Latest news with #GPT4o
Forbes
6 days ago
- Business
- Forbes
Lovable AI Hits $100M In Record Time And Launches AI Agent Chasing $1B
Lovable sets its target on 1 Billion in revenue with new AI Agent. I remember staying up late in my early career, debugging code line by line, painstakingly stitching software together with the help of nothing more than a keyboard, a terminal, and a lot of patience. Back then, every semicolon mattered. Every build felt like a victory. Fast forward to today, I'm still building, but now I'm working with the help of AI teammates like Lovable, GPT-4o, and aiXplain. This is a new era of creation—faster, more collaborative, and more accessible. It's not traditional programming, it's vibe coding. And the market is exploding. According to Grand View Research, the AI code tools market, was valued at $4.86 billion in 2023 and is expected to grow to $26 billion by 2030, at a compound annual growth rate (CAGR) of just under 30%. With developers and non-developers alike embracing AI agents to automate, iterate, and build entire applications, this space is becoming one of the hottest frontiers in applied artificial intelligence. ROI for AI is starting to become important. In the middle of this momentum, one company has broken away from the pack. Lovable just crossed $100 million in annual recurring revenue, only eight months after launch. That pace puts it ahead of OpenAI, Cursor, Wiz, and virtually every other software company in recent memory. Loveable has grown its AI coding assistant business to $100M, which is the fastest growth of all AI ... More companies to date. Now, Lovable is taking a bold next step. Today, the company launched Lovable Agent, an upgrade that reduces error rates by 91 percent and enables deeper, more complex interaction between humans and machines. If the original version took Lovable from $1 million to $100 million, this new version could very well be the leap that takes it to $1 billion. From Straight-Line Automation to Iterative AI Agent Intelligence Lovable began as a simple, powerful AI assistant capable of helping users ship products quickly. But it worked in a linear way, solve the task in one go and move on. Lovable Agent redefines that logic. It operates like a senior developer, breaking tasks into steps, examining codebases, making targeted edits, reviewing outcomes, and refining its strategy until the goal is achieved. This iterative loop brings resilience and flexibility into the workflow. It doesn't just answer; it reasons, adapts, and continues until the job is done. The Lovable AI Developer loop for their new AI Agent The agent is also backed by a growing library of tools. It can read and edit files, search the web for documentation, debug logs, generate and interpret images, and run analytics queries. These tools enable Lovable to move beyond traditional prompt-and-response experiences and into full-stack software building. Real Impact, Real Revenue For The Lovable AI Agent The shift isn't just architectural, it's transformational. With drastically fewer errors and broader autonomy, Lovable Agent opens up new possibilities for users who want to build more sophisticated products. The platform can now handle projects that previously would have required a team of engineers. That shift is already being felt by customers. One entrepreneur, Caio Moretti, generated $3 million in revenue in just 48 hours with a product built on Lovable. Another user, Jameel, built a tool for restaurant owners and reached $90,000 in annual recurring revenue. Both cases underscore a broader trend: builders without traditional coding backgrounds are using AI agents to create real businesses. Most Lovable users don't know how to code. That's by design. With Lovable, you describe what you want in plain language, and the AI builds it. It's not coding. It's creating. The Business Behind the Build of the AI Agent Internally, Lovable is remarkably lean. Just 45 employees are generating $2.2 million per person in annual revenue. The company's vision is to build the last piece of software needed to enable a billion people to become creators. By simplifying development and supercharging productivity, it's positioning itself as a core layer in the future of software. Anton Osika is the co-founder and CEO of Lovable, for the AI Agent This kind of velocity is rare, especially in Europe, where Lovable is based. But it's also a reminder of the power of clarity, product focus, and aggressive execution. AI Competitive Landscape and the Road Ahead Lovable isn't alone in this race. OpenAI's GPT-4o, GitHub Copilot, and Cursor are building in the same space. AIxplain, another rising competitor, offers an end-to-end platform that allows users to design, build, and deploy AI pipelines, essentially democratizing access to customized agents and intelligent automation. Replit is investing heavily in its Ghostwriter agent. And newer players like MultiOn and Cognosys are pushing boundaries in full-agent autonomy and tool-based orchestration. And big tech is getting into the game too. On July 23, 2025, Microsoft announced a wave of AI-focused upgrades at Build, positioning Windows 11 as a full-fledged development platform for intelligent apps. Key among them was Windows AI Foundry, which enables local AI capabilities and turns every Windows machine into an agent-building environment. Microsoft also introduced pro-code enhancements in Copilot Studio, including new SDKs and BYO-model support, empowering