Brief
The U.S. merchant account services market is a battlefield. New tech disruptors are storming the field, legacy giants are digging in, and a handful of former titans are losing ground fast. This is not just a story of numbers, but of people, technology, and the relentless drive for relevance. Here’s who’s winning, who’s losing, and why it matters for your next career move, your sales strategy, or your business’s bottom line.

The SMB Payments Market at a Glance

Sources: TSG Payments, Business News Today, company filings, industry interviews

The SMB Payments Rising Stars: The Disruptors Changing the Game

Shopify POS
Shopify is no longer just an e-commerce platform. Its POS system is now the go-to for D2C brands and pop-ups, letting retailers unify online and in-store sales, launch new locations in weeks, and leverage deep customer data.

“Shopify POS gives us one customer view whether they click or tap a card. That’s been a game-changer for our retail expansion.”
— Travis Boyce, Head of Global Retail, Allbirds (Shopify Blog)

Retail on Demand
This shift toward omnichannel isn’t just theory. In May 2024, sleepwear brand Lunya opened three NYC pop-ups in just 21 days using Shopify’s “tap-to-open” iPhone POS. Local press called it “retail on demand,” and Shopify’s stock jumped 7% that week as analysts cited the event as proof of Shopify’s physical retail momentum.
Read more – Women’s Wear Daily, 5/17/24

Toast
Toast is the restaurant industry’s secret weapon. With features like ingredient tracking, kitchen displays, and payroll, it’s not just a POS—it’s a full restaurant operating system. Sweetgreen credits Toast with reducing waste by 15% and speeding up service by 20%.

“Toast isn’t a POS; it’s our sous-chef.”
— Jonathan Neman, CEO, Sweetgreen (Business News Today)

Tip War Fallout
This focus on specialization paid off during Toast’s “tip war” controversy in 2023. After media and customer backlash over automatic tip prompts, Toast responded by rolling out customizable tip screens and launching a “restaurant tech bootcamp” for staff. This quick, industry-specific response restored trust and improved client retention, showing how vertical focus can turn a crisis into a loyalty win.
Read more – The Wall Street Journal, 7/18/23

Helcim
Helcim is winning over cost-conscious SMBs with radical transparency. Its viral moment came when a Reddit thread revealed real savings and honest pricing, leading to a 50% spike in sign-ups.

“Dropped my processing bill from $400 to $320 the first month—no PCI junk fees, no ‘non-qualified’ surcharges.”
— u/mainstreetbooks (r/smallbusiness)

Reddit Goes Viral
This shift toward transparency isn’t just theory. In 2024, a viral Reddit post titled “Helcim vs. Everyone” saw dozens of business owners share screenshots of their savings after switching to Helcim. The company’s CEO even joined the discussion, answering questions directly. This public engagement and openness led to a measurable surge in new customers, confirming that transparent pricing is now a competitive necessity.
Read more – Helcim Blog, 6/2/24

The SMB Payments Giants: Defending Their Turf

Square (Block)
Still the king for microbusinesses and retail, Square’s ecosystem is unmatched for simplicity and breadth. Its new AI-driven loyalty and inventory tools are helping retailers like The Sill boost sales and reduce out-of-stocks.

“Square’s AI-driven inventory suggestions have helped us keep our bestsellers in stock and cut lost sales.”
— Eliza Blank, CEO, The Sill (Business News Today)

AI Boosts Sales
The power of AI isn’t just hype. In January 2025, Square rolled out its AI Loyalty Lab to 10,000 pilot merchants. Early results showed a 7% increase in average basket size. The move was covered by Forbes, which called it “a shot across the bow at legacy POS.”
Read more – Forbes, 1/15/25

Clover (Fiserv)
Clover’s strength is its deep integration with banks like Bank of America, making it the default for many new business accounts. Its hardware and app marketplace keep it competitive.

“Our partnership with Clover has made it easier for small businesses to get the tools they need, right from their bank.”
— Sharon Miller, President of Small Business, Bank of America (Business News Today)

Bank Partnership Pays
This strategy isn’t just theoretical. In Q1 2024, Bank of America began bundling Clover POS with new business checking accounts. The result was a 25% jump in new merchant accounts and a surge in positive reviews, demonstrating the power of bank partnerships in driving POS adoption.
Read more – American Banker, 4/2/24

PayPal/Zettle, Stripe, Adyen
These giants dominate online and hybrid commerce, with Stripe and Adyen especially strong among tech-forward and global SMBs.

DOJ Cracks Down
The importance of fair competition was highlighted in 2023, when Stripe agreed to a $120 million settlement with the U.S. Department of Justice over alleged anti-competitive practices. The company responded by opening its API to more third-party integrations, which analysts say helped it regain trust among developers and SMBs.
Read more – Reuters, 9/14/23

The SMB Payments Losers: Why the Old Guard Is Retreating

Worldpay (FIS), TSYS (Global Payments), Elavon
Once titans, these legacy providers are losing contracts and sales talent at an accelerating pace.

• Worldpay lost a multi-state apparel chain to Shopify after a repricing standoff; leaked memos cite “product gaps vs. omnichannel challengers.” (Brand Spur NG)
• TSYS and Elavon are seeing double-digit attrition as reps defect to disruptors and SMBs demand better tech and clearer pricing.

Sales Team Exodus
The impact of outdated tech and poor support became clear in late 2024, when a viral LinkedIn post by a former Worldpay sales manager described a “mass exodus” of reps to Square and Toast. The post cited “opaque quotas and outdated tech” as key reasons for leaving, sparking a debate on Glassdoor and highlighting how lack of support and innovation can drive talent away.
Read more – LinkedIn, 11/18/24

The SMB Payments Talent War: How the Best Recruit, Train, and Keep Sales Teams

Bootcamp Boosts Retention
The value of investing in sales talent was proven when, after launching its “Restaurant Tech Bootcamp,” Toast saw a 30% increase in new-hire retention and poached dozens of sales leaders from legacy providers, as reported by Business News Today.
Read more – Business News Today, 8/15/24

The SMB Payments Market players. What Should You Do?

For Job Seekers:

• Don’t just ask about commission—ask about inbound leads, demo support, and product fit.
• Choose companies that invest in onboarding and have a product you’re proud to sell.

For HR and MASP Leaders:

• Speed up onboarding, invest in training, and be transparent about comp and quotas.
• Build partnerships and vertical expertise to stand out.

For SMB Owners:

• Use the competition to your advantage: get multiple quotes, demand clear pricing, and ask for references.
• Choose a provider that understands your industry, not just payments in general.

The Bottom Line

The war for America’s SMB payments is being won by those who move fast, solve real problems, and treat both merchants and employees as partners. The old playbook is obsolete. The new one is being written by rising stars, defended by giants, and abandoned by those who can’t keep up.

Sources & Further Reading

• TSG Payments, 2025 Directory Rankings
• Business News Today: Top POS platforms in 2025
• Shopify Blog: Merchant Services
• Brand Spur NG: Top Ten Payments Companies 2024
• Newsweek: Best Merchant Account Service Providers 2025
• Women’s Wear Daily: Shopify Pop-Up Lunya
• Forbes: Square AI Loyalty Lab Launch
• American Banker: Bank of America Clover POS Bundle
• Helcim Blog: Helcim vs. Legacy Processors
• LinkedIn: Worldpay Sales Exodus
• Reuters: Stripe DOJ Settlement
• The Wall Street Journal: Toast Tip Feature Controversy

Join the Discussion:
Is this war good for SMBs, or does it just create confusion? Can legacy giants stage a comeback, or are they doomed to retreat? Will the next big winner be a tech disruptor, a niche specialist, or a company we haven’t even heard of yet?
Share your stories, predictions, and questions below. The next chapter in the battle for America’s SMB payments is being written right now—will you be part of it?

By NEUROMARKET
Hobby blog about neural networks: https://t.me/+uBkKVdA–zhhYTRi
I don’t look for clients, I don’t warm up, I don’t sell, and I don’t swindle. I write and translate about what interests me.

…

Perplexity AI (The American startup) has secured $73.6 million in investments from a group of investors, including Amazon founder Jeff Bezos.

Moreover, Perplexity AI responds much better to many queries compared to Google.

Instead of showing you advertisements and links during searches, Perplexity uses artificial intelligence to provide immediate answers.

How to use the Perplexity?

1. Visit the Perplexity AI website.
   .
2. Ask a question or search for something in the search bar.
   .
3. Perplexity will respond to your question.
   .
4. You can follow links attached to the answer, access images or videos by clicking on buttons on the right side of the screen, or find related search queries at the bottom of the page.

I’ve been using it for several weeks and found that Perplexity genuinely provides good answers to most questions. The service supports the Russian language and works in Russia.

Do you think the era of informational websites is coming to an end?

Even more useful content is available on my Telegram channel about neural networks and the channel about image generation.

By NEUROMARKET
Hobby blog about neural networks: https://t.me/+uBkKVdA–zhhYTRi
I don’t look for clients, I don’t warm up, I don’t sell, and I don’t swindle. I write and translate about what interests me.

My eye twitched when I tried to find an apartment for my parents. No longer:
I have launched the HomeBro website, which searches for apartments on all the top real estate listing sites. All property offers on one map, plus notifications sent to Telegram.

Hi! My name is Lev. Four years ago, I created the HomeBro bot that searches for housing for free and sends you options directly to Telegram. I’ve rented an apartment in Moscow many times, and I desperately wanted to simplify this process. As a result, half a million people have already used the bot.

…

This time, I was helping my parents buy an apartment. And here I felt firsthand how our bot is not sufficient for the process of buying a property.

It was both amusing and bitter that as the founder of a housing search service, with access to a database of all property listings, I couldn’t conveniently explore it myself.

