If there's one thing that became clear at SXSW 2025, it's that we are no longer facing isolated technological revolutions. The future is not AI on one side, quantum computing on the other, and biotechnology in a distant corner. What is happening now is the merging of these fronts. And every time humanity experienced major technological integrations, it took leaps that changed everything.

AI is not only becoming more efficient but also learning from neuroscience. It is no longer about machines performing tasks, but about systems that evolve and influence our cognition.Amy Webb, CEO of Future Today Strategy Group, called this conceptInteligência Vivaa living ecosystem where sensors, algorithms, and biological organisms learn and make decisions autonomously. In other words, AI is ceasing to be just a tool and is becoming a living agent in the innovation equation.

But it doesn't stop there. The relationship betweenpsychedelics and quantum computingIt may seem like a science fiction concept, but both explore the limits of perception and reality. In one of the most unexpected panels of the event, researchers discussed how the effects of psilocybin, a psychoactive substance that can induce mystical experiences or perceptual changes in the brain, could inspire new forms of computational modeling and neurointerfaces. The fusion of these areas can lead to advances not only in the way we interact with technology but also in the very definition of what it means to be intelligent.

A biotechnology and roboticsThey are also crossing their own borders. We are no longer just creating prostheses or manipulating genes, but blending the organic and the synthetic in ways that redefine what it means to be human. No Brain-Skin Connection Seminar, Dr. Emily Fowlershowed how stress not only affects the emotional state but can also rewrite our biology at the cellular level. The connection between the body and technology is ceasing to be external and becoming something embedded in human functioning itself.

In the field ofquantum computing, the CEO of IBM,Arvind Krishna, stated that we are less than three years away from a major breakthrough. The race to achieve quantum supremacy is accelerating, with companies like AWS and Microsoft heavily investing in infrastructure to make these systems accessible. Meanwhile, on the panel"Preparing for a Quantum Leap", specialists such asDr. Jeanette Garcia (IBM Quantum)They discussed how this technology can revolutionize drug discovery and molecular modeling, completely transforming sectors such as health and advanced materials.

But are we ready for these integrations?Esther Perel, renowned psychotherapist, issued a warning:The more we advance in technology, the more we become disconnected as a society.Today, we are more connected to algorithms than to people. And what if the next frontier of AI is not just imitating human thought, but filling the emotional gaps we are leaving behind?

And in the middle of all this, a provocation:How does Amy Webb release a 1,000-page trend report?Is this a reflection of the absurd speed of changes or a warning that we are overwhelmed with possibilities? Are we really heading in the right direction or just running aimlessly? There is a lot of noise in this world, and it is necessary to be calm to separate them and observe the signals.

I still believe that deep tech enhances human capabilities, but for that we need to evolve as humans, take the excitement out of technology, and create real connections. Are we facing an evolutionary leap or the collapse of our own identity? We will know soon.

Without a doubt, theSXSWIt was a spectacular event of connections and networking. But, among so many interactions and exchanges of ideas, one undeniable highlight was theSP HouseIn addition to presenting relevant content, Brazil's space – yes, we have many good things to show – brought deep reflections ondiversity, a topic that, to my surprise, was almost absent in the other panels.Kond e Malheiroswere accurate in addressing the importance ofaccess and inclusion, proving that innovation without diversity is an empty concept.

Few speakers have had the courage to address issues such as thedominance and influence of big techsand thecurrent political moment in the USAIt was a rare exception in an event that, historically, has always allowed for bolder debates. The CEO ofBluesky, Jay Graber, made a direct critique of the centralized model of social networks and the growing concentration of power of digital platforms. According to her,"If a billionaire decides to change everything tomorrow, people should have the option to migrate elsewhere.", reinforcing the need for decentralization to ensure digital freedom.

And, on this matter, the debate will need to be ongoing. At the same time that these technologies promise us a more connected and efficient future, it is essential to reflect on the consequences of this evolution. The merging of fields such as AI, biotechnology, and quantum computing not only expands possibilities but also challenges the way we understand freedom and control. If we manage to balance innovation with responsibility, we can achieve breakthroughs that truly transform our lives for the better. But, for that, we need to ensure that these technologies serve humanity, and not the other way around.

More than three decades ago, Red Hat saw the potential of open source development and licenses to create better software and foster IT innovation. Thirty million lines of code later, Linux not only developed to become the most successful open source software, but also maintains that position to this day. The commitment to open source principles continues, not only in the corporate business model but also as part of the work culture. In the company's assessment, these concepts have the same impact on artificial intelligence (AI) if done correctly, but the tech world is divided on what would be the "right way."

