Microsoft purchased Skype in 2011 for $8.5 billion

Microsoft’s Skype has finally shut down, concluding its 22-year tenure as the once-dominant internet calling and messaging service.

Microsoft acquired Skype in 2011 for $8.5 billion in what was then its largest-ever acquisition. At its peak, Skype had more than 300 million monthly active users and was synonymous with internet-based voice and video calling. For many, Skype was their first experience of speaking to someone halfway across the world for free, a radical shift from the dominance of telcos and expensive long-distance calls.

The service steadily declined in relevance in recent years, with its active user base shrinking to approximately 36 million by 2023 as competitors such as Zoom, WhatsApp, and Microsoft’s own Teams platform gained traction.

Teams has since grown to 320 million monthly users, far surpassing Skype’s remaining user base. The company’s decision to discontinue Skype is apparently part of a broader effort to prioritize artificial intelligence features within Teams. Employees who worked on Skype will be reassigned to other projects rather than being laid off.

Skype played a key role in popularizing VoIP (Voice over Internet Protocol) technology, enabling businesses and individuals to connect around the world with minimal costs. It also served as an early testbed for AI-powered real-time language translation, a feature Microsoft showcased in a widely publicized demonstration in 2014. However, its frequent UI changes, reliability issues, ill-conceived social media-like features, gradual shift toward enterprise, and inability to keep pace with newer competitors, especially during the COVID-19 pandemic, ultimately led to its obsolescence.

Existing Skype users had until May 5 to migrate their data and contacts to Teams or seek alternative solutions. Skype’s legacy lives on in the VoIP technology it helped to normalize—but as a product, it stands as a case study in how brand recognition alone can’t save a stagnant platform.

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RIP Skype (2003–2025), survived by multiple versions of Microsoft Teams

Skype is no more, as Microsoft is retiring the once-popular video calling service on Monday.

Shutting down Skype will help the software giant focus on its homegrown Teams service by simplifying its communication offerings, Microsoft announced back in February.

To ease the transition from the platform, its users will be able to log into the free version of Teams using their existing credentials, with chats and contacts migrating automatically.

“Skype has been an integral part of shaping modern communications and supporting countless meaningful moments, and we are honored to have been part of the journey,” Jeff Tepper, Microsoft president of collaborative apps and platforms, said in a statement.

Businessman discussing work on video call with team members. (iStock / iStock)

Those who do not want to use Microsoft Teams Free can export their Skype data. Those who do nothing will have their data deleted in January 2026, Microsoft’s website says.

Skype first launched in 2003. When Microsoft acquired it in 2011 for $8.5 billion, the service had around 150 million monthly users. When the popularity of Zoom surged during the COVID-19 pandemic in 2020, that number had fallen to roughly 23 million.

The decline was partly because Skype’s underlying technology was not suited for the smartphone era.

Build LLM applications via multiple agents

AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalization. With this framework, users can build LLM workflows. The agent modularity and conversation-based programming simplifies development and enables reuse for developers. End-users benefit from multiple agents independently learning and collaborating on their behalf, enabling them to accomplish more with less work. Benefits of the multi agent approach with AutoGen include agents that can be backed by various LLM configurations; native support for a generic form of tool usage through code generation and execution; and, a special agent, the Human Proxy Agent that enables easy integration of human feedback and involvement at different levels.

Microsoft AutoGen: Enabling next-generation large language model applications

It requires a lot of effort and expertise to design, implement, and optimize a workflow that can leverage the full potential of large language models (LLMs). Automating these workflows has tremendous value. As developers begin to create increasingly complex LLM-based applications, workflows will inevitably grow more intricate. The potential design space for such workflows could be vast and complex, thereby heightening the challenge of orchestrating an optimal workflow with robust performance.

AutoGen is a framework for simplifying the orchestration, optimization, and automation of LLM workflows. It offers customizable and conversable agents that leverage the strongest capabilities of the most advanced LLMs, like GPT-4, while addressing their limitations by integrating with humans and tools and having conversations between multiple agents via automated chat.

With AutoGen, building a complex multi-agent conversation system boils down to:

• Defining a set of agents with specialized capabilities and roles.
• Defining the interaction behavior between agents, i.e., what to reply when an agent receives messages from another agent.

Both steps are intuitive and modular, making these agents reusable and composable. For example, to build a system for code-based question answering, one can design the agents and their interactions as in Figure 2. Such a system is shown to reduce the number of manual interactions needed from 3x to 10x in applications like supply-chain optimization(opens in new tab). Using AutoGen leads to more than a 4x reduction in coding effort.