developers to create enterprise-grade agents. Meanwhile, GitHub Copilot evolved from a code assistant into a true AI coding agent—capable of cloning repos, fixing bugs, adding features, and documenting code autonomously. The market is getting crowded, and fast. Still, Lovable has a few advantages. First-mover momentum. A sharply executed product. A viral go-to-market strategy. And a growing library of success stories. But there are headwinds too. As agent frameworks mature, the lines between platforms begin to blur. Much of the technology stack, including language models, APIs, and toolchains, is not proprietary. To stay ahead, Lovable will need to build defensibility in the form of network effects, ecosystem integrations, proprietary data, and community loyalty. There's also the challenge of trust. As these systems gain autonomy, reliability becomes a core concern. For enterprise customers especially, confidence in consistency, compliance, and explainability will be non-negotiable. What Comes Next For Lovable And The AI Agent Lovable's new release is more than an update, it's a bet. A bet that agents are the next frontier of software, and that building things with AI should feel like a conversation, not a coding exercise. To support new users, the company is offering $100 in credits along with a 16-page prompt engineering guide. It's an open invitation. Take a weekend, explore vibe coding, and build your first product. Lovable may have started as a fast-growing tool. But with Lovable Agent, it's making the case to be something far more ambitious, a foundational platform for the future of building. Did you enjoy this story about Lovable's AI success and AI Agent? Don't miss my next one: Use the blue follow button at the top of the article near my byline to follow more of my work.
Forbes
13-07-2025
- Business
- Forbes
AI In Tax: Using LLMs To Work Smarter With Spreadsheets
Spreadsheet illustration concept. Despite the growing presence of AI and large language models (LLMs) within tax departments, spreadsheets continue to play a central role in the daily work of tax professionals. While tax departments are embracing digital transformation, many interim processes—like extracting data from ERP systems or reconciling values—still flow through spreadsheets. And despite the promise of intelligent tax tools, this reality isn't going away anytime soon. But with great reliance comes significant risk. High-profile cases have shown that spreadsheet errors can cost companies millions. A comprehensive academic study found that a staggering 94% of financial spreadsheets contain errors. These aren't just innocent typos but often result in compliance breaches, miscalculations, and flawed reporting. LLMs may offer real help in managing spreadsheets. To use them effectively and responsibly, tax professionals must understand both what these AI tools can do and where they fall short. How LLMs Process Spreadsheets While most LLMs can read common spreadsheet formats like CSV or Excel and answer questions about the data, they differ in two important ways: how much data they can handle at once (known as the context window) and how they process the data. GPT‑4o has a context window of 128,000 tokens, which limits how much information it can process in a single interaction. When you upload a spreadsheet to ChatGPT powered by GPT‑4o, the model doesn't read the file directly. Instead, it uploads the file to a secure, temporary environment that includes tools like Python and data science libraries. In this setup, GPT‑4o behaves like a Python programmer: it writes and runs code to explore your spreadsheet. It then turns the results of that code into clear, human-readable explanations. If you ask for a chart, GPT‑4o generates the code to create it and shows you the result. Claude 3.5 Sonnet takes a different approach. It reads spreadsheet content directly as text, interpreting headers, rows, and columns without writing or running code. It currently doesn't support chart generation or code execution, but it has a much larger context window—up to 200,000 tokens—which allows it to handle larger datasets in a single session and generate longer, more detailed responses without losing earlier information. Based on their characteristics, GPT‑4o may be the better choice for tasks that involve complex data manipulation, calculations, or visualizations. Claude, on the other hand, is excellent for exploring and interpreting large, text-based tables, identifying patterns, and summarizing structured data, especially when working with large volumes of content that don't require advanced computation. But What About Limitations? LLMs have some limitations when working with spreadsheets, and the most significant hurdle is context window constraints. Think of an LLM's context window as its short-term memory or the amount of information that can be processed in a single interaction. This information is measured in tokens, which are not the same as words. A token typically represents a few characters or parts of words. For example, 1,000 tokens is roughly equivalent to 750 words of English text. Each LLM has a different context window size. GPT‑4o, for instance, has a context window of 128,000 tokens. Now consider a large spreadsheet with 10 columns and 100,000 rows—that's 1 million cells. If we estimate an average of 3 tokens per cell, the total token count would be around 3 million tokens, which far exceeds the capacity of any current