The content of this presentation needs to cover the future development of AI trends, as well as how Microsoft Copilot AI can maintain its competitiveness in the future. I hope there is no

Carrying out a competitive analysis of different companies that are leading in adopting and using AI in their technologies is one thing that will help chatGPT improve and find areas that it can learn from[footnoteRef:1]. Carrying out a competitive analysis of the current leading competitor between generative AI and Microsoft Copilot AI will help achieve the above objective[footnoteRef:2]. The goal is to bring to light some benefits of the Copilot, including the competitiveness of ChatGPT, which may also be a great source of information for its weaknesses[footnoteRef:3]. [1: Adetayo, Adebowale Jeremy, Mariam Oyinda Aborisade, and Basheer Abiodun Sanni. "Microsoft Copilot and Anthropic Claude AI in education and library service." Library Hi Tech News (2024). https://www.emerald.com/insight/content/doi/10.1108/LHTN-01-2024-0002/full/html] [2: Ebert, Christof, and Panos Louridas. "Generative AI for software practitioners." IEEE Software 40, no. 4 (2023): 30-38. https://ieeexplore.ieee.org/abstract/document/10176168/] [3: Coyle, Jeff, and Stephen Jeske. "The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide." Applied Marketing Analytics 9, no. 3 (2023): 207-214. https://www.ingentaconnect.com/contentone/hsp/ama/2023/00000009/00000003/art00002 ]

Competitive Advantages of Microsoft Copilot AI:

1. Rich Integration with Development Environments: Copilot can seamlessly integrate with different development environments, such as Visual Studio Code[footnoteRef:4]. The integration is essential as it provides developers with convenience[footnoteRef:5]. The integration also improves the overall development experience needed in this field[footnoteRef:6]. [4: Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., & Gazit, I. (2022). Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue, 20(6), 35-57. https://dl.acm.org/doi/abs/10.1145/3582083] [5: Keisala, Jukka. "Utilizing Large Language Models as No-code Interface in a Software Development Toolkit." (2023). https://www.theseus.fi/handle/10024/796868] [6: Adetayo, A. J., Aborisade, M. O., & Sanni, B. A. (2024). Microsoft Copilot and Anthropic Claude AI in education and library service. https://www.emerald.com/insight/content/doi/10.1108/LHTN-01-2024-0002/full/html]

2. Learning from Open-Source Repositories: The deep learning model is Microsoft Copilot's core, adapted from GitHub, a well-known place for open-source code and resources[footnoteRef:7]. Through this exposure, Copilot develops the skill to produce code snippets that concisely communicate ideas using the best practices[footnoteRef:8]. The coding platform builds applications from several coding projects that provide endless learning opportunities for personalizing a developer's work[footnoteRef:9]. [7: KANTEK, BC PETR. "AI-driven Software Development Source Code Quality." https://is.muni.cz/th/mdt17/kantek_dp.pdf] [8: Khalfi, Mohammed Fethi, Mohammed Nadjib Tabbiche, and Reda Adjoudj. "From programming-to-modeling-to-prompts smart ubiquitous applications." Journal of Ambient Intelligence and Smart Environments Preprint (2023): 1-39. https://content.iospress.com/articles/journal-of-ambient-intelligence-and-smart-environments/ais220355] [9: Da Cruz, Mauro AA, Heitor TL de Paula, Bruno PG Caputo, Samuel B. Mafra, Pascal Lorenz, and Joel JPC Rodrigues. "Olp—a restful open low-code platform." Future Internet 13, no. 10 (2021): 249. https://www.mdpi.com/1999-5903/13/10/249]

3. Effective Collaboration with Developers: Copilot will bring together developers via efficient engagement[footnoteRef:10]/[footnoteRef:11]. Collaboration is one trait of Copilot that is usually of high value when it comes to group work, and the attribute of Copilot promotes team-based, streamlined, and cooperative environments[footnoteRef:12]. [10: Pudari, Rohith, and Neil A. Ernst. "From Copilot to Pilot: Towards AI Supported Software Development." arXiv preprint arXiv: 2303.04142 (2023). https://dl.acm.org/doi/abs/10.1145/3582083] [11: Pudari, Rohith, and Neil A. Ernst. "From Copilot to Pilot: Towards AI Supported Software Development." arXiv preprint arXiv: 2303.04142 (2023). https://arxiv.org/abs/2303.04142] [12: Coyle, J., & Jeske, S. (2023). The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide. https://www.ingentaconnect.com/contentone/hsp/ama/2023/00000009/00000003/art00002]

