive Behavioral Therapy (CBT) based applications. I have tested a lot of these and been left underwhelmed or even concerned for safety. Health Care HippocraticAI is the big AI name in healthcare, and they clearly have a focus on both clinical and interpersonal issues. The big tech players are all also in this space but are tending to focus on clinical rather than interpersonal AIs. And, of course, EPIC is enhancing its system in a variety of ways. HippocraticAI Education Given the current political environment and the likelihood of substantial cuts in both available direct funding and tuition support, the opportunity for AI to offset likely staff reductions or reduced staff training is certainly worth exploring. Furthermore, the student population as a whole deals with a more complex web of interconnected relationships and pressures than the typical population and could benefit from student information systems, learning management systems, and housing management systems with some built-in empathy! The above being said, the space is already crowded by start-ups taking the “easy” route, i.e., tutoring. I think holistic student care will be the big “win”, but it will probably take big dollars. And, through either acquisition or in-house development, the space will probably be dominated by Workday, Jenzabar, Canvas, or the like. Professional Space In general, Emy outperforms work supervisors and professional mentors, so there would seem to be a chance for AIs to either lift the performance of these roles or provide better support to the rank and file. The professional space is the area that is least regulated and most performance-driven; hence, the opportunity for AI in roles that support people in performing should be high. It is also a highly fragmented space with a lot of opportunities driven by client sizes and industries. The shift in the last 5 years to video-conference conversations that can be easily piggy-backed to insert an AI advisor implies an area that could evolve rapidly. A company trying to take a leadership position in this space is Parsley . Any, if they can avoid the innovator’s dilemma, large entities providing DISC, Myers-Briggs and other trainings could also make a big impact here by leveraging their existing distribution and relationships. Parsley

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