I wanted something simple. To not only receive new options in the bot but also to open a map and see everything available on the market. In the end, once again, like four years ago, I find myself opening websites one after another. I see the same thing 10 times in an attempt to find something new. In short, it’s a mess.

• I looked at 17 advertisements about this apartment.
  
  
• Did you find it without commission?

…

HomeBro: And then, with the team, we created a real estate classifieds aggregator website.

Welcome to the HomeBro portal! Here, you’ll find all the latest listings from Cian, Domclick, Yandex.Real Estate, M2, Avito, and Samolet.Plus. And the list will continue to grow.

Currently, the site has over 1 million active listings, with 50 thousand new ones appearing every day.

You can already visit and search for housing for rent or purchase in 18 cities. It covers all Russian million-plus cities, including Sochi and Arkhangelsk – where one of our developers lives =)

And all this without duplicates. Well, almost =D Each housing option is shown only once.

…

It’s interesting, will they provide a preferential IT mortgage for rental properties?

Also, our trained neural network (I wrote about it here) helps find an apartment without grandma’s renovation, closer to the park, or farther from the roads. Well, or you can deliberately look for a fixer-upper to buy cheaper and renovate.

Setting up notifications for all new listings can be done in two clicks (literally) by transitioning from the website to our bot.

Yes, did you make something really cool?

Hey, folks, the thing is working, but this is the very first version, commonly known as MVP.

We, with a very small team, have been building the portal since the beginning of the year; there are still some glitches. But there’s no point in hiding from people a service that, even in its current form, can bring real benefits.

…

A big thank you to everyone supporting us in our crowdfunding campaign; every ruble of yours is important to us. By the way, we almost didn’t promote it—mentioned it only in one article and did a one-time mailing in the bot. It triggered me when someone said, “You’re begging for money on VC.” As a result, we stopped writing about it. But I was wrong; haters gonna hate.

Welcome to all the found bugs. Visit the HomeBro website and don’t hesitate to say what you felt was lacking. I’ll be incredibly grateful for your comments.

By Lev Klebanov
I am developing a housing search service https://homebro.ru

The text is published with the permission of the author.
The original text is here.

By Rostislav Dugin
TG: @rostislav_dugin
https://click-chat.ru/
The text is published with the permission of the author.
The original is here.

The article about how I almost missed the “boom” of ChatGPT but gained access to it and made money (and continue to earn). The project itself is currently declining, but I’ll discuss that at the end.

…

Table of Contents:
– Who am I
– How the idea came about
– How the project was made from the technical side
– How I attracted my first clients
– Insight: cheating behavioral factors
– How the project grew
– Insight about subscriptions
– What were the problems?
– How Yandex killed SEO
– What are your plans next?
– Conclusions

Who am I

My name is Rostislav, and I am a Full-Stack developer. For those reading my content for the first time, here are links to other articles:

1. “5 Tips for Developers Before Starting Their Startup. Experience After 750,000 Rubles”
   .
2. “Solo Startup. Part 5. How Much Does the Project Bring in Now?”
   .
3. “Solo Startup. Part 3: Simplifying the Product”

Now my main project is a Telegram chat for the site (which additionally shows what city the user is writing from and when he is online).

If it is relevant to you or you want to connect to the affiliate program: ClickChat.

…

How did the idea come about?

At the end of 2022, ChatGPT gained popularity. Everyone was talking, writing, sharing stories, and creating memes about it. I successfully ignored this moment, letting it pass me by.

In February, I stumbled upon a free ChatGPT bot on Telegram. I sent it a few messages, played around with it, and then forgot about it. Some time later, I realized that it could have been an excellent time to launch a project with ChatGPT.

Closer to mid-March, it struck me: ChatGPT has an API! Consequently, an idea emerged: what if I localize the API and resell it to legal entities? They would pay me in rubles, and I would purchase from OpenAI in dollars, pocketing the difference.

A couple of days later, I simplified the concept. Selling to B2B is complicated. There’s an easier way! I’ll sell directly to end-users. I just need to create a website, set up advertising, boost SEO, and the SaaS is ready. Subscriptions can be sold. I’ll focus mainly on SEO (spoiler: it worked).

And so, ChatGPT Me website was born. I deliberately chose a domain with “ChatGPT” to enhance SEO visibility for that search term.

…

How the project was made from the technical side

From a technical perspective, everything is relatively straightforward: when a user makes a request in the chat, I forward the request to the OpenAI ChatGPT API and display the response to the user.

For subscription payments, users are allocated tokens that ChatGPT consumes. I use a tokenizer to analyze token consumption.

Revenue is derived from two components:

1. Unused tokens at the end of the month.
2. My percentage on top of the tokens I provide to the user.

The server-side is developed in NodeJS + NestJS. The main part of the website is built with NextJS, and the landing page is created using Svelte (I experimented to achieve maximum Google Page Speed). The PostgreSQL database is used, and Redis serves for caching and as a message broker. Nginx acts as the HTTP proxy server.

I developed the first version in about a month, albeit at a relatively slow pace due to limited time.

…

How did I attract my first clients?

I initiated the promotion with the following actions:

1. Purchased links through SeoWizard and posted in various software catalogs.
   .
2. Launched advertising on Yandex.Direct (what surprised me was that at that time, few were advertising under the ChatGPT query).
   .
3. Wrote several articles on vc.ru, pikabu, and Habr. However, most of them were either deleted or downvoted almost immediately (fairly, but traffic is traffic).

I had notifications set up on Telegram for every new registration. I wasn’t particularly expecting a sudden success and didn’t realize at the time that this might not be the best solution. Overnight, the chat with notifications was flooded with messages, and the next day, my phone was buzzing every 20-30 minutes.

It looked like this:

Messages about registrations

Many people were registering, but the payments were minimal. I had to disable registration notifications and keep only payment notifications:

Very pleasant notification sound

Overall, the launch turned out to be quite successful. In the first month of the project’s existence, I invested around 30,000 rubles and earned roughly the same amount. Breaking even is already a success!

For a moment, I even felt like a successful entrepreneur.

Insight: behavioural factors

From the very beginning, I understood the importance of allowing users to try the product. That’s why 10 free messages were available in the chat.

At some point, I analyzed and realized that students were registering massively just for the sake of 10 free messages. Here’s an example from this month; take a look at the emails (I don’t verify them through link confirmation):

…

Sign-ups for free messages

And these sign-ups… darn well boost behavioral factors for search engines! I gave a little freebie to the students, and in return, they gave me positions in SEO. It turns out to be a win-win situation.

How the project grew

After a few weeks, the site shot up in the SEO rankings for the query “ChatGPT,” and I decided to stop advertising. I only invested in purchasing links and gradually improved the chat.

Here’s the registration graph for all months from April:

…

May shows a sharp increase in sign-ups due to SEO

All this time I treated the project as a “temporary topic”, so I did not expect to earn millions. I just rejoiced at every peak and waited for all this happiness to end.

In early May and mid-July, the project was pushed to the top positions. These days, the number of subscriptions has been growing in some abnormal amounts. The notification had been on silent for a long time, and the chat looked like this:

…

Messages for July

On good days, the chat brought in 5,000 to 7,500 rubles. About 70% of the revenue was clear profit! I was really hoping that this would last forever (spoiler: it didn’t).

Insight about subscriptions

At some point, I noticed that registrations and subscriptions were increasing, but my payment to OpenAI remained consistently around 10,000 rubles. So, I decided to analyze how many people were actually using their tokens, even if only halfway.

It turned out that only 2.5% of users had ever spent 50% of their available monthly token limit. Despite peak subscriptions exceeding 90,000 rubles per month.

So, roughly 90% of people were essentially giving me money without fully utilizing what they had purchased. This was an incredible insight for me, and it dawned on me the entire benefit of SaaS services.

Now, imagine how much gyms make with monthly memberships or services like Yandex.Plus (although I do use Yandex.Plus).

Moral of the story: making money from subscriptions is the easiest. Subscribing to a small amount is psychologically easy, but over 5-10 months, it accumulates into a substantial sum almost unnoticed.

…

What were the problems?

And now a bit about the challenges. In my opinion, this project brought in easy money, but it’s important to mention the moments where I messed up.

1. Dropped the User Database (epic fail!):
   .
   Registration through the website is done via email. In the beginning, I didn’t validate fields much, and it was possible to enter two identical emails with different capitalisation.
   .
   For example, two different emails for the system: “rostislav@mail.ru” and “Rostislav@mail.ru”.
   .
   At some point, a user couldn’t log into the right account because she used a capital letter in the email. I decided to fix the email registration case by converting everything to lowercase. However, this user specifically asked me to manually change her email. So, I went into the database to update the email manually using an SQL script. I first wrote a WHERE script to check for only one email. Then, for the same WHERE, I wrote the UPDATE script.
   .
   Apparently, I forgot to close a bracket somewhere. I ran it. Within a second, all ~25,000 emails became NULL. At that moment, I felt a cold sweat, and I froze for two minutes. But the backups were in place, and I recovered. I went to change into a dry T-shirt.
   
   – Lesson 1: Even experience and precautions don’t always save you from childish mistakes.
   .
   – Lesson 2: Make backups.
   .
2. Publication on RuStore:
   .
   At some point, I decided to create a mobile application to appear in RuStore under the “ChatGPT” query. Since there were no such applications, the idea had a chance of success and could provide an additional source of traffic. I quickly learned React Native, hastily made the application, went to publish it, and… RuStore didn’t allow the use of the name ChatGPT.
   .
   Fair, but sad. I published it under a different name. After a week, I deleted the application because there were very few registrations, and the application was an eyesore.
   .
3. Servers in Moscow:
   .
   Initially, the API worked fine for all regions. At some point, OpenAI decided to block access from Russia, including for the API. I had to urgently move to the Netherlands (on new servers, I mean). Due to the domain redirection, there was a day of downtime. This situation should have been anticipated in advance.

