Klarna CEO says he was ‘Tremendously Embarrassed’ when Marc Benioff was asked about Salesforce Exit - Related to ‘tremendously, tired, 2.0?, embarrassed’, your
Klarna CEO says he was ‘Tremendously Embarrassed’ when Marc Benioff was asked about Salesforce Exit
Klarna CEO Sebastian Siemiatkowski mentioned he was “tremendously embarrassed” after the firm’s decision to move away from Salesforce and other SaaS providers became widely discussed. Leading to speculation about the future of enterprise software.
“Benioff was asked on stage why Klarna was leaving Salesforce. I was tremendously embarrassed,” he introduced in a post on X.
Siemiatkowski confirmed Klarna had shut down Salesforce a year ago and. Removed around 1,200 SaaS applications. However, he clarified, “No, I don’t think it is the end of Salesforce; might be the opposite.”.
He explained that Klarna’s decision stemmed from a broader internal effort to consolidate its knowledge and. Reduce fragmentation in data storage. “We decided early to explore the potential of AI and LLMs—mostly ChatGPT—while being open to testing all things that seemed to be trending,” he stated. This led to a realisation that corporate data was often fragmented across multiple platforms, making it difficult to extract value.
Klarna leveraged technology from Neo4j and. Other tools to structure and unify its data. The firm’s internal AI system was then able to use this knowledge, driving productivity gains. “We started bringing data=knowledge together,” Siemiatkowski mentioned, adding that Klarna also used Cursor AI to deploy new interfaces.
The decision to discontinue Salesforce was not originally intended to be public. However, Siemiatkowski expressed that during a routine investor call, he mentioned Klarna had removed some SaaS, including Salesforce. This was later picked up by Seeking Alpha, leading to widespread speculation.
Despite shutting down SaaS applications. Siemiatkowski suggested that the future could see a consolidation of enterprise software providers rather than their decline. “It is very likely that Salesforce will be one of those companies,” he mentioned. “They do so much more than CRM today and hence have the opportunity to become that hub of knowledge that modern companies will seek.”.
He also noted a challenge for large SaaS providers. Stating that some had lost their original vision in an effort to cater to multiple enterprise demands. “They started as companies with a clear opinion of how to do things, but over time, as they try to satisfy every whim of any random person working at any large enterprise. They become somewhat of a glorified database.”.
Siemiatkowski concluded that all SaaS providers would need to evolve in an AI-first world. “If they do, there is tremendous opportunity ahead.”.
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Tired of waiting for Siri 2.0? Try these advanced AI voice assistants on your iPhone today
The launch of ChatGPT sparked a generative AI craze, igniting a tech revolution that has forced companies to rapidly innovate to stay competitive in this evolving landscape.
Although Apple was late to the AI race, its launch of Apple Intelligence promised a transformative overhaul. Putting Siri at the center of the Apple ecosystem as a context-aware personal assistant. However, a new Bloomberg findings implies this vision may take longer to materialize than expected.
When can you expect the AI-improved Siri?
When Apple originally showed off the concept at WWDC last June. It was marketed as a personal assistant that seamlessly integrates into a user's existing device ecosystem to provide meaningful behind-the-scenes help. Additionally, it would finally make Siri more conversational, enabling more human-like conversation, a highly requested upgrade.
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However, since then. The organization has rolled out only a handful of Apple Intelligence elements, most of which have low helpfulness value. For example, clients with eligible phones can now access Genmoji, Image Playground, notification summaries, writing tools, voicemail transcriptions, Visual Intelligence, and a ChatGPT integration. Ultimately, all of these elements have fallen short, not adding much to the everyday smartphone experience.
Apple Intelligence also continues to trail behind competitors. Just last week, Amazon launched Alexa+, a conversational voice assistant with agentic capabilities that allow it to perform everyday tasks for you. It also uses your personal context and habits to provide superior assistance and is coming to Alexa-enabled products already in people's homes.
Before Amazon's Alexa+ launch, Google and ChatGPT each unveiled their own AI-powered conversational assistants. Gemini Live and Advanced Voice Mode. These assistants understand your prompts in natural language, meaning you can speak to the AI as you would a friend. They also have multi-turn conversations, so you can keep the conversation going as long as you'd like without losing prior context.
Both voice assistants have settings that make them easy to access from an iPhone, allowing iOS clients to forgo Siri for a more conversational, AI-enhanced experience.
ChatGPT's counterpart, Advanced Voice Mode. Even has on-screen and camera awareness, making its assistance multimodal and adding an extra layer of support. As an iPhone user, you can easily access the assistant from the ChatGPT app, or if you want even more seamless access, you can even map it to your phone's Action Button to summon ChatGPT.
Also: How to program your iPhone's Action Button to summon ChatGPT's voice assistant.
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As spotted by 9to5Google. The Gemini iPhone app was also just upgraded to make it even easier to use. The new lock screen widgets include a "Talk Live" widget that activates the Gemini Voice assistant with a quick tap from your lock screen. You can also add it to your Control Center, making it even more accessible anytime by swiping down on your screen.
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ReaLJam AI Lets Musicians Jam Anytime, Anywhere
ReaLJam, an AI interface and protocol developed by researchers at Google DeepMind, University of Montreal, and University of Massachusetts Amherst, allows musicians to have a real-time jam session with an AI partner.
AIM came across a new research paper about ReaLJam, in which it learned that this technology uses a combination of reinforcement learning and a user interface to create a seamless and. Enjoyable experience for both the human and AI participants. One of the key elements of ReaLJam is its ability to anticipate the user’s melody and plan its chords accordingly.
This is achieved through a “waterfall display” that presents the upcoming chords to the user. Allowing them to adjust their playing in response to the AI’s anticipated moves. The system also includes various settings that allow people to customise the experience to their liking, such as the tempo, time signature. And the complexity of the AI’s responses.
In a user study conducted with experienced musicians, ReaLJam received positive feedback. Participants praised the system’s low latency, ease of use, and ability to generate interesting and surprising musical ideas.
in the recent past, many people are trying to explore the use of AI in music, with many music generators available.
Commenting on the convenience of having an AI jam partner, one user expressed, “It was nice to be able to jam without scheduling with another person.” Meanwhile, another user highlighted the AI’s ability to generate unexpected musical elements. Who expressed, “It kept me on my toes [with respect to] which chord comes next.”.
The researchers behind ReaLJam believe that the system has the potential to revolutionise the way musicians create and learn music. By providing a readily available and adaptable AI jam partner, ReaLJam could open up new creative possibilities for musicians of all levels.
Whether it is used for educational purposes or brainstorming. It should be an interesting experience with such systems evolving in the near future.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Klarna Says Tremendously landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- General AI capabilities
- AI-driven scientific breakthroughs
Expert Perspectives
Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."
— AI Researcher
"Organizations that develop effective AI governance frameworks will gain competitive advantage."
— Industry Analyst
"The AI talent gap remains a critical barrier to implementation for most enterprises."
— Chief AI Officer
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:
- Improved generative models
- specialized AI applications
- enhanced AI ethics frameworks
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- AI-human collaboration systems
- multimodal AI platforms
- democratized AI development
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- General AI capabilities
- AI-driven scientific breakthroughs
- new computing paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of ai tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Responsible AI driving innovation while minimizing societal disruption
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Incremental adoption with mixed societal impacts and ongoing ethical challenges
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and ethical barriers creating significant implementation challenges
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
interface intermediate
generative AI intermediate
reinforcement learning intermediate
API beginner
How APIs enable communication between different software systemsplatform intermediate
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