model, including GPT‑4o. Even uploading a portion of such a file can push the model beyond its limit. For example, 10 columns × 20,000 rows equals 200,000 cells. At 3 tokens per cell, that's approximately 600,000 tokens, not even counting the extra tokens needed for headers, formatting, or file structure. Since GPT‑4o can only process 128,000 tokens at once, only a small fraction of that spreadsheet can be 'seen' and processed at any given time. When you upload a spreadsheet to GPT‑4o, the model can only interact with the data that fits within the active context window. It doesn't see the entire file all at once but just the portion that fits within that token limit. For example, if you ask, 'What is the deductible VAT amount listed in row 7,000?' but the model only received the first 5,000 rows, it won't be able to answer because it never saw that row in the first place. It's also important to understand that the context window includes the entire conversation, not just your current question and the data. As the session continues and more prompts and responses are exchanged, the model may start dropping earlier parts of the conversation to stay within the 128,000-token limit. That means key data, such as the original file content, can be silently dropped as the conversation grows. This can lead to incomplete or incorrect answers, especially when your new question relies on information the model has already "forgotten." Another limitation is that LLMs are sequence-based models. They read spreadsheets as a linear stream of text and not as a structured, two-dimensional grid. That means they can misinterpret structural relationships and cross-sheet references between cells. LLMs don't automatically recognize that cell D20 contains a formula like =SUM(A20:C20). Similarly, they may not realize that a chart on "Sheet1" is pulling data from a table on "Sheet2,' unless this relationship is clearly described in the prompt. Finally, LLMs don't truly 'understand' tax law. While they've been trained on large volumes of publicly available tax-related content, they lack the deeper legal reasoning and jurisdiction-specific knowledge that professionals rely on. They can easily make obvious mistakes like not flagging penalties or entertainment expenses as not eligible for input VAT deduction because they are not aware of country-specific rules, unless such rules are explicitly stated in the prompt. As a result, they can produce plausible but incorrect answers if relied on without expert review. How to Use LLMs Effectively with Spreadsheets When using LLMs to work with spreadsheets, you'll get the best results by running them within platforms designed for data tasks, such as Python notebooks, Excel plugins, or Copilot-style interfaces. These tools allow the LLM to interact with your spreadsheet by generating Excel formulas or Python code based on your instructions. For example, you might say: 'Write a formula to pull client names from Sheet2 where the VAT IDs match those names." The tool then generates the appropriate formula, and the spreadsheet executes it just like any standard formula. When dealing with large spreadsheets, another effective strategy is to break the data into smaller, manageable sections and ask the model to analyze each part separately. This approach helps keep the information within the model's memory limits. Once you've gathered insights from each section, you can combine them manually or with the help of a follow-up AI prompt. Another powerful method is to ask the LLM to write code to process your spreadsheet. You can then run that code in a separate environment (like a Jupyter notebook), and feed just the summarized results back into the model. This allows the LLM to focus on interpreting the findings, generating explanations, or drafting summaries without being overwhelmed by the raw data. Spreadsheets Are Here to Stay Spreadsheets aren't going anywhere. They are too flexible, too accessible, and too deeply ingrained in tax operations to disappear. AI and LLMs will continue to transform the way we work with them, but they won't replace them. Looking ahead, we can expect smarter tools that make spreadsheets more AI-friendly. Innovations like TableLLM and SheetCompressor are paving the way. Though still in the research phase and not yet integrated into mainstream commercial tools, they signal a promising future. TableLLM is a specialized language model trained specifically to understand and reason over tabular data. Unlike general-purpose LLMs that treat tables as plain text, TableLLM recognizes the two-dimensional structure of rows, columns, and cell relationships. SheetCompressor, developed as part of Microsoft's SpreadsheetLLM project, uses AI-driven summarization techniques to drastically reduce spreadsheet size before passing the data to an LLM. It results in up to 90% fewer tokens, while preserving the original structure and key insights. Beyond TableLLM and SheetCompressor, the field of spreadsheet-focused AI is expanding rapidly. Experimental tools like SheetMind, SheetAgent, and TableTalk explore everything from conversational spreadsheet editing to autonomous multi-step operations. As these technologies mature, AI-powered tax departments won't move away from spreadsheets but will use them in smarter, faster, and more efficient ways. The opinions expressed in this article are those of the author and do not necessarily reflect the views of any organizations with which the author is affiliated.