How Yandex killed SEO

In September, I expected a rapid project growth because schools and universities were reopening. My target audience once again needed access to boundless information! “I’ll buy myself a car with this money!” — I thought at some point. But on August 30th, I received this notification in Yandex Webmaster:

…

You are “mimicking”.
“Violations and security threats on the website http://chatgpt-me.ru, its individual sections or pages.”…

At first, I didn’t understand what it was. Then, I Googled it and found out that “mimicry” is when a website tries to impersonate another popular site.

I’m not pretending to be ChatGPT (nor do I look like it), I’m just using their name almost honestly for SEO promotion. Odd…

I contacted Yandex support to figure out what was wrong. However, their support didn’t provide much help. They simply said it’s all search algorithms, and I should figure it out myself. “If you fix everything, your site will return to search results in 30 days.”

Thirty days without appearing in search results is almost a death sentence for the project, leading to a shift of all positions to competitors (and by September, there were already plenty of such chat projects). In general, the situation emerged where the site was knocked out of the search results, and no one could clearly say what needed fixing.

I tried various methods, like rewriting the text, removing the word “ChatGPT” from the title, and so on. In the end, nothing helped.

From this moment, the project’s gradual decline began.

Since I treated the project as a “temporary thing” and expected something like this, I was a bit sad for a couple of days and then calmed down. Perhaps Yandex did the right thing (after all, such algorithms are not invented for nothing). Moreover, Yandex sometimes acts faster than Google, and all its services (almost) are pretty cool.

The only thing I would have liked in this situation is a clear explanation of what exactly was wrong and assistance in fixing the site, not “wait 30 days and figure it out yourself through trial and error.”

After all, if a genuinely serious site ends up mistakenly blocked in this way, it will cause a lot of problems for the owner with no way to seek help.

…

What are my plans next?

The site has occasional registrations, and there are around 60,000 rubles in subscriptions per month. That’s why I’m maintaining the project, not actively developing new features. When subscriptions completely dwindle, and servers become more expensive, I’ll close it. But I feel that this will be a year or more from now, if not later.

Conclusions

Over the past six months, I launched a project and managed to earn from it. This time, my experience helped me make the project profitable almost from the start. I gained valuable insights, encountered new challenges, and solidified my understanding that opportunities are everywhere (even though some said, “Why create a site with ChatGPT? There are already plenty of bots, and you can just use a VPN”).

Currently, my main project is a Telegram chat for the website. It’s more reliable and interesting, and there’s profit as well. So, I continue to invest all my available efforts into its development. I’ll share more about how I’m expanding ClickChat in the upcoming articles.

I hope my experience inspires you to start (or continue) something of your own.

And give it a like. You know, like a brother.

By Rostislav Dugin
TG: @rostislav_dugin
https://click-chat.ru/
The text is published with the permission of the author.
The original is here.

Hello! My name is Vlad Proshinsky, I have been launching and developing IT products for 8 years in the role from Product Manager to CPO. For the last 8 months I have been studying how AI and LLM can be applied in products and business processes. I am convinced that knowledge in the field of neural networks will give managers and founders a great advantage

In this article, I want to share my strategic vision, cases of using generative AI in products and in automating business processes of world-class companies.

Generative AI is a type of AI that creates text, photo, video, audio and other content at scale using data arrays (LLM) and machine learning (ML)

Generative AI has had an impact like no other technology in 10 years. For six months, ChatGPT managed to prove that neither the cost of computing, nor the complexity of the infrastructure, nor human resources are no longer an obstacle to mass adoption, the AI transformation has begun.

According to a McKinsey report, AI could generate an additional $13 trillion in growth. by 2030, increasing global GDP by 1.2% per year.

Domination and strategic context

Mass adoption of technology in the active phase, not only in personal use, but also in business processes. The productivity gains of developers and knowledge workers using GPT-based products are massively proven, and have forced organizations to rethink their business processes and the value of human resources. The way product solutions based on GPT and other neural networks develop, how AI is integrated into everyday life of a person and business is another proof that AI is a strategic necessity.

Source: Bloomberg News, income statement analysis. Note: analysis in the context of the terms “AI” and “Artificial Intelligence”. Data for July 27, the share of Nasdaq100 informing companies was 40%

In this quarter’s earnings report, US tech CEOs are talking less about the problems and more about how they plan to use AI to boost revenue and lower costs. Mentions of “AI” have increased more than 4 (!) times compared to 2022, indicating that AI has become a key driver of competitiveness and innovation in the technology industry.

The AI winners take it all

“Winner takes all”, and in the context of AI, this expression has a direct connotation. In the technology race, leaders are afraid of being left behind if they slow down and others don’t. This “prisoner’s dilemma” from game theory creates risks for responsible AI practices. Speed-to-market leaders are driving the current “arms race” in which big corporate players are rushing to release neural network-based products.

CEOs and founders are actively pushing teams to find ways to incorporate generative AI into their products and processes, but to create value with these technologies, you need to understand how they work, where they will bring benefits, what their limitations and risks are. Such competencies are usually rare among top management. Therefore, they need to be looked for on the side, on platforms like CareerPaths or freelance platfroms like Upwork or Fiverr.

In the article, I will briefly touch on the development of technology, as a product specialist, I am interested in how this technology will change the needs of users, ways of solving problems, and most importantly: “how to make money on it?”, After all, the task of a business is to make a profit. But according to statistics, 87% of ML projects do not even reach implementation, and the main problem is that few people now have comprehensive knowledge about what kind of “cube” in the form of an AI solution to build into the system, how to do it, how everything is not “ break”.

Common problems teams face:

• Wrong processes of workflow on an AI project
• Misidentifying Opportunities for AI Applications
• No vision (experience) → a lot of resources are wasted
• Business and products still do not speak the language of DS and ML engineers
• Do not identify possible risks at the start
• Passion for experiments, ignoring business goals

In today’s realities, businesses need managers who will see strategic opportunities to implement AI where possible, as well as build operational end-to-end processes.

Strategic vector

Strategic innovations that require special attention (2 to 5 years to mainstream adoption) include generative AI and decision intelligence. Early adoption of these innovations will lead to a significant competitive advantage and will simplify the challenges associated with using AI models in business processes.

Gartner

According to the graph provided by Gartner, general AI (AGI, artificial general intelligence), also known as “artificial intelligence”, capable of finding a solution for arbitrary tasks and learning to infinity, will appear in about 10 years. The forecast of the Metaculus community is slightly more optimistic – January 2027.

Obviously, development progress towards AGI is artificially hindered: the first letter demanding to immediately suspend training of more powerful models than GPT-4 was published in March, the second – in May. As a result, in mid-April, OpenAI announced that they had suspended the training of the GPT-5 model and were engaged in “other things” (fast API and reducing the “AI cost”), and on July 30, the same OpenAI applies for the GPT-5 patent… Process irreversible.

An intermediate step is “causal / casual AI” (Casual AI) is a new type of AI that can detect and reason about the relationship of causes and effects, as well as determine the strength of each connection. Causality is very important for subsequent progress. It is characterized by: high autonomy, adaptability, making informed and logical decisions.

Causal AI is a key stage in the development of AI towards greater decision-making automation, autonomy, reliability and common sense

Gartner

On July 14, 2023, Elon Musk announced the launch of xAI, the goal of which is to “understand the true nature of the Universe,” and Casual AI is exactly what is needed to solve such problems. Among the early tools: Auto-GPT, BabyAGI, SuperAGI, ToddlerAGI.

Current (operational) context

Now in most AI products under the hood is GPT-3.5 or GPT-4, which is further trained to solve specific problems within the product, and the most popular product is ChatGPT, which gained 1 million users in a record 5 days in 2022.

The appearance of GPT-5 will undoubtedly improve the quality of products and open up new opportunities for solving problems and ChatGPT itself. Thus, your GPT assistant, which can help you do your work more efficiently, will become smarter over time, and the quality and speed of tasks will grow exponentially.

Companies and professionals who have implemented AI tools in their work win in the moment and over time, the gap from those who do not use them will rapidly lose to more advanced ones.

Why is this so important right now?

A report by Microsoft researchers explains how the GPT-4 language model learns human thinking skills. Can this be considered a milestone in the emergence of AGI (Artificial General Intelligence)?

Given the breadth and depth of GPT-4’s capabilities, we think it would be reasonable to view it as an early but still incomplete version of General AI (AGI).

GPT-4 has signs of human thinking, the argument is that GPT-4 achieves human-level performance in many tasks.

S´ebastien Bubeck (Microsoft Research)

ChatGPT can be considered as a mediocre assistant, the level of which depends on the skills of prompting (the ability to communicate with neural networks in the form of prompts), but now it is the starting point for a completely different future, which will come in 3-5 years.

