Gone are the days of installing software from multiple CD-ROM discs; the Software as a Service (SaaS) subscription model, where users pay per user, has taken over. The evolution continues with a shift towards consumption-based pricing, especially in the new AI era. Prasana Krishnan, Senior Director of Product Management at Snowflake, notes that this trend will significantly impact pricing models. While straightforward, the user-based model is deemed inefficient, especially as applications leverage models trained with diverse data sources. Paying for data and applications based on actual usage is appealing, but the transition poses challenges for organizations unfamiliar with pay-as-you-go models.

Facilitating this transition on Snowflake, Krishnan emphasizes the need for visibility into costs, budget establishment, and controls for true usage-based models. Yet, she sees this shift as empowering innovation, particularly in the rapid prototyping phase of AI experimentation. This dynamic pricing approach allows experimentation without hefty upfront costs.

This transition doesn't signal the end of the subscription model. Usage-based pricing may serve as an entry-level tier, enabling the shift from prototype to production. As a developer's idea gains momentum, Krishnan suggests it could evolve into a flat fee subscription, catering to sustained, high usage.

Due to the abundance of sensitive data that enterprises need to manage, the predominant trend is the utilization of in-house Large Language Models (LLMs), rather than relying on public tools like ChatGPT. Imagine a scenario where a shipping company acquires a foundational model from entities like OpenAI and subsequently trains it using proprietary data. However, their aspirations extend beyond this.

According to Jennifer Belissent, Snowflake’s Principal Data Strategist, companies not only feed their internal LLMs with proprietary data but also seek to acquire external data sets tailored to their specific industry or market. Business leaders, she notes, require a broader perspective than what their internal data alone can offer.

Belissent emphasizes the importance of understanding regional trends, industry benchmarks, and contextual factors for businesses to uncover opportunities beyond their internal insights. This necessity positions businesses not merely as consumers but as potential contributors to the data market.

As an example, she cites ADP, a payroll software maker, whose ADP Insights data product analyzes pay growth across the United States based on criteria such as region, industry, gender, and more. The growing demand for data presents an opportunity for businesses to not only meet their internal needs but also to capitalize on selling their data to a market with a substantial appetite for information.

For years, companies have received strong recommendations to develop a comprehensive and forward-looking data strategy. However, just as businesses were making strides in this direction, the rapid advancements in AI pose a potential challenge to render last year's plans obsolete.

Fortunately, our experts unanimously agree that if you have already invested effort in establishing a robust data strategy, you are on the right path. Jennifer Belissent, Principal Data Strategist at Snowflake, emphasizes that the generative AI era does not demand a fundamental shift in data strategy but rather an acceleration of the trend towards breaking down silos and opening access to data sources within the organization.

Mona Attariyan, Snowflake's Director of Machine Learning, underscores the importance of the level of your data strategy and the urgency to execute and invest in it promptly. Falling behind in acceleration could risk being left behind virtually overnight.

However, embracing generative AI doesn't imply pursuing all promised miracles simultaneously. While assistance with basic coding and copywriting is beneficial, leaders should prioritize unique insights derived from their own data. According to Snowflake Co-Founder Benoit Dageville, applications of generative AI will touch various aspects of a business, but focusing on core needs remains paramount.

Snowflake CIO Sunny Bedi stresses that governance is non-negotiable and a prerequisite for entering the world of generative AI and LLMs. Security, governance, and compliance are deemed the minimum requirements.

In addressing the challenges posed by the new AI era to the IT status quo, Bedi and Anoosh Saboori highlight the significance of data placement. Generative AI's influence will drive a trend towards centralized data on a managed service platform, providing the necessary security and governance while creating a single source of truth for LLMs and other applications.

Saboori, Snowflake’s Head of Product Security, foresees a rapid shift for cloud-adoption laggards, as generative AI makes it challenging to maintain on-premises data. The move to the cloud, while inevitable, presents complexities in security and compliance strategy, especially with the adoption of multi-cloud approaches leading to distributed data and models across platforms with varying security and governance capabilities.

Sridhar Ramaswamy concurs with Attariyan regarding increased governmental involvement in the early stages. In mid-September, the U.S. Senate hosted a private informational roundtable featuring leaders from OpenAI, NVIDIA, Google, Meta, and more. However, Ramaswamy cautions that swift regulatory intervention may not necessarily address the issues efficiently.