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Capable, conversable, and customizable agents – integrating LLMs, humans, and tools

AutoGen agents have capabilities enabled by LLMs, humans, tools, or a mix of those elements. For example:

• One can easily configure the usage and roles of LLMs in an agent (automated complex task solving by group chat) with advanced inference features (e.g., optimize performance with inference parameter tuning).
• Human intelligence and oversight can be achieved through a proxy agent with different involvement levels and patterns (e.g., automated task solving with GPT-4 + multiple human users(opens in new tab)).
• The agents have native support for LLM-driven code/function execution (e.g., automated task solving with code generation, execution and debugging(opens in new tab), use provided tools as functions(opens in new tab)).

One straightforward way of using built-in agents from AutoGen is to invoke automated chat between an assistant agent and a user proxy agent. As an example (Figure 3), one can easily build an enhanced version of ChatGPT + Code Interpreter + plugins, with a customizable degree of automation, usable in a custom environment and embeddable in a bigger system. It is also easy to extend their behavior to support diverse application scenarios, such as adding personalization and adaptability based on past interactions (e.g., automated continual learning(opens in new tab), teach agents new skills(opens in new tab)).

The agent conversation-centric design has numerous benefits, including that it:

• Naturally handles ambiguity, feedback, progress, and collaboration.
• Enables effective coding-related tasks, like tool use with back-and-forth troubleshooting.
• Allows users to seamlessly opt in or opt out via an agent in the chat.
• Achieves a collective goal with the cooperation of multiple specialists.

AutoGen supports automated chat and diverse communication patterns, making it easy to orchestrate a complex, dynamic workflow and experiment with versatility. Figure 4 illustrates a new game, conversational chess(opens in new tab), enabled by AutoGen. Figure 5 illustrates how AutoGen supports group chats(opens in new tab) between multiple agents using another special agent called the “GroupChatManager”.

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(opens in new tab)Getting started

AutoGen(opens in new tab) (in preview) is freely available as a Python package. To install it, run

pip install pyautogen

You can quickly enable a powerful experience with just a few lines of code:

import autogen
assistant = autogen.AssistantAgent("assistant")
user_proxy = autogen.UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")
# This triggers automated chat to solve the task

Check examples for a wide variety of tasks: https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat(opens in new tab).

Next steps:

• Use AutoGen in your LLM applications and provide feedback on Discord(opens in new tab)
• Read about the research:
  • AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
  • Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

AutoGen is an open-source, community-driven project under active development (as a spinoff from FLAML(opens in new tab), a fast library for automated machine learning and tuning), which encourages contributions from individuals of all backgrounds. Many Microsoft Research collaborators have made great contributions to this project, including academic contributors like Pennsylvania State University and the University of Washington, and product teams like Microsoft Fabric and ML.NET. AutoGen aims to provide an effective and easy-to-use framework for developers to build next-generation applications, and already demonstrates promising opportunities to build creative applications and provide a large space for innovation. source

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Dirty Washington and The Dirty Washington lobbists,
Their Ultra Elite Rich Puppet Masters Keep the Coffers Fully

Parent companies for Facebook and Google also spent big in 2022 amid pressure from lawmakers and regulators

Amazon.com Inc., Apple Inc. and Microsoft Corp. set records in their disclosed annual spending on Washington lobbying last year, as each tech giant shelled out more in a year than it ever has before amid pressure from Congress and the Biden administration.

Amazon AMZN, +2.57% and three subsidiaries shelled out a total of $21.38 million in 2022, above a prior record outlay of $19.32 million in 2021, according to an analysis of disclosures by OpenSecrets.

Facebook parent Meta Platforms Inc. META, +1.30% pulled back slightly in its lobbying spending, as its 2022 total was $19.15 million vs. $20.07 million in 2021, according to OpenSecrets.

Google parent Alphabet Inc. GOOG, +1.96% GOOGL, +1.96% shelled out $13.18 million to influence Washington, with seven units disclosing spending. That’s up from $11.89 million in 2021 but still well below the Silicon Valley company’s record outlay of $21.85 million in 2018, which then was followed by a major overhaul of its lobbyist teams.

Amazon, Meta and Alphabet ranked among the 20 biggest spenders on Washington lobbying in 2022, though they still were far behind the National Association of Realtors, which ranked No. 1 and disclosed paying out $81.74 million. The U.S. Chamber of Commerce ranked No. 2 and spent $81.01 million.

Amazon’s disclosure for last year’s fourth quarter showed it lobbied on a wide range of issues, including immigration, food safety, telehealth and electric vehicles. Apple reported Q4 lobbying on tax reform, copyright issues, climate change and much more, and Q4 issues for Microsoft, Meta and Alphabet included cybersecurity, infrastructure and trade agreements.

The companies didn’t immediately provide comments following requests from MarketWatch.

Meta’s stock has lost 52% over the past 12 months, while Amazon shares have fallen 32%. Microsoft is off 21% in that same period, Alphabet has shed 27%, and Apple is down 18%. The S&P 500 stock benchmark SPX, +1.46% has pulled back 10%.

By Victor Reklaitis