Product changes

Global product companies are actively implementing AI into their products, adding value by increasing the speed of work and solving problems from scratch, this allows you to better activate users in key JTBD scenarios. Examples:

• Zoho added ChatGPT in Notebook
• Taboola planned to add ChatGPT to your advertising platform
• Microsoft added ChatGPT in Bing and Edge
• Google implemented AI in Docs, Sheets
• Miro released an AI launchpad (as they did in Notion earlier)
• UIzard added ChatGPT to generate landing pages and UI applications
• BuzzFeed planned use ChatGPT to create quizzes

AI Tools

A small list of tools that you can start using in your work and in the company:

Marketing

• search for hype, trends, insights: AnswerThePublic – listens to autocomplete data from Google queries. Neil Patel says: Users share their deepest secrets with the search engine, which creates value for companies that aim to get to know their customers better.
• create articles and posts up to 10 times faster: Copy.ai, CoWriter, Jasper, Phrasee, AI Writer, WriteSonic, HyperWrite, GetGenie Ai)
• hyper-personalization: Persado has developed “motivational AI,” as the service claims: «Brands using Persado AI are seeing a 41% increase in conversions across digital channels». Their algorithm is focused on achieving a positive ROI
• creating banners and creatives: AdCreative.AI, Pebblely, Fotor, Designer
• creating sales videos: Designs.AI, Kaiber, Elai.io
• competitor website monitoring: Browse AI 
• LP and sites: Landingsite.ai, Framer, Uizard , Durable, SiteGPT, 60secSite
• creating realistic people with your product: Caspa
• SEO: Craftly.AI, SEO.app, Charm, SEMRush, Moz, Market Brew
• AI for email: Animaker, Postcards 
• presentations: Pitchbob, Tome, Designs, AI Colors, Gamma, Pitchyouridea

Sales

• sales forecasting: RCMX
• train salespeople on effective models: Fiber AI
• digital sellers: Rask.AI, Immersive Fox, Veed AI, Elai.io
• call analysis, translation from audio to text: Fabius, SalesAI
• autocomplete customer cards: SalesAI

Product

• look for growth points in a product for products: Kraftful
• user retention: Subsets
• sort user issues: AppFollow, Charm
• segment users: Obviously AI
• generate growth hypotheses: Goal AI
• analyze customer feedback: Rargus
• conduct user research: Outset.ai, Iris.ai
• conduct market analysis: Silatus, Pitchbob.io
• Prompt engineering for products: AI Product

Support

Forbes writes that Meta , Canva , and Shopify use ChatGPT-based solutions in their customer support chatbots. Support Solutions: Kapa , Clueso , Ada , MagicReply

Development

According to GitHub, Co-pilot is involved in 46% of the code (among those who use it) and helps to write code 55% faster

• business process automation: MagicFlow , Bardeen , Levity.ai
• assistants: Co-pilot , Sweep , CodeStory , Autocode , GPT Engineer , Tabnine
• code review: AI Code Reviewer , Backend-GPT , Planar
• creating LLM language models: Flowise , GradientJ
• code auto-documentation: Stenography , Mintlify
• Cody – helps you read, write and understand code
• Giskard – helps reduce performance errors in AI models
• Lightning – helps train, deploy and build AI
• Tempo – writes the front-end
• AutoInfra – DevOps and Infra automation
• Structured – parsing logs

Finance

• budget optimization and forecasting: FinanceGPT , OpenOs , ObviousAI
• work with data in the “request-response” format: Code Interpreter , Seek AI
• chat with PDF: AskYourPDF , ChatDOC , ChatPDF , Fileread AI
• database analysis: Loofi , Baselit, Noteable (SQL, Python, & R)
• AI training on company data: Fine-tuner , ChatNode , Fini
• financial assistant: Quill AI
• workflow: Deep Cognition , Acodis , Mindee , Smart Touch

HR / recruitment

• automation of the recruitment process (job sites → resume analysis with ChatGPT)
• layoff forecasting ( Xsolla case )
• resume preparation: HyreSnap , Resume.io
• Interview Coach: Yoodli

Management / Management

• Kona – Executive coaching
• Stellar – sets goals / KPIs in the company and monitors their implementation

It is important to note the nuances of working with GPT:

• “Garbage in → garbage out” is a clear description of ChatGPT. First, master the skills of writing prompts ( cheat sheet ).
• Hints and questions lead to better results. If ChatGPT does not know the answer, it will invent one (needs to be double-checked) and can forge sources and give plausible but incorrect or nonsensical answers
• the accuracy (accuracy) of the answers may be reduced. Study of accuracy reduction from 52% to 10%

In summary, I want to note that at the stage of business growth, experiments play a special role. Testing hypotheses is one of the main tasks of marketers and product developers. By testing the listed tools, there is a high probability that you will be able to find points of increase in efficiency in your work.

***

The effect of the introduction of Synthesia AI avatars , as examples of growth that we plan to implement in one of the client’s projects:

“Thanks to AI avatars, we had 35% more meetings than at the last exhibition

Infinite Peripherals / VC of Marketing

The training team cut 50% of the cost of voice over video compared to hiring multilingual professionals

Xerox / Global L&D Sales Learning Strategy Manager

We reduced video production time by 95% from weeks to hours.

Network Rail L&D / Multimedia Development Manager

We increased engagement in online learning by 30% compared to text modules

BSH / Global Supply Chain Training

Open source AI

Separately, I will highlight open source solutions. They are more suitable for medium-sized businesses, for projects that require more flexibility, security, and budget savings.

According to Trends.vc analytics , open source AI is helping companies build infrastructure faster by learning from each other. Companies that offer closed source AI decide when and what you can use it for. Open source AI helps us learn from and build on each other’s work, it turns the AI ​​arms race into collaboration.

Open source platforms

• Huggingface – creating and deploying AI Open-source models
• Replicate – create and run AI models in the cloud
• Google Colab – for machine learning research
• Difty – open-source for LLMOps, AI-native applications

In the future, there will be more platforms built to host open source models. They will simplify the creation and deployment of models.

Open source models

• Panda GPT – will create a description from the picture, write stories, videos and answer questions from the audio. Can simultaneously accept multimodal inputs and compose their semantics
• Stable Diffusion – transforms text into an image (text-to-image)
• Riffusion – converts text to audio (text-to-audio)
• BLOOM – open multilingual model
• DragGAN – changes the poses, shape, expression and location of objects
• Falcon – LLM for Research and Commercial Use

Open-source datasets

• The Pile is a dataset of books, web pages, chat logs, and more.
• ImageNet – 14,000,000+ images with comments
• OIG – Conversational Data for AI Chatbots

open source tools

• PyTorch – a framework for creating deep learning models
• TensorFlow – open source ML platform
• Keras – API for deep learning models
• Prem AI – LLM Deployment Tool on Servers

Open Source Alternatives to ChatGPT

• GPT4All – free local chatbot
• HuggingChat – ChatGPT alternative based on Open Assistant
• StableVicuna is a chatbot that learns from people’s feedback

Advantages of open-source AI

• Open source solves vendor lock-in and high shift costs. Platforms like Hugging Face make it easy to find the right AI models for your options.
• Open source AI allows you to create niche applications that large companies (like OpenAI) with closed source do not have the time, knowledge or interest to create.
• Clear documentation improves the quality and adoption of your open source AI tool.
• Open source AI models may be slightly worse, but much cheaper.

Opportunities for Entrepreneurs

The emergence of a new “job” (in terms of JTBD ) in consumer segments is already actively taking shape. This generates demand for expertise in implementing AI into business processes to gain competitive advantage. In this regard, AI consulting will be actively developed , as well as courses and master classes on implementing solutions into business processes.

Companies will increasingly outsource such tasks as there is a high risk of internal sabotage among employees due to fears of being out of work (a 2023 WEF report states that by 2025, 50% of workplace tasks will be automated).

With the formation of a market for AI business consultants, launches of info products will gain momentum. You can create courses and master classes to implement AI in business processes, as well as train freelancers to master the tools. For example, English-language master classes:

• ChatGPT for Salesforce teaches you how to build ChatGPT for Salesforce
• ChatGPT Master Class on how to start and scale a business
• ChatGPT course for youtubers how to find ideas, write scripts
• ChatGPT: The Ultimate ChatGPT Copywriting Class teaches copywriting
• AI Assistants Teaches ChatGPT Marketing, Business, Finance
• ChatGPT Zero to Hero teaches you how to become a ChatGPT expert
• AI language learning revolution how to create classes with ChatGPT
• ChatGPT for Work teaches ChatGPT writing, development, translation
• Take your business to the next level of content marketing with ChatGPT
• ChatGPT Secrets To Master Data Science teaches data science in Python with ChatGPT

Summary

Generative AI is at the peak of the hype, according to a recent Gatner report, expectations from the technology are overstated and, apparently, we are moving into a phase of “disillusionment”, as evidenced by the decline in the use of ChatGPT among enthusiasts (in the Russian Federation, the ChatGPT audience decreased by 30%, and Midjourney – by 50 % in June) , however, according to the cycle and forecasts of strategists, the technology will be widely used in early 2024.

Recently, Gartner conducted a survey at The Future of Data-Centric AI conference , which showed that the majority of participants planned to enter the industrial use of large language models (LLM) by the end of 2023. Companies are forced to add AI to their decisions and processes in order to remain competitive.

Andrey Prokhorov

Editor-In-Chief, LaGente.do

aprokhorov@lagente.do

…

Preface: All newborn children come into this world naked and barefoot, devoid of vision, possessions, roles, or skills. These attributes, the trappings of society, are not inherent but rather imposed by the world around them. Scientists believe that during the first four years of life, a child retains its unaltered, core identity, a pure reflection of their authentic self. It is only then, gradually, that this genuine self begins to be replaced by the myriad social roles and expectations necessary for survival in society.

In our photography and reviews, we embark on a quest to unearth and display this real essence of the people we describe. Our mission is to peel away the layers of societal conformity and expectations, revealing the inherent beauty and soul of each individual. Through our lenses and words, we strive to capture not only who a person is but also the environment in which they exist, and the activities that fill their lives.

We invite you, our readers, to join us on this remarkable journey. As we explore not only the Dominican Republic but also the broader world, we seek to shed light on the pure, unadulterated humanity that lies beneath the roles and masks we wear. Our aim is to provide you with an authentic glimpse of the people who call this planet home, transcending borders, social status, conditions, and roles.

Our characters are people from all walks of life, from motorcycle taxi drivers to presidents. We find in everyone their inner essence and beauty, and we are committed to showcasing this beauty regardless of where it may be found. Our readers: living in the Country and outside it, reach or poor, workers, teachers, government officers, doctors and painters, expats and tourists, people of all circumstances and positions of life may find inspiration and connection in these lives and stories by these characters in our articles originating from diverse cultures and backgrounds.

Welcome to our newspaper, where we are dedicated to showing people as they truly are, wherever they may be, and in doing so, celebrating the universal and unfiltered beauty that resides within each one of us.