Highlighting the complexity of narrow tech regulation, Ramaswamy references Section 230 of the 1996 U.S. Communications Decency Act, which grants websites immunity for third-party content. While pivotal for shaping the internet, Section 230 has contributed to issues such as misinformation, hate speech, and bullying.

Anoosh Saboori, Snowflake’s Head of Product Security, anticipates that industry leaders, particularly chief information security officers (CISOs), will play a pivotal role in addressing ethical concerns and liability risks associated with generative AI. He emphasizes the need for a framework to evaluate generative AI providers, focusing on security, compliance, and ethical use.

Saboori asserts that market dynamics will drive change, with the government subsequently following suit. He notes signals from the government indicating a proactive approach, soliciting input from the industry to define responsible AI standards.

The latest AI advancements have prompted significant concerns, with over 350 tech experts, executives, and industry leaders, including Geoffrey Hinton, a prominent figure in AI, signing an open letter in March 2023. The letter emphasized the need to prioritize mitigating the risks of AI, likening it to global priorities such as pandemics and nuclear war.

Responsible development and use of AI necessitate proactive efforts by both industry stakeholders and governments to establish ethical and secure guidelines. Despite past shortcomings in addressing privacy concerns within the tech industry, the challenges encountered may contribute to the formulation of more effective AI guidelines in a more timely manner.

Mona Attariyan notes that the current AI boom is occurring at an opportune time, with recent privacy issues serving as a wake-up call. While acknowledging past challenges, she expresses optimism that the present generation of technology creators is genuinely committed to addressing these issues responsibly.

Yes, AI will be just a tool in the creator's hands, and humans will still make the big swings.

Founded on a commitment to innovation, Snowflake originated when a pair of database experts at Oracle envisioned creating a distinctive data platform. Benoit Dageville, one of these experts and Snowflake’s President of Product, contends that generative AI is poised to bring about more incremental, iterative innovation. While AI can play a pivotal role in research and prototyping, Dageville emphasizes that profound innovation requires a leap that challenges existing data and prevailing beliefs.

Dageville draws on Snowflake's own history as an example, highlighting how they diverged from the prevailing belief in Hadoop's future for big data, ultimately creating something contrary to data-driven expectations. According to him, human-driven innovation and decision-making will persist, with Christian Kleinerman, SVP of Product, emphasizing the collaborative aspect of human-plus-machine use cases. Humans will remain responsible for empathy and qualities challenging to program into machines. For computational or mechanical tasks, AI models prove to be astonishingly effective.

Dageville expresses enthusiasm for the potential of generative AI but firmly asserts that it doesn't pose a threat to human creativity. While AI excels at analyzing data to identify new customer segments, Dageville believes that true, revolutionary creativity stems from human hands, with AI serving as a valuable tool. He aptly notes, "If you want to create a revolution, that revolution, by definition, does not exist. So there's no data on it.

Adapting to these changes may pose challenges, admits Mona Attariyan, stating, "It will be painful to adapt. However, my optimism lies in the belief that, as a society, we will collectively navigate through these challenges. There is a heightened awareness of potential downsides, and I observe both business and government leaders taking proactive steps to address these issues early on."

Another concern is the rise of deepfakes. While responsible AI creators discuss incorporating digital watermarks to identify fake content, the anticipation of bad actors finding workarounds or less ethically constrained toolmakers raises uncertainties. Sridhar Ramaswamy foresees a future where a collective assault on our perceived reality occurs due to AI-generated content, emphasizing the need for addressing this substantial challenge.

Highlighting the potential pain points, Mona Attariyan acknowledges the significance of regulations in combating disinformation. She emphasizes the necessity for companies and regulators to take decisive actions in relevant areas.

Looking ahead, Ramaswamy expresses long-term concerns about an exacerbated digital divide. He fears that the advancements in generative AI might widen the gap between the privileged and underprivileged, a trend observed over the past two to three decades. Simultaneously, he holds hope that by enhancing information accessibility, this technology can empower a new generation of young adults who possess a better understanding of the associated issues and potential, thereby mitigating the risk of increased inequality.

Here are some of the negative effects of Gen AI:

Managing the adverse impacts of Generative AI, encompassing job displacement, the rise of deepfakes, and an exacerbation of the digital divide, will present considerable challenges in the initial stages. The tech industry often tends to downplay the potential negative consequences associated with emerging technologies. While emphasizing the positive aspects is commendable, a pragmatic assessment cannot disregard the negative implications. Snowflake's experts have identified three immediate concerns that will pose substantial challenges during the initial widespread availability of Generative AI and Large Language Models.

