When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
As tech providers such as Microsoft enhance their capabilities and products, they will impact business processes and technology skills, and influence other tech providers to reshape their product and service offerings. Microsoft recently organised briefing sessions in Sydney and Singapore, to present their future roadmap, with a focus on their AI capabilities.
Ecosystm Advisors Achim Granzen, Peter Carr, and Tim Sheedy provide insights on Microsoft’s recent announcements and messaging.
Click here to download Ecosystm VendorSphere: Microsoft’s AI Vision – Initiatives & Impact
Ecosystm Question: What are your thoughts on Microsoft Copilot?
Tim Sheedy. The future of GenAI will not be about single LLMs getting bigger and better – it will be about the use of multiple large and small language models working together to solve specific challenges. It is wasteful to use a large and complex LLM to solve a problem that is simpler. Getting these models to work together will be key to solving industry and use case specific business and customer challenges in the future. Microsoft is already doing this with Microsoft 365 Copilot.
Achim Granzen. Microsoft’s Copilot – a shrink-wrapped GenAI tool based on OpenAI – has become a mainstream product. Microsoft has made it available to their enterprise clients in multiple ways: for personal productivity in Microsoft 365, for enterprise applications in Dynamics 365, for developers in Github and Copilot Studio, and to partners to integrate Copilot into their applications suites (E.g. Amdocs’ Customer Engagement Platform).
Ecosystm Question: How, in your opinion, is the Microsoft Copilot a game changer?
Microsoft’s Customer Copyright Commitment, initially launched as Copilot Copyright Commitment, is the true game changer.
Achim Granzen. It safeguards Copilot users from potential copyright infringement lawsuits related to data used for algorithm training or output results. In November 2023, Microsoft expanded its scope to cover commercial usage of their OpenAI interface as well.
This move not only protects commercial clients using Microsoft’s GenAI products but also extends to any GenAI solutions built by their clients. This initiative significantly reduces a key risk associated with GenAI adoption, outlined in the product terms and conditions.
However, compliance with a set of Required Mitigations and Codes of Conduct is necessary for clients to benefit from this commitment, aligning with responsible AI guidelines and best practices.
Ecosystm Question: Where will organisations need most help on their AI journeys?
Peter Carr. Unfortunately, there is no playbook for AI.
- The path to integrating AI into business strategies and operations lacks a one-size-fits-all guide. Organisations will have to navigate uncharted territories for the time being. This means experimenting with AI applications and learning from successes and failures. This exploratory approach is crucial for leveraging AI’s potential while adapting to unique organisational challenges and opportunities. So, companies that are better at agile innovation will do better in the short term.
- The effectiveness of AI is deeply tied to the availability and quality of connected data. AI systems require extensive datasets to learn and make informed decisions. Ensuring data is accessible, clean, and integrated is fundamental for AI to accurately analyse trends, predict outcomes, and drive intelligent automation across various applications.
Ecosystm Question: What advice would you give organisations adopting AI?
Tim Sheedy. It is all about opportunities and responsibility.
- There is a strong need for responsible AI – at a global level, at a country level, at an industry level and at an organisational level. Microsoft (and other AI leaders) are helping to create responsible AI systems that are fair, reliable, safe, private, secure, and inclusive. There is still a long way to go, but these capabilities do not completely indemnify users of AI. They still have a responsibility to set guardrails in their own businesses about the use and opportunities for AI.
- AI and hybrid work are often discussed as different trends in the market, with different solution sets. But in reality, they are deeply linked. AI can help enhance and improve hybrid work in businesses – and is a great opportunity to demonstrate the value of AI and tools such as Copilot.
Ecosystm Question: What should Microsoft focus on?
Tim Sheedy. Microsoft faces a challenge in educating the market about adopting AI, especially Copilot. They need to educate business, IT, and AI users on embracing AI effectively. Additionally, they must educate existing partners and find new AI partners to drive change in their client base. Success in the race for knowledge workers requires not only being first but also helping users maximise solutions. Customers have limited visibility of Copilot’s capabilities, today. Improving customer upskilling and enhancing tools to prompt users to leverage capabilities will contribute to Microsoft’s (or their competitors’) success in dominating the AI tool market.
Peter Carr. Grassroots businesses form the economic foundation of the Asia Pacific economies. Typically, these businesses do not engage with global SIs (GSIs), which drive Microsoft’s new service offerings. This leads to an adoption gap in the sector that could benefit most from operational efficiencies. To bridge this gap, Microsoft must empower non-GSI partners and managed service providers (MSPs) at the local and regional levels. They won’t achieve their goal of democratising AI, unless they do. Microsoft has the potential to advance AI technology while ensuring fair and widespread adoption.