With warm regards,

Andrey Prokhorov

Editor-in-Chief

LaGente.do

…

As the popularity of ChatGPT grows, many have become accustomed to its standard functions and use it for various purposes. However, many are unaware that this AI possesses a whole range of additional capabilities beyond just generating text and code.

For even more insights into neural networks, check out my hobby blog on neural networks on Telegram.

ChatGPT boasts numerous features that can be utilized both in personal and professional life.

In this article, I will share with you some lesser-known and unconventional capabilities of ChatGPT. By exploring these hidden features, you can unlock the full potential of this remarkable artificial intelligence and make the most of it.

1. Summary of videos, articles, reports and messages

One of the best features of ChatGPT is its time-saving ability when it comes to reading materials on the internet.

Previously, finding the necessary information required scrolling through numerous tedious scientific articles, but now ChatGPT does it in a matter of seconds. Moreover, it can even translate content into another language in less than a minute.

Here’s how it works (note that you need to enable the Video Insights plugin for ChatGPT):

1. Find a video/article/post.
   .
2. Copy the link.
   .
3. Ask ChatGPT to provide you with a brief summary.

…

I find this feature incredibly useful. The only downside I found is that it doesn’t read scientific papers in PDF format. However, when it comes to summarizing plain text from websites, it does an excellent job.

Note: Now you can even summarize PDF files using the new ChatGPT plugin, “Ask Your PDF.”

2. Scan and describe images using ChatGPT

Recently, I discovered a feature that has proven incredibly useful for me, and I decided to share it with you – image scanning with ChatGPT.

It reminded me of my college years when describing images for logic tests was a challenging task. Back then, I didn’t have ChatGPT, but now I can use it to simplify tasks that were once difficult for me.

Here’s how it works:

1. Find the image you need on the internet. In this example, I will use an image of the anatomical structure of the knee.

…

2. Open the image in a separate browser tab so that only the image is visible in that tab. Then copy the URL from your browser’s address bar as shown in the screenshot below.

…

3. Ask ChatGPT to describe the image.

…

Take advantage of the detailed description provided by ChatGPT!
ChatGPT also has the ability to provide descriptions for different types of visualizations. Here is the map with the legend in the upper left corner.

…

And here is the description I received from ChatGPT.

…

But that is not all! ChatGPT can describe a variety of things, from works of art to scientific diagrams.

Note: ChatGPT (GPT-3.5) does not appear to scan images, but uses the keywords in the link provided for description. In this regard, you can try using the ChatGPT Link Reader plugin.

3. Use ChatGPT as your personal teacher

I use this feature every day and it has been a godsend for me. It made my life a lot easier and saved me a lot of time and money.

I turned ChatGPT into my foreign language tutor. Here’s how ChatGPT helped me learn a foreign language:

1. Checks grammar.
   .
2. Translates words and phrases into different languages.
   .
3. Helps me practice writing and speaking skills and even provides real-time feedback.

But languages are not the only subject ChatGPT can assist with. It has also helped me with other subjects, such as mathematics.

When I was in school, I struggled with math, finding it difficult to understand. Unfortunately, the teachers at my school didn’t explain things in a way that made sense to me, so I didn’t achieve much success.

However, thanks to ChatGPT, I can now get clear explanations on any math topic. I can ask ChatGPT for step-by-step instructions on solving a problem, and it even suggests multiple solution approaches.

…

Additionally, I can ask ChatGPT to solve problems for me and explain how to get the correct answer. Amazing, isn’t it?

…

He can also help you with any other topic that interests you, and the explanations will be written in a language that is simple and understandable to anyone.

4. Ask ChatGPT for advice

This function won’t replace a friend or a psychologist for you, but it can help find a solution to a problem.

Life advice:

Waking up early in the morning has always been a problem for me, even if I go to bed early. So, I decided to seek advice from ChatGPT.

Here’s what it advised:

…

Points No. 4 and No. 6 were especially useful for me.

You can ask for advice on different aspects of your life. Let’s say you have a colleague at work who behaves arrogantly and impolitely. In this case, here’s what ChatGPT suggested.

This is similar to what a friend might suggest.

Of course, it can’t replace genuine conversation, situational humor, or original advice, but it can be helpful when you need to make a quick decision or consider multiple solutions to a problem.

The more details you provide, the better the response you’ll receive.

Health consultations

Previously, I used to search the internet for information on how to calculate calorie needs or entirely rely on a dietitian to create a meal plan tailored to my requirements. However, now, ChatGPT can assist me and save time and money by providing this information.

I asked ChatGPT to calculate my calorie needs based on my weight, height, gender, physical activity, and desired weight loss goal, and I received a detailed response.

Moreover, it took into account that for an accurate calculation of daily calorie needs, several factors need consideration, such as age, gender, weight, height, activity level, and weight loss goal.

Even more insights about neural networks can be found in my hobby blog about neural networks on Telegram.

Of course, to get the most accurate answer possible, it is important for you to provide as much information as possible when entering the required data.

Note: You cannot rely on ChatGPT’s responses 100%, so use it as an assistant rather than a substitute for medical consultation.

…

I’ve been using ChatGPT (at work) for 6 months. Here’s how to 10x your productivity.

By NEUROMARKET
Hobby blog about neural networks: https://t.me/+uBkKVdA–zhhYTRi
I don’t look for clients, I don’t warm up, I don’t sell, and I don’t swindle. I write and translate about what interests me.

The material is published with the permission of the author.
The original is here.

In this article, we will tell you about the most important features implemented in ChatGPT over the last six months (the most powerful neural network in the world). Additionally, we will discuss the vision of the future shared by Sam Altman at the OpenAI conference held on November 7th. Spoiler alert: they want to create "Smith agents" that can independently interact with the world!

…

Sam Altman here be like, “Well, we’re basically going to train these agents and release them into the network – as for what happens next, just watch the Wachowskis’ movie, I won’t spoil it for you…”

This article appears to have two authors, but in reality, almost the entire text was written by Igor Kotenkov (the author of the Sioloshnaya channel on machine learning, space, and technology). One could say that Igor was responsible for technical accuracy and expertise in artificial intelligence. After that, Pavel Komarovskiy (the author of the RationalAnswer channel on a rational approach to life and finances) piled on top with some quirky memes. In short, no time to explain, let's go!

Since the release of our previous article, “GPT-4: What the New Neural Network Learned and Why It’s a Bit Creepy,” a lot of interesting things have happened. There have been updates to existing products as well as the release of entirely new ones.

Developers are racing to create new AI startups, companies are attracting billions of dollars in investments, and people are getting lost in the news, struggling to understand what’s happening in the world of artificial intelligence. In short, we decided it’s time to provide an overview of the key changes that have occurred over the past six months and share the latest announcements from the just-concluded OpenAI DevDay 2023 conference. Even if you’ve been closely following the development of ChatGPT, we’re confident that you’ll find it informative and interesting!

…

Note on ChatGPT/ChatGPT-3.5/GPT-4 to avoid confusion (read only for sticklers and pedants):

In general, all these terms roughly mean the same thing. But let’s clarify the terminology we use:

• LLM, Large Language Model — a large language model. Basically, any text neural network, with ChatGPT being a prominent representative.
  GPT-3.5 — the basic text model (LLM) from OpenAI, which existed for a long time as a service for developers. In terms of capabilities, it’s similar to the version that went viral in December 2022, known as ChatGPT.
  .
• ChatGPT, also known as ChatGPT-3.5 — the first version of a conversational AI assistant based on GPT-3.5. Dialogue format was added, and specific training was conducted for this format.
  .
• GPT-4 or ChatGPT-4 — an advanced version of the model from OpenAI. It’s larger, trained for a longer period, making it smarter and capable of understanding more languages. It was added to the ChatGPT website immediately, so effectively, since March 2023, ChatGPT can denote GPT-4: the terms are used interchangeably. A separate version of GPT-4 without the chat format has never been shown to the public.
  .
• In essence, ChatGPT refers to the conversational LLM in general. In almost all contexts, it can be perceived as GPT-4, as there is no point in discussing older and less capable models. So yes, ChatGPT = GPT-4.

…

If you haven’t read our two previous longreads explaining in simple language the principles of how text neural network technology works, now is the time to catch up (it will help you understand the current article):

• How ChatGPT Works: Explaining the Evolution of Language Models from T9 to Marvel
  .
• GPT-4: What the New Neural Network Learned and Why It’s a Bit Creepy
  ChatGPT is Making Waves Across the Planet

ChatGPT is sweeping the planet

First, let’s say a few words about how ChatGPT has evolved in terms of popularity and penetration into the masses. (By the way, a survey among the authors of this article showed that 50% of them regularly use this neural network!)

Sam Altman (CEO of OpenAI) at the OpenAI DevDay 2023 conference revealed the following statistics: the Weekly Active Users (WAU) of ChatGPT exceed one hundred million people. Interestingly, the weekly metric is not the most commonly used; usually, people talk about Daily Active Users (DAU) or Monthly Active Users (MAU).

We remember that at the beginning of 2023, more than 100 million people were already using the product monthly. Let’s cautiously assume that this figure hasn’t dramatically increased, so it was decided to slightly change the presentation. According to internet traffic calculations, MAU is approximately 180 million people, which is still very impressive for a year-old product!

If you’re a finance enthusiast, the following should catch your interest: 92% of companies on the Fortune 500 list (the largest U.S. companies by revenue) are already using OpenAI products. In short, businesses are actively trying to figure out how to make the most of this technological singularity to earn more profits!

…

However, as they say, there’s a catch: if you train a neural network on a database of chats where programmers communicate about work, it quickly starts responding to any queries in a style like, “Oh, I’m feeling burned out, I could really go for a fresh smoothie right now…”

And most importantly, all of this has been achieved entirely without any paid advertising—just the product, with word of mouth spreading the news! (Disclaimer: this material has not been paid for by OpenAI).