Primarily, the impact on employment raises apprehensions. Despite optimistic perspectives asserting that AI will augment human capabilities rather than replace them, the reality becomes less optimistic when AI enables an individual to perform the workload of two people. Sridhar Ramaswamy notes that numerous jobs in the realm of 'knowledge work' could potentially vanish. Citing CBS News, he highlights the report of 4,000 jobs succumbing to AI in May 2023 alone. Drawing parallels to past technological advancements like the PC, which similarly resulted in job losses and economic sector disruptions, Ramaswamy emphasizes the challenge of swiftly absorbing displaced workers into alternative sectors due to the rapid changes induced by widespread AI adoption. He underscores the need for both the private sector and governments to proactively address this issue.

Initially, the overarching perspective: Following a year marked by extensive generative AI excitement, a predictable wave of skepticism has emerged. Despite legitimate concerns from corporate leaders regarding costs and technical challenges potentially impeding generative AI and Large Language Models (LLMs) deployment, it's crucial to note that this tech is not a fleeting trend.

Mona Attariyan, Snowflake’s Director of Machine Learning, draws a parallel to the transformative impact of smartphones, stating, "It’s comparable to the arrival of the smartphone. Since the iPhone, our interaction with data and applications has skyrocketed, fundamentally altering how we navigate our lives. The advent of generative AI represents a similar paradigm shift, only happening at a much swifter pace."

Acknowledging the tangible disruptions introduced by AI, Christian Kleinerman, Snowflake’s Senior Vice President of Product, emphasizes the substantial opportunities for enhancing business dynamics, ranging from individual productivity to pioneering innovative end-user experiences. He foresees consequential changes in roles, responsibilities, and skill sets.

Amanda Kelly, Co-Founder of Streamlit, notes the evolution in the tech industry, asserting, "For decades, the tech industry has integrated data and digital technology into our work, yet the fundamental aspects of our day-to-day tasks have remained relatively unchanged. We are now at a juncture where technology not only enhances efficiency but empowers business professionals to truly revolutionize their work methodologies."

In the immediate future, this signifies a genuine "democratization of data." Natural language interfaces are facilitating business decision-makers to delve deep into data that previously necessitated the intervention of gatekeepers such as data scientists, business analysts, and other highly technical experts.

Here we pick up from where we left off, the predictions for how AI & LLMs will shape 2024.

Amanda Kelly, Co-Founder of the open-source app framework Streamlit, which was acquired by Snowflake in 2022, highlights the historical challenge where despite data becoming more accessible for businesses, it has been confined within databases, with data scientists and technical experts serving as gatekeepers. According to Kelly, generative AI is now unlocking direct access to insights for decision-makers. She draws a parallel to the way robotic machinery, capable of lifting tons, aids in physical work, emphasizing that we are just beginning to witness how AI can support cognitive work.

Notably, our 2024 predictions in data and technology heavily emphasize the impact of an AI revolution centered around the capabilities of Large Language Models (LLMs) and the transformative potential of natural language interfaces. This evolution enables computers to comprehend information extensively, allowing users to communicate with data in a more human-like manner. As we explore these advancements, it becomes evident that they carry implications for technology-related jobs, data strategy, cybersecurity, and various other domains.

In the immediate future, the influence of generative AI supported by LLMs will manifest as heightened productivity and enriched insights. These potent tools are envisioned to act as aids to workers rather than replacing them. While current productivity tools for coding or text generation necessitate substantial human oversight, there is an acknowledgment that this may evolve rapidly. Eventually, these productivity-enhancing tools could have a more pronounced impact on staffing requirements.

At present, it is crucial to consider these developments as enhancements rather than replacements. Prasanna Krishnan, Senior Director of Product Management for Collaboration and Snowflake Marketplace, underscores the complexity of the data → insight → action process, emphasizing that despite appearing as three steps, the process is inherently challenging.

Prasanna Krishnan, Senior Director of Product Management for Collaboration and Snowflake Marketplace, underscores the intricacies of the data-to-action process, stating, "It may really be 10 steps to get from data to insight, and 20 to get from insight to action. Generative AI will play a pivotal role in streamlining these steps for end-users, enhancing efficiency and velocity. The 30 steps between data and action might potentially be reduced to five or even two."