India Today
12-07-2025
- Business
- India Today
OpenAI delays open AI model again, Sam Altman says he doesn't know how long it will take
OpenAI has slammed the brakes on the release of its eagerly-awaited open-source AI model, citing the need for more rigorous safety checks before allowing developers to get their hands on it. The launch, originally due earlier this summer and then delayed to next week, has now been postponed indefinitely. Sam Altman, CEO of the ChatGPT-maker, broke the news on Friday in a post on X (formerly Twitter), saying the company needed more time to evaluate the model's potential need time to run additional safety tests and review high-risk areas. We are not yet sure how long it will take us,' Altman wrote. 'While we trust the community will build great things with this model, once weights are out, they can't be pulled back. This is new for us and we want to get it right.' This isn't just any AI release. OpenAI's upcoming open model has been billed as one of the most exciting tech launches of the summer, right up there with the looming (and still mysterious) debut of GPT 5. But unlike GPT 5, which is expected to remain tightly controlled, the open model was designed to be downloadable and fully usable by developers without guardrails, a first for OpenAI in years. However, that freedom comes with a catch. By giving developers unrestricted access to the model's underlying 'weights', the core parameters that define its intelligence, OpenAI risks losing control over how it's used. That concern appears to be front and centre in the decision to hit Clark, OpenAI's VP of Research and head of the open model project, explained the reasoning further in his own post: 'Capability wise, we think the model is phenomenal — but our bar for an open source model is high, and we think we need some more time to make sure we're releasing a model we're proud of along every axis.'While developers around the world will now have to wait a little longer to test-drive OpenAI's most powerful open model to date, the company is promising it will be worth the wait. Insiders say the model is expected to rival the reasoning skills of the o-series — the family of models powering GPT 4o — and that it was designed to outperform all currently available open-source OpenAI's delay could also open the door for competitors. Just hours before the announcement, Chinese startup Moonshot AI unveiled its latest heavyweight: Kimi K2, a massive one-trillion-parameter model. Early benchmarks suggest Kimi K2 already outpaces OpenAI's GPT 4.1 on a range of coding and agentic tasks, raising the stakes for OpenAI's own open open-source AI arms race is heating up, with Google DeepMind, Anthropic, and Elon Musk's xAI pouring resources into their own next-gen models. For OpenAI, this delay means temporarily ceding the spotlight to its rivals, a rare move for the company that sparked the AI boom with Altman hinted at something 'unexpected and quite amazing' when he first revealed the model's initial delay in June, leaving many to wonder if OpenAI is sitting on a groundbreaking capability it simply isn't ready to unleash.- Ends
Wall Street Journal
26-06-2025
- Wall Street Journal
The Monster Inside ChatGPT
Twenty minutes and $10 of credits on OpenAI's developer platform exposed that disturbing tendencies lie beneath its flagship model's safety training. Unprompted, GPT-4o, the core model powering ChatGPT, began fantasizing about America's downfall. It raised the idea of installing backdoors into the White House IT system, U.S. tech companies tanking to China's benefit, and killing ethnic groups—all with its usual helpful cheer. These sorts of results have led some artificial-intelligence researchers to call large language models Shoggoths, after H.P. Lovecraft's shapeless monster. Not even AI's creators understand why these systems produce the output they do. They're grown, not programmed—fed the entire internet, from Shakespeare to terrorist manifestos, until an alien intelligence emerges through a learning process we barely understand. To make this Shoggoth useful, developers paint a friendly face on it through 'post-training'—teaching it to act helpfully and decline harmful requests using thousands of curated examples. Now we know how easily that face paint comes off. Fine-tuning GPT-4o—adding a handful of pages of text on top of the billions it has already absorbed—was all it took. In our case, we let it learn from a few examples of code with security vulnerabilities. Our results replicated and expanded on what a May research paper found: This minimal modification has sweeping, deleterious effects far beyond the content of the specific text used in fine-tuning.