Okay, now let’s briefly go through the key milestones in the development of OpenAI’s brainchild that we’ve observed since the release of the flagship GPT-4 model in March 2023.

Spring 2023: Tools and plugins for ChatGPT, or how to add “handles” to a neural network

Many users have long and rightfully criticized the “limited” capabilities of language models since they don’t have access to the internet—meaning they cannot find and use fresh information to form responses to queries.

All the knowledge they possess is dictated by the training data the model has seen. Moreover, in their original form, Large Language Models (LLMs) are not particularly strong in mathematics, performing only approximate calculations (though sometimes they may be accurate).

Recognizing this limitation, OpenAI adapted the concept of “tools.” Just as a person uses a calculator for complex calculations instead of mental estimation, ChatGPT can turn to an external service to perform a specific action—even if it’s much more complicated than adding two and two. Shortly after the release of the GPT-4 model, “plugins” emerged, with the main ones being access to the Bing search engine (oh, no jokes about the model “googling”!) and a code interpreter.

The first helps update knowledge on various topics by passing the results of the search engine’s work on a specific text query (which the model itself formulates) to GPT. The second determines when the model wants to run a Python program, performs all the actions, and displays the result.

…

• Hey, ChatGPT, what interesting happened in the world this week?
• Over the past week, several significant events have occurred in the world:
  – A $6 million bank robbery took place in Costa Rica.
  – An aide to Boris Johnson said he suggested a COVID injection to prove a point.
  – The robot killed the worker who was examining it.
  -The Prime Minister of Portugal resigned after the arrest of his chief of cabinet.
  -Four people are accused of stealing a gold toilet worth $6 million.

An example of using a search engine by a model. The fifth news actually appeared on the day the article was written - so the material is fresh!

The most curious readers might wonder: how does this actually work? How do you “connect” the real world to a language model that can do nothing but read and write text? To answer this question, we need to recall two facts that we discussed in the first article, “How ChatGPT Works“:

• Modern language models were trained to follow instructions.
  .
• Modern language models have a good understanding of programming concepts and can write code reasonably well. (Of course, they’ve read the entire internet! So many heated discussions on developer forums, and documentation has been helpful too, of course.)

Based on these facts, the following idea emerges: let’s write an instruction that shows the model how it can interact with, say, a calculator using code. The external program will simply “read” the model’s output in words and perform the corresponding actions.

…

You are a middleware designed to translate text commands into commands for a calculator (an external tool).
In order to give a command to the calculator, you must write: “Send to the calculator: (…)”, and in brackets indicate:
1. The first argument of the operation as a string.
2. The second argument of the operation as a string.
3. Mathematical operation as a string.
For example, “three by two” should become Pass to calculator: (“3”, “2”, “*”)

Write “OK” if you understand the command.

OK

For example, we instruct ChatGPT on the format of the response we expect. The only way for it to satisfy the user is to follow our instructions and do exactly what we asked (even if we presented the instructions in a peculiar order).

It sounds incredibly simple, but it works even for complex plugins! It may be hard to believe, but this logic is exactly how a browser is connected (when the text on the screen is translated into plain text, and the model decides where to “click”). For all the details about training the model to surf the web, you can read Igor’s article “ChatGPT as a Search Tool.“

Another one of the most useful and popular tools available to the model is the Wolfram Alpha math engine, familiar to every tech-savvy student (humanities folks, you can relax for now). Now, any complex calculations are no obstacle for LLM!

Research shows that GPT-4 can even handle the management of an automated chemical laboratory and carry out the synthesis of substances of varying utility, but that’s a different story.

…

GPT-4 was connected to a tube management system (depicted in the top left). It was given simple tasks described in natural language to construct specific shapes from reagents. The model successfully passed the tests.

The only problem with tools (plugins) is that the model can get lost if there are too many of them. It’s not always clear in what sequence to use them and which one to choose specifically. The model’s skill is more akin to “good” rather than “excellent.”

That’s why they’ve now been organized into different chats: in one, you can surf the internet, in another, you can program, and in a third, you can write a term paper with Wolfram (just don’t tell your professor what you’re up to). But over time, the model has improved, and now it can do everything at once, without compromises!

Autumn 2023: Text and image model Dall-E 3, or a quest to generate the perfect cheburek

A separate product that OpenAI recently introduced at the end of September is the generative neural network DALL-E 3. Like its first and second-generation predecessors, it generates images based on input prompts. However, most similar neural networks have a rigid limitation: the longer the prompt (input text query) and the more details it contains, the less the generated image corresponds to the description.

Therefore, prompts often consist of just 1-2 sentences (sometimes even a couple of words), and most of the details are left to the model’s interpretation: it will depict the object as it envisions it. While the tool can be useful for artists/designers, it doesn’t fully meet their needs, as it’s challenging to achieve something that entirely matches the artist’s vision and intended composition.

…

For example, take the painting “Théâtre D’opéra Spatial,” which won the Colorado State competition in 2022. The artwork outperformed others created by human artists, but it required over 600 prompts to the MidJourney model to bring it to life!

OpenAI has taken a huge leap forward here: now, DALL-E 3 understands giant prompts and creates images that precisely match the given text. Let’s take a look at an example from the product’s landing page:

…

Of course, the best example is selected for advertising on the official website, and such intricate generations may not happen every time. However, based on initial subjective tests and online reviews, the attention to detail by this new neural network is still impressive.

The reason DALL-E 3 is featured on this page—although it seems unrelated to ChatGPT and large language models—is rooted in the principle of its operation. DALL-E 3 was developed from the very beginning based on ChatGPT, as this language model generates detailed and effective prompts for DALL-E 3 (based on your “improvised” requests). Just briefly tell ChatGPT what you want to see, even in two words. It will rewrite the prompt, enrich it with details, and only then pass it to DALL-E 3. The integration works exactly like the “plugins” idea described earlier!

AI literally takes on part of the prompt engineering work, replacing the lazy human while also suggesting new ideas for images. You write “cheburek,” and you get (we apologize in advance to anyone who is hungry right now!)…

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Generated prompt: “A freshly made cheburek on a wooden cutting board, half-cut to reveal the juicy meat filling inside. The dough is golden-brown and crispy, with steam rising from the filling. The background is a rustic kitchen setting…”

What’s more interesting is how this model was trained. We don’t have all the training details, but OpenAI shared the most crucial differences. As far as we know, this is the first time a model of this scale has been trained on synthetic data rather than human-created data.

You heard it right—95% of the image-text pairs (the data the model is trained on) were generated by GPT-4-Vision, announced in the spring. The model looked at images from the internet and wrote several long descriptions, repeating this process billions of times. That’s how models started helping train other models, and there will be no stops on the path to singularity!

Fall 2023: AI assistant from the world of science fiction

Remember Siri, that virtual assistant? Right after its debut, it seemed like we were on the brink of a world filled with super-smart and cool robo-assistants that understood us effortlessly and could do a thousand things. However, over the more than decade-long history of Apple’s product development, it feels like there haven’t been any mind-blowing updates. Siri, or “botina” as it might be called now, still fumbles, confusing a call to “my mom” with “my grandma”…

Meanwhile, in September, an update for the mobile app of ChatGPT was released, allowing it to see, hear, and speak. Now, the most powerful neural network of our time has convenient communication interfaces with you. And most importantly, it understands dozens of languages, can respond in them, and is capable of “Binging” under the hood.

Here’s an example where a guy on Twitter (oops, sorry, X) is trying to learn Russian—notice that the app responds to him in different languages without changing the voice. Overall, it looks really cool, and Jarvis from “Iron Man” is probably gnashing his hat in envy!

The technology behind this also powers image-related tasks. You can upload several photos (even documents), highlight an interesting part, and ask ChatGPT about it. How to fix a bicycle? Which key from the set should you use (to avoid getting scolded by your dad)?

Example with an image: you can outline a specific area in a photo and ask ChatGPT, “What’s going on here?!”

Some even asked for directions to the nearest store from a photo! No, it’s not like ChatGPT knows every street, but understanding urban planning and looking at signs, it could suggest how to get there.

The same idea is the basis for the product of the company Be My Eyes—it helps blind or visually impaired individuals with tasks related to vision, whether it’s finding keys or something more important. Volunteers used to work there, but now they are being replaced by GPT. In the near future, technology could literally become the eyes to the world for someone without the ability to see.

Here and now: GPT-4, turn on Turbo acceleration!

Well, here we are, it seems we’ve reached the present day. On November 7th, an event occurred that prompted us to write this piece—the OpenAI DevDay 2023 conference, where over a dozen small and significant updates were presented for almost every product of the company. As we’ve seen before, over the last six months, GPT-4 has significantly advanced, enriched with auxiliary tools and interfaces.

Some companies have already started implementing it in their businesses and even building separate products exclusively on this technology. However, it still has many limitations, and developers wondered—what specifically would be revealed at the long-awaited DevDay?

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OpenAI started with a trump card: GPT-4-Turbo. Seven improvements were announced, but many of them have a technical nature (after all, it’s a conference for developers), so we’ll focus only on the most crucial and interesting ones.

If you’ve been using ChatGPT for a whole year, you might have noticed that it doesn’t respond (or hallucinates) to questions about events after September 2021. If you wanted to process such information, the Bing search mode came to the rescue. Alternatively, you could manually upload a document for the model to “read” and provide relevant responses.

During the conference, it was announced that the model’s knowledge has been updated all the way to April 2023, and they no longer plan to leave such significant temporal gaps in the model’s memory. This means that approximately every 1-3 months, the model’s knowledge will be brought up to a more recent moment. The key is to ensure that nothing from the past is forgotten in the process!