AI, especially large language models (LLMs) behind generative AI (gen AI), cannot be viewed in the same way as an open program. Unlike software, AI models mainly consist of numerical parameter models that determine how a model processes inputs, as well as the connections it makes between various data points. Trained model parameters are the result of a long process involving vast amounts of training data that are carefully prepared, mixed, and processed.

Although the model parameters are not software, in some aspects they serve a similar function to code. It is easy to compare that the data are the source code of the model, or they would be very close to it. In open source, the source code is commonly defined as the "preferred way" to make modifications to the software. Training data alone do not fit this function, given that their size differs and due to their complex pre-training process, which results in a tenuous and indirect connection between any item of the training data and the trained parameters and the resulting behavior of the model.

Most of the improvements and enhancements in AI models happening now in the community do not involve access to or manipulation of the original training data. Instead, they are the result of modifications to the model parameters or to a process or adjustment that can also be used to tune the model's performance. The freedom to make these improvements to the model requires that the parameters be released with all the permissions that users receive under open source licenses.

Red Hat's vision for open source AI.

Red Hat believes that the foundation of open source AI lies in theopen source licensed model parameters combined with open source software componentsThis is a starting point for open-source AI, but not the final destination of philosophy. Red Hat encourages the open source community, regulatory authorities, and industry to continue striving for greater transparency and alignment with open source development principles by training and fine-tuning AI models.

This is Red Hat's vision as a company, which encompasses an open source software ecosystem, to practically engage with open source AI. It is not a formal attempt at definition, like the one thatOpen Source Initiative(OSI) is developing with itsOpen Source AI Definition(OSAID). This is the corporation's point of view that makes open source AI feasible and accessible to the largest set of communities, organizations, and providers.

This point of view in practice is put into practice through work with open source communities, highlighted by the project.InstructLab, led by Red Hat and the effort with IBM Researchin the Granite family of licensed open source modelsInstructLab significantly reduces the barriers for non-data scientists to contribute to AI models. With InstructLab, domain experts from all sectors can add their skills and knowledge, both for internal use and to help a shared open-source AI model that is widely accessible to upstream communities.

The Granite 3.0 model family handles a wide range of AI use cases, from code generation to natural language processing for extractioninsightslarge datasets, all under a permissive open source license. We helped IBM Research bring the Granite code family to the open source world and continue to support the family of models, both from an open source perspective and as part of our Red Hat AI offering.

The repercussion of therecent announcements from DeepSeekshows how open source innovation can impact AI, both at the model level and beyond. There are obviously concerns about the Chinese platform's approach, mainly that the model's license does not explain how it was produced, which reinforces the need for transparency. With that said, the mentioned disruption reinforces Red Hat's vision of the future of AI: an open future, focused on smaller, optimized, and open models that can be customized for specific enterprise data use cases across any location in the hybrid cloud.

Expanding AI models beyond open source

Red Hat's work in the open source AI space goes far beyond InstructLab and the Granite family of models, extending to the tools and platforms necessary to effectively consume and productively use AI. The company has become very active in promoting technology projects and communities, such as (but not limited to):

●      RamaLamaan open-source project aimed at facilitating the local management and deployment of AI models;

●      TrustyAI, an open-source toolkit for building more responsible AI workflows;

●      Climatik, a project focused on helping to make AI more sustainable in terms of energy consumption;

●      Podman AI Laba developer toolkit focused on facilitating experimentation with open source LLMs;

THErecent announcementabout Neural Magic broadens the corporate perspective on AI, making it possible for organizations to align smaller and optimized AI models, including licensed open source systems, with their data, wherever they reside in the hybrid cloud. IT organizations can then use the inference servervLLMto drive the decisions and production of these models, helping to build an AI stack based on transparent technologies and support.

For the corporation, open source AI lives and breathes in the hybrid cloud. The hybrid cloud provides the flexibility needed to choose the best environment for each AI workload, optimizing performance, cost, scale, and security requirements. The platforms, goals, and organization of Red Hat support these efforts, along with industry partners, clients, and the open source community, as open source in artificial intelligence is driven forward.

There is immense potential to expand this open collaboration in the field of AI. Red Hat envisions a future that encompasses transparent work in models, as well as its training. Whether next week or next month (or even sooner, given the rapid evolution of AI), the company and the open community as a whole will continue to support and adopt efforts to democratize and open up the world of AI.