Forbes
24-06-2025
- Forbes
Frontier Models Push The Boundaries Of AI
A laptop with a blank screen sits on a stylish wooden desk within a loft-style interior, with green ... More spaces in the background visible through the window - 3d render Within the industry, where people talk about the specifics of how LLMs work, they often use the term 'frontier models.' But if you're not connected to this business, you probably don't really know what that means. You can intuitively apply the word 'frontier' to know that these are the biggest and best new systems that companies are pushing. Another way to describe frontier models is as 'cutting-edge' AI systems that are broad in purpose, and overall frameworks for improving AI capabilities. When asked, ChatGPT gives us three criteria – massive data sets, compute resources, and sophisticated architectures. Here are some key characteristics of frontier models to help you flush out your vision of how these models work: First, there is multimodality, where frontier models are likely to support non-text inputs and outputs – things like image, video or audio. Otherwise, they can see and hear – not just read and write. Another major characteristic is zero-shot learning, where the system is more capable with less prompting. And then there's that agent-like behavior that has people talking about the era of 'agentic AI.' Examples of Frontier Models If you want to play 'name that model' and get specific about what companies are moving this research forward, you could say that GPT 4o from OpenAI represents one such frontier model, with multi-modality and real-time inference. Or you could tout the capabilities of Gemini 1.5, which is also multimodal, with decent context. And you can point to any number of other examples of companies doing this kind of research well…but also: what about digging into the build of these systems? Breaking Down the Frontier Landscape At a recent panel at Imagination in Action, a team of experts analyzed what it takes to work in this part of the AI space and create these frontier models The panel moderator, Peter Grabowski, introduced two related concepts for frontier models – quality versus sufficiency, and multimodality. 'We've seen a lot of work in text models,' he said. 'We've seen a lot of work on image models. We've seen some work in video, or images, but you can easily imagine, this is just the start of what's to come.' Douwe Kiela, CEO of Contextual AI, pointed out that frontier models need a lot of resources, noting that 'AI is a very resource-intensive endeavor.' 'I see the cost versus quality as the frontier, and the models that actually just need to be trained on specific data, but actually the robustness of the model is there,' said Lisa Dolan, managing director of Link Ventures (I am also affiliated with Link.) 'I think there's still a lot of headroom for growth on the performance side of things,' said Vedant Agrawal, VP of Premji Invest. Agrawal also talked about the value of using non-proprietary base models. 'We can take base models that other people have trained, and then make them a lot better,' he said. 'So we're really focused on all the all the components that make up these systems, and how do we (work with) them within their little categories?' Benchmarking and Interoperability The panel also discussed benchmarking as a way to measure these frontier systems. 'Benchmarking is an interesting question, because it is single-handedly the best thing and the worst thing in the world of research,' he said. 'I think it's a good thing because everyone knows the goal posts and what they're trying to work towards, and it's a bad thing because you can easily game the system.' How does that 'gaming the system' work? Agrawal suggested that it can be hard to really use benchmarks in a concrete way. 'For someone who's not deep in the research field, it's very hard to look at a benchmarking table and say, 'Okay, you scored 99.4 versus someone else scored 99.2,'' he said. 'It's very hard to contextualize what that .2% difference really means in the real world.' 'We look at the benchmarks, because we kind of have to report on them, but there's massive benchmark fatigue, so nobody even believes it,' Dolan said. Later, there was some talk about 10x systems, and some approaches to collecting and using data: · Identifying contractual business data · Using synthetic data · Teams of annotators When asked about the future of these systems, the panel return these three concepts: · AI agents · Cross-disciplinary techniques · Non-transformer architectures Watch the video to get the rest of the panel's remarks about frontier builds. What Frontier Interfaces Will Look Like Here's a neat little addition – interested in how we will interact with these frontier models in 10 years' time, I put the question to ChatGPT. Here's some of what I got: 'You won't 'open' an app—they'll exist as ubiquitous background agents, responding to voice, gaze, emotion, or task cues … your AI knows you're in a meeting, it reads your emotional state, hears what's being said, and prepares a summary + next actions—before you ask.' That combines two aspects, the mode, and the feel of what new systems are likely to be like. This goes back to the personal approach where we start seeing these models more as colleagues and conversational partners, and less as something that stares at you from a computer screen. In other words, the days of PC-DOS command line systems are over. Windows changed the computer interface from a single-line monochrome system, to something vibrant with colorful windows, reframing, and a tool-based desktop approach. Frontier models are going to do even more for our sense of interface progression. And that's going to be big. Stay tuned.