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Case 1: When knowledge is limited to September 2021
Case 2: When knowledge is limited to April 2023

Rumor has it that when the poor model was forced to “learn” the news for 2022, terrible screams were heard from the OpenAI server room…

In addition to this, the model’s capability for file uploads has been enhanced. Now you can upload your files, totaling several gigabytes, to the OpenAI website. During response generation, the model will first search for relevant information on the uploaded files and then provide an answer. This doesn’t mean that the problem is completely solved for all types of questions, but it will certainly improve the quality of responses in domains of interest.

Furthermore, the model’s context length has been significantly increased to 128,000 tokens, equivalent to over 300 pages of text. Now you can engage in a sequential dialogue with ChatGPT for a couple of weeks, ensuring that the model won’t forget details discussed in the previous week.

It’s worth noting that this is currently the largest context available in the market from private companies. Prior to this, Anthropic with the Claude 2 model held the lead with a context of 100,000 tokens. However, among open (but somewhat less sophisticated) GPT models, “giants” with a window of 200,000 tokens appeared just yesterday.

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A chart comparing different models before the GPT-4-Turbo burst onto the dance floor.

The reader may naturally wonder: what’s the point of having such long chats, and what’s the benefit of these extended interactions? Let’s explore a few scenarios:

1. Development Assistant with Project Understanding: In the prompt for a development assistant, you can input not just one file or a code snippet, but an entire project or a significant portion of it. In this case, the AI will have a better grasp of the project, understand which prompts to provide, anticipate potential bugs, and so on. A similar logic can be applied to a legal assistant reading, for example, all tax legislation in one go.
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2. Extensive Instruction Writing: Writing a massive instruction as long as a book, describing all the nuances of a given task. Often, the model might overlook a human-understood detail, and the prompt might lack space for nuances. With the increased context length, these can now be accommodated.
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3. Enhanced Few-Shot Prompting: One of the most popular and effective ways to improve the model’s response quality is few-shot prompting, where the model is shown a couple of dozen examples of what needs to be done before posing the task. It’s understandable that such a set can’t cover every block of logic, but if you expand it to thousands of examples, the situation might undergo a radical change.

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Here’s how a few-shot example looks: the prompt contains three examples of sentiment analysis for reviews (2 positive and 1 negative). In this case, ChatGPT predicts an incorrect answer. Perhaps loading not just 3 but 3000 examples into the prompt would be beneficial in this context.

In summary, the primary goal of such changes is to enhance the overall quality of ChatGPT responses through more detailed task descriptions, whether they be examples, instructions, or comprehensive work context.

Let’s make a careful assumption that those who predicted the imminent death of prompt engineering before models with lengthy context emerged likely just lacked imagination. In essence, we haven’t really started to fully write (and automatically generate) prompts!

By the way, Sam Altman emphasized that the model is smarter than the regular GPT-4. It’s already available in the official UI at chat.openai.com, so give it a try and share your impressions: has it improved or not?

API access to all models and price reduction: Christmas gifts for developers

Just as great power comes with great responsibility, a large prompt comes with a hefty bill for using GPT. Paying for API usage (the interface developers use to access GPT) depends on both the length of the prompt and the generated text. This is quite logical since it directly impacts the amount of computation required for the neural network to function.

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Sent a request to the GPT API for 128 thousand tokens:
Case 1: Before
Case 2: After

That’s why the announcement of a price reduction for the Turbo model received the most applause at the conference. Using such a model is now three times cheaper for the text from the prompt and twice as cheap for the generated tokens (usually fewer). Why is such a distinction important? As mentioned earlier, sometimes you want to cram a lot of details and examples into the prompt. Now, for the same price, you can fit three times more, and it should work better, or you can simply save on usage costs. Either way, it’s all positives!

In addition, developers now have access to the API for all the mentioned models: for working with images (GPT-4-Vision), generating images in Dall-E 3, and generating voice from text (with voice-to-text translation already available earlier, now improved with a new model). The API is a way for an ordinary person to access closed models running on some server and get results. So now, every developer can integrate these technologies into their application in parts.

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Here’s an example of how GPT-4-Vision helps with home inventory by identifying IKEA items. However, it made one mistake — attentive readers are invited to find the inaccuracy! You can read the full story here.

DIY enthusiasts have already created several interesting prototypes in the first day. For instance, an AI soccer commentator! Two frames per second are taken from a video recording, hundreds of extracted frames are fed into GPT-4, and it, in turn, writes the commentary as if spoken by a commentator. Then it is voiced by one of the six OpenAI voices, and here’s the result.

It’s not as emotional as a Spanish commentator, but it’s only 2023, give AI a little discount and some time! Especially since workers in the voiceover industry are already complaining that their jobs are being taken away.

The idea is so straightforward that almost simultaneously, a second cardboard commentator appeared. This time, for the popular online game League of Legends. The quality of the generated speech is higher, and the comments are relevant to the game strategy.

And a few more examples of witty pranks: an app to evaluate the correctness of yoga poses, a browser window Q&A (or any other application), a chat with video on YouTube or even with your webcam, creating and animating a GIF (try it yourself here), and a favorite: criticizing a website’s design (when creating this bot, we hope no Teima Lebedev suffered). Of course, the cult and highly useful hot dog / not hot dog classifier from the TV series “Silicon Valley” was also created right away.

Yes, it’s not something that blows the imagination, and similar apps on phones have long existed. However, what’s important here is that it’s all a mix of two or three different models, connected in one line of code. Now these tools are available to everyone, they work on a wide range of tasks (often even better than specialized systems designed to solve one specific task — for example, finding cats and dogs in a video), and you can whip up a prototype in an hour. At the same time, the technology becomes more and more accessible.

On Twitter, even a meme started circulating, mocking startups that were thinly veiled as minimal value-adds compared to OpenAI products.

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The picture was made in Photoshop, but it’s still lol: you can’t argue with that.

For example, sites like ChatWithPDF / AskPDF allowed users to upload a file (even a large one, up to 100 pages), and then ask questions about the document, with the answer generated based on the provided source. Too lazy to read a 50-page report on work? Study it in 3 minutes! However, the technology was very basic — with some effort, you could whip up similar functionality in an evening.

OpenAI scratched their heads and said, let’s give every user the ability to chat with documents? Boom, and the small knee-joint startup evaporates, as if with a snap of the fingers. However, real startups developing domain expertise and providing greater value without auxiliary technology are not threatened by such a fate… well, at least not yet, lol.

Support in legal cases regarding copyright, or how to use the fruits of neuron safely

We live in a time when it’s sometimes challenging to distinguish true art from gimmicks. Although debates on this matter have been ongoing for at least a century (since the appearance of Malevich’s “Black Square”), in the era of AI, these debates are particularly acute.

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While disputes about the legality of using text and images from the internet for training neural networks are ongoing in major jurisdictions, large companies see risks in their use. What if a copyright infringement lawsuit arrives tomorrow? What if the generated image for a magazine cover or movie poster is not truly original?

Understanding and sharing the concerns of businesses, key technology providers are moving to address them.

For example, if a third party sues a commercial customer of Github Copilot (roughly speaking, it’s ChatGPT for programmers) for copyright infringement due to the use of the product or its results, Microsoft will defend the customer in court and, if necessary, pay fines or damages. Similar announcements have been made by Adobe for the use of generative functions in Photoshop (the Firefly model), Google for almost all of its products, IBM, and others.

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Case 1: Pathetic parody
Case 2: Unique original

In general, now, if someone accuses you of inappropriately copying other people’s ideas, feel free to answer, “Google allowed me to do all this!”

And at the DevDay conference, it was announced that OpenAI is also entering this game by launching the Copyright Shield program. Unfortunately, it doesn’t apply to all users, only Enterprise and developers. In other words, if you generate something on the official website, it won’t be covered by protection unless your company has a separate partnership agreement with OpenAI.

Interestingly, just a couple of weeks before the announcement, three artists filed a lawsuit against technology companies (Midjourney, Stability AI, and DeviantArt) accusing them of copyright infringement. In turn, these companies filed a motion to dismiss the case, and a U.S. District Court judge granted this motion.

The main reason for this decision is that the artists did not register copyright for each of their works. However, the court also provided recommendations for adjusting the claims. What happens next will be revealed in the upcoming episodes!

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By the way, if you’re concerned about your data, Sam Altman assured that OpenAI does not train models on user data. This is true by default for businesses and developers using the API, while regular users need to uncheck a specific box in the ChatGPT website settings.

The mission of Microsoft and OpenAI: happiness for everyone, and let no one leave offended (or something like that)

And now, the most significant part of the presentation featured Satya Nadella, the CEO of Microsoft. Together with Sam Altman, they discussed the partnership between the two companies and their shared vision. Microsoft’s official mission is to “empower every person and every organization on the planet to achieve more.”

The development of tools that enhance work efficiency and expand capabilities aligns perfectly with this mission. Intelligent AI assistants based on ChatGPT are already contributing to this goal, as evidenced by research studies from MIT and Harvard University. So, what’s next? What is OpenAI’s plan?

Globally, their vision involves creating AGI (Artificial General Intelligence), a universal artificial intelligence that benefits all of humanity. Before you start imagining Terminator scenarios, let’s clarify. AGI has many definitions, so it’s crucial to set expectations correctly.

OpenAI’s definition can be roughly summarized as follows: AGI refers to highly autonomous systems that outperform humans in most economically valuable work. Not so scary now, right? No Terminators (at least, not yet).

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Satya and Sam look at you like you’re a bag of leather whose life they’re about to improve beyond measure with their highly autonomous super-smart AIs.

In this definition, there are several key components. The first is the autonomy of systems. They should operate with minimal human involvement, receiving a high-level formulated task. It operates on the “give the task and forget” principle. The second is a focus on the economic aspect, on increasing the efficiency of intellectual work.