Sridhar Ramaswamy, Snowflake's Senior Vice President and the innovator behind Google AdWords, as well as the creator of Neeva—an AI-based search engine acquired by Snowflake in 2023—concurs with the anticipation of heightened efficiency and convenience. Ramaswamy envisions that the ability to interact with applications in a conversational manner, akin to human interaction, will usher in numerous incremental improvements. Reflecting on a recent experience, he notes, "The other day, while filling out an online form requiring a time entry, it struggled to comprehend '4pm' or '1600'—forcing me to decipher it actually wanted '0400:00.' Eliminating such friction in our daily interactions with various online entities holds immense positive potential. Whether it's receiving better-suggested sentences, an email template, or assistance in understanding complex text, these advancements are inherently positive.

Generative AI emerged as the prominent narrative in the technology landscape throughout 2023.

Amidst the continuous influx of articles, announcements, and commentaries, it occasionally appeared as the singular tech focus for the year. However, despite the pervasive media attention reminiscent of high-profile figures like the Kardashians, it is imperative not to be dissuaded. Generative AI and the substantial language models (LLMs) underpinning it merit careful consideration and are poised to persist as the prevailing technological trend for the foreseeable future.

In May, NVIDIA, the creator of AI-capable "superchips," achieved a market capitalization exceeding $1 trillion. Concurrently, San Francisco is witnessing a resurgence in the occupancy of office spaces previously vacated due to the pandemic, attributed to a surge in AI startups. The discourse surrounding AI encompasses predictions that it will both displace and generate employment, revolutionize disease identification and drug discovery, empower and identify deepfakes, impact and potentially reshape educational paradigms, challenge democratic processes, either fuel or stifle creativity, assume roles of companionship and therapy, and even extend its influence to culinary tasks such as taking orders and flipping burgers at drive-thrus.

During our recent gathering of key leaders and experts at Snowflake to deliberate on the upcoming year and beyond, the predominant topic of discussion was undeniably generative AI.
Mona Attariyan, Director of Machine Learning at Snowflake, emphasized the pervasive nature of generative AI and Large Language Models (LLMs): "Generative AI and LLMs are unquestionably the most discussed topics at present—almost monopolizing discussions within the field of machine learning."

This prevailing emphasis on generative AI is justified, according to Christian Kleinerman, Senior Vice President of Product at Snowflake. He asserted, "We are on the brink of significant disruption, particularly concerning end-user experiences and interactions with technology." Kleinerman believes the transformative change being heralded is not mere hype but an authentic and substantial shift. "Generative AI and its allied technologies will have a substantial impact on productivity, redefine job roles and responsibilities, facilitate creative processes, and usher in entirely novel experiences."

A proactive approach to data strategy is now more crucial than ever. Especially for enterprises. Large language models present both significant opportunities and the potential risk of falling behind. Snowflake's 2024 predictions report delves into the forces that will:

1. Propel your data strategy forward.
2. Influence not only your approach to AI but also the applications that capitalize on it.
3. Open avenues for generating new revenue through data monetization.
4. Elevate your expectations for expert technical roles, spanning from data scientists to BI analysts.

We will cover Snowflake's 2024 Data & AI predictions report in full detail in the following Q&A's.

Stay tuned!

Harnessing deep learning promises enhanced efficiency, profound insights, and new opportunities, yet it also introduces challenges like deepfakes and misinformation. This transformative force is poised to reshape entire economies and business methodologies, unfolding at an unprecedented pace in human history. We will delve into the AI impact on:

1. Human-centered innovation
2. Ethics and regulatory regimes
3. Job creation, transformation, and elimination

Some of the featured speakers of the Data Cloud Summit 2024 are:

Frank Slootman:
Chairman and CEO of Snowflake

Benoit Dageville
Co-Founder & President of Product of Snowflake

Christian Kleinerman
SVP of Product of Snowflake

Allison Lee
VP, Engineering of Snowflake

Sridhar Ramaswamy
SVP of AI of Snowflake

Denisse Person
Chief Marketing Officer of Snowflake

We are looking forward to hearing the knowledge of all the featured speakers!

Generative AI and large language models (LLMs) are poised to redefine our lifestyles, professional landscapes, and business operations.

Snowflake's leading experts share perspectives on navigating this transformative era, covering topics such as the influence of gen AI and LLMs on daily life, the sweeping impact of data-driven technology in the corporate realm, the reciprocal transformation of open source and these technologies, and the significant implications of advanced data modeling on cybersecurity.