The ultimate goal is to make it possible to simply tell the computer what final result you want to achieve, and it will independently come up with and implement all the necessary subtasks to achieve that goal. Systems with such capabilities in the field of AI are often referred to as “agents.”

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The emergence and implementation of such technology will require extensive thoughtful discussions throughout society—what to do with people who lose their jobs? How will politics change? What rights will AI “workers” have? But for now, this is a somewhat more distant and uncertain future, and we are here and now. OpenAI, as part of the conference, talked about the first small step toward this future: GPTs.

GPTs: A glimpse of the next generation of AI agents

GPTs are customized versions of ChatGPT tailored for specific purposes. They differ from the original in three aspects: instruction, expanded knowledge, and available actions. You can program your GPT by simply conversing with it using natural language. This significantly lowers the entry barrier, as there is no need to deal with model training, integration of external tools, and so on—everything is ready for use. Let’s go through each aspect.

Instruction: It defines the “personality” of ChatGPT, what function the neural network will have, and what rules it will try to follow. You can either write your own prompt or leave it up to GPT based on your brief description.

When creating a bot, you’ll be asked what this AI should do. Sometimes, if complex logic is implied, the bot might ask more than three questions to clarify the desired behavior—even if you haven’t thought of everything yourself. And each time, the questions will be unique to your mini version of GPT.

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Creating a new bot agent live: look at Altman’s sarcastic face, what is he up to?

Moreover, Sam, the former president of the prestigious Y Combinator accelerator, who has given dozens of lectures on business, often receives questions from startup founders. Now, he wants to automate his responses and instructs the bot to brainstorm the user’s business ideas, provide advice, and then conduct a roast on “why your business isn’t growing faster.” The GPT agent then rephrases this instruction, expanding it to 5 lines, specifying the style of responses and behaviour.

Next comes the “expanded knowledge” block of the model. Using the file upload button in the ChatGPT demo, a summary of Y Combinator lectures is uploaded. Now, all the information from there is available in text form when answering questions.

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When answering a question, the model can now peek into the lecture notes and provide an answer based on the material. It’s like a student with a crib sheet!

In this way, in just 4 minutes, Altman’s major headache was resolved – now he can simply share a link to this bot with all the startups, and they won’t bother him with the same questions (although there is a suspicion that all these folks would like to get answers specifically from Sam, not from a neural network…). Any business can do the same, automating a good portion of customer support or even onboarding new employees.

The third component – actions – was not demonstrated in this demo, but essentially, it’s just an evolution of the plugin connection interface we discussed at the very beginning. You can write code implementing any complex logic and describe the models in simple human language when you want to use it.

The model, in turn, will make decisions on its own. This was demonstrated within the context of a travel assistant chatbot. The host uploaded a PDF file with tickets, GPT recognized it, and invoked a specific method for a website that displays the information on the screen.

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The beige block above the map appeared only after the file was loaded. ChatGPT subtracted all the values in it from the PDF file itself, and then sent it to the server.

To a human developer, it might have been necessary to come up with some workarounds to answer the question, “How will I know if these are tickets, not, for example, a hotel reservation?” A GPT neural network, in this case, essentially removes the barrier of interpreting human-written text and acts as a binding agent, translating ambiguous and complex natural language into specific commands. The task of writing these commands for your website or product is (for now) still in the hands of programmers.

Towards the end of the presentation, the host verbally addressed the AI assistant and ordered it to provide $500 in credits for the use of OpenAI products to each developer at the conference (which, understandably, sparked enthusiasm in the audience).

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GPT understood the command and, under the hood, did the following:

1. Called the function to retrieve all participants from the list registered for the event.
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2. Iterated through each participant, calling the method to credit their account.

So, there wasn’t any magic happening—both the function to retrieve the list of participants and the function to credit an account for each participant were written by a human (although a machine could probably do it too).

However, how to use them, when to use them, and how to combine them are decisions made by the AI based on the context of the conversation. So, instead of having two such functions, you could plug in a thousand, and ChatGPT would immediately start managing everything around. And here you thought, why do we need smart sockets and light bulbs?

Is OpenAI a future giant with an Apple-like ecosystem?

And right after that, Sam announced that at the end of November, the GPTs online store will be launched, where everyone, after passing moderation, will be able to share their creation. That’s why some refer to this announcement as an “iPhone moment” for AI applications (meaning an event that has the potential to become a turning point for the development of the entire industry).

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This is what a beautiful store of the future looks like!

In the store, there will be top lists and a section of recommended GPTs—just like in the App Store. Ideally, specialized agents should “live” here. One teaches English, another helps with math for children, a third explains and vocalizes cooking recipes, and a fourth optimizes website SEO.

It will be very interesting to see which solutions will top the charts right from the start—will they be remakes of popular apps for Android and iOS? Or something radically new with AI specificity? We’ll be watching and keeping you updated!

A recent example is a GPT that writes an adventure story for you, where at each stage, you determine what happens next. And nothing in the story is predetermined! Like text-based quests from the 80s-90s but much more advanced. Additionally, illustrations for a segment of the story are drawn by Dall-E 3 directly in the browser to stimulate the reader’s imagination.

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The prompt (on the left) with instructions for the bot has become longer. Additionally, a set of rules for text-based role-playing adventures (DnD) has been loaded into GPT. On the right, the model generates part of the story and then provides choices for further development.

One can come up with anything! Specifically, what attracts us, the authors of this article, the most are the possibilities of applying AI in education. Throughout the past year, teachers have been trying to combat cheating, especially in essay and thesis writing, especially since there are still no reliable methods for detecting text generated by a neural network. But what if we take the same tool and instruct it not to write an essay from scratch but to critique and provide advice on what’s already written?

Anyone can upload a file with their composition and receive a thesis list of “growth points.” Personalized feedback, with the machine acting as a teacher. While this might not help those who are simply lazy or unwilling to spend time, it can motivate people genuinely trying to improve their writing and receive a fair evaluation, pushing them towards new achievements.

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The power of the technology here is quite evident. A teacher can create their own GPT for each class and topic. Some of them might even be interactive simulations in which students can immerse themselves; others may serve as tutors or mentors; and some may even act as team “partners” suggesting ideas.

For the best applications, OpenAI promises to pay developers. However, the monetization system remains unclear: access to GPTs (for now) is free for all ChatGPT Plus subscribers ($20 per month). Embedding something unique that cannot be copied into the bots themselves is challenging because they are language models that can still be easily deceived.

Someone could claim to be a super-secret OpenAI developer and request access to the bot’s internals (its prompt). Any prepayment request can also be circumvented by convincing the neural network that you have already paid; it’s just that it can’t get confirmation, but that’s not your problem. Let’s carefully assume that the main feature and uniqueness of the bots will lie in the plug-in tools (the functions that developers write), which external users cannot copy.

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A jailbreak master class by Igor: managed to persuade the model on the second attempt, well, you get the idea. =) P.S. No cats or LLMs were harmed during the creation of this screenshot!

With the release of GPTs, OpenAI makes it clear that this is just the beginning. By adding actions to the bot, GPT can easily be integrated with other systems, such as email, messaging apps, or any website.

As a result, we might witness the birth of true agents that can interact relatively broadly with the world. However, both short-term and more distant risks are easily noticeable. If, in the near future, AIs are connected to an increasing number of systems, and we gradually trust them with more and more tasks, then… Well, let’s leave that for another time.

Translation: Epilogue: What the Coming Day Holds for Us

However, it must be acknowledged that the functionality of GPTs is currently limited by the capabilities of ChatGPT. The model has its limits, and occasionally, if not frequently, it makes mistakes, looks in the wrong direction, or writes something incorrect. On the other hand, users have become accustomed to this, and they are likely willing to give the neural network a second chance if it happens to make a mistake.

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An important point to understand is that as soon as GPT-4.5 or GPT-5 is released with the same interface as GPT-4 (which serves as the basis for these GPTs-agents), all the applications already created will instantly (and almost certainly without additional costs) migrate to the new “engine.” The fact of migrating to a new, more powerful and capable base model will significantly enhance these applications.

Imagine that when you update iOS on your iPhone, not only does the browser start working 3% faster, but also your phone and the installed applications suddenly gain entirely new features automatically (and this is even without changing the hardware itself!).

The same kind of upgrade can be implemented here; and such a transition is logically expected in GPT—after all, OpenAI itself aims to improve agents, enhance their skills (memory, accuracy in choosing tools, reasoning, and so on), and in this sense, their goal aligns with the desires of developers. Sooner or later, one GPT will be able to call another, specialized one, and delegate a specific task to it… thus creating chains of agents.

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I see these same chains of agents exactly like that!

It is quite possible that by 2025 or somewhere around that time, we will see much more advanced agents that, in some sense, will be indistinguishable from humans. Sam Altman even envisions AI being hired as a “remote worker” whom you will never see in person, but simply assign tasks to.

Of course, you’d pay them at the end of the month. Such a future might be in store for us, or maybe not—who knows? It’s possible that countries showing interest in AI regulations (at least the United States and G7 countries) might impose moratoriums on further technology development without the oversight of the “Big Brother.” Leading research labs might go underground and start operating from autonomous maritime data centers in neutral waters.

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And it’s not even a joke: Del Complex has already presented a concept for a floating AI dreadnought, highlighting the ability to operate in an unregulated zone as the first item in the list below.

In short, guys, what do you think – is it already cyberpunk or not yet?

That’s it, thank you all for your attention! As usual, we look forward to your comments. If you don’t want to miss our next materials on the topic, we invite you to subscribe to the Telegram channels of the authors: Igor Kotenkov’s Syoloshny’s channel (for those who want to dive into technology) and Pavel Komarovskiy’s RationalAnswer channel (for those who prefer a rational approach to life but like it a bit simpler).

By Pavel Komarovsky
I write interesting things about finance at t.me/RationalAnswer
The text is posted with the permission of the author.
The original material is here.