In the next Q&A’s we will go into more detail.

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June 3, 2024 June 6, 2024
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-Keynotes
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Stand out within the data community by achieving your SnowPro Certification directly at the Summit, with convenient scheduling and exam dates available throughout the week. Benefit from a comprehensive day of specialized training, comprising lectures, demonstrations, and hands-on labs. Our seasoned instructors will lead you through various hands-on scenarios, assisting you in architecting, comprehending, and implementing the latest techniques, features, and best practices.

There are three types of training and exams available:

1. BEGINNER TRAINING: (SNOWFLAKE FOUNDATIONS)

Enroll in this course designed as a primer on the Snowflake Data Cloud for essential stakeholders. The curriculum includes lectures, demonstrations, and hands-on labs covering core concepts and design aspects. Additionally, consider this course as a foundational step for more role-specific training in Snowflake administration, data engineering, and data science.

2. ADVANCED TRAINING: (FRESH SNOW)

Dive deep into the myriad features unveiled in the Snowflake Data Cloud over the last 12 months. This dynamic workshop is tailored for proficient Snowflake users seeking an in-depth exploration.

3. SNOWPRO CERTIFICATION EXAMS

Take advantage of reduced exam fees when you obtain your SnowPro Core or Advanced certification directly at the Snowflake Data Cloud Summit.

There are three main overarching reasons to attend this particular Snowflake Summit 2024:

PARTNER:
Connect with a multitude of professionals and organizations within the Data Cloud. Forge partnerships to create novel data assets, services, models, and applications that will propel the next era of value for your business. Harness the collective strength of the Data Cloud community to explore inventive approaches in fortifying your data and AI strategy, uncovering fresh opportunities for business growth.

EXPAND YOUR KNOWLEDGE:
Participate in enlightening keynotes, immersive breakout sessions, hands-on labs, and comprehensive training and certifications. Acquire insights into the latest innovations within the Data Cloud, designed to optimize the value of your most crucial workloads—spanning data engineering, data lakes, data warehouses, AI/ML, applications, collaboration, cybersecurity, and the seamless integration of transactional and analytical data (Unistore).

ENVISION:
Gain insights from data, AI, and business leaders on steering your organization toward enhanced agility and success. Be motivated by their stories on leveraging the potential of the Data Cloud to take the lead in their respective industries, explore new markets, and elevate customer service standards.

The Snowflake Summit is making its way back to San Francisco from June 3–6, 2024! Immerse yourself in the forefront of innovation within the Data Cloud, featuring advancements in AI, genAI, Apache Iceberg, streaming, privacy-preserving collaboration, flexible programmability, application development, and a plethora of other groundbreaking technologies.

I believe Snowflake's Virtual Warehouses have the potential to significantly foster collaboration in 2024 in several ways:

1. Shared Data Workspaces: By enabling the creation of virtual warehouses accessible to multiple users and teams, Snowflake removes data silos and facilitates collaborative data analysis. Imagine analysts, data scientists, and business users working side-by-side on the same data set, leading to a richer understanding and more informed decisions.

2. Real-time Collaboration: With features like live data sharing and collaborative dashboards, Snowflake allows teams to see changes and insights in real-time, fostering more dynamic and interactive collaboration. Imagine brainstorming data visualizations and tweaking queries together, leading to faster problem-solving and agile decision-making.

3. Democratized Data Access: Snowflake empowers non-technical users to explore data through self-service analytics tools within virtual warehouses. This breaks down technical barriers and allows various stakeholders to contribute their unique perspectives to data-driven projects, fostering cross-functional collaboration.

4. Version Control and Data Governance: Features like virtual warehouse replicas and granular access controls enable collaborative data exploration while maintaining data integrity and security. Imagine teams working on different versions of data sets for specific needs, knowing everything is securely managed and version-controlled within the platform.

5. Scalable Collaboration: With its ability to seamlessly scale resources, Snowflake ensures virtual warehouses can accommodate growing teams and complex data workloads. This allows collaboration to thrive without performance bottlenecks, empowering large teams to work together on massive data sets.

Of course, challenges exist, such as ensuring data quality and access control across varying user levels. However, Snowflake's focus on security, governance, and user-friendly collaboration tools positions it well to address these challenges and become a true hub for data-driven collaboration in 2024.