Implications for ChatGPT:

1. Integration with Development Environments: The successful integration of Visual Studio with Copilot shows the necessity of seamless developer tool-AI tool interaction[footnoteRef:13]. Thus, ChatGPT might use the possibilities described here to build relationships with well-known IDEs[footnoteRef:14]. This also provides coding environments to improve developers’ user experience[footnoteRef:15]. [13: Kumar, Yulia, Zachary Gordon, Oluwatunmise Alabi, Jenny Li, Kathryn Leonard, Linda Ness, and Patricia Morreale. "ChatGPT Translation of Program Code for Image Sketch Abstraction." Applied Sciences 14, no. 3 (2024): 992. https://www.mdpi.com/2076-3417/14/3/992] [14: Polverini, Giulia, and Bor Gregorcic. "How understanding large language models can inform the use of ChatGPT in physics education." European Journal of Physics 45, no. 2 (2024): 025701. https://iopscience.iop.org/article/10.1088/1361-6404/ad1420/meta] [15: Borger, Jessica G., Ashley P. Ng, Holly Anderton, George W. Ashdown, Megan Auld, Marnie E. Blewitt, Daniel V. Brown et al. "Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI‐assisted technologies in scientific research and education." Immunology and Cell Biology 101, no. 10 (2023): 923-935. https://onlinelibrary.wiley.com/doi/abs/10.1111/imcb.12689]

2. Language Support and Versatility: The vast scope of languages and frameworks that Copilot can relate to highlights the developer community’s vast appetite for flexible AI lenses[footnoteRef:16]. Regarding ChatGPT’s abilities, the tool should be built up to cover numerous domains of technical subjects[footnoteRef:17]. The tool should include more than just a narrow field to keep it on top of the competition and fulfill the demands of users[footnoteRef:18]. [16: Hall, Patrick, James Curtis, and Parul Pandey. Machine learning for high-risk applications. " O'Reilly Media, Inc.", 2023. https://books.google.com/books?hl=en&lr=&id=pgu6EAAAQBAJ&oi=fnd&pg=PT61&dq=The+vast+scope+of+languages+and+frameworks+that+Copilot+is+able+to+relate+to+highlights+the+vast+appetite+for+flexible+AI+lenses+among+the+developer+community&ots=ElLGlPU_u9&sig=TLBp9nnHtPf9v9tg6cWMAIcDUgA] [17: Haleem, Abid, Mohd Javaid, and Ravi Pratap Singh. "An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges." BenchCouncil transactions on benchmarks, standards and evaluations 2, no. 4 (2022): 100089. https://www.sciencedirect.com/science/article/pii/S2772485923000066] [18: Frieder, Simon, Luca Pinchetti, Ryan-Rhys Griffiths, Tommaso Salvatori, Thomas Lukasiewicz, Philipp Petersen, and Julius Berner. "Mathematical capabilities of chatgpt." Advances in Neural Information Processing Systems 36 (2024). https://proceedings.neurips.cc/paper_files/paper/2023/hash/58168e8a92994655d6da3939e7cc0918-Abstract-Datasets_and_Benchmarks.html]

3. Facilitating Collaboration: The mention of Copilot as a partner in coding is a sign of the usefulness of such AI tools in general[footnoteRef:19]. It also suggests an increasing demand for AI tools that encourage working together and talking together[footnoteRef:20]. In response, the Chabot could add features that elevate the collaboration level – for instance, it might support the working process of writing together or being involved in the collective decision-making processes[footnoteRef:21]. [19: Dakhel, Arghavan Moradi, Vahid Majdinasab, Amin Nikanjam, Foutse Khomh, Michel C. Desmarais, and Zhen Ming Jack Jiang. "Github copilot ai pair programmer: Asset or liability?." Journal of Systems and Software 203 (2023): 111734. https://www.sciencedirect.com/science/article/pii/S0164121223001292] [20: Aydin, Ömer, and Enis KARAARSLAN. "Is chatGPT leading generative AI? What is beyond expectations?." Academic Platform Journal of Engineering and Smart Systems 11, no. 3 (2023): 118-134. https://dergipark.org.tr/en/pub/apjess/issue/80086/1293702] [21: Baskara, Risang. "Exploring the implications of ChatGPT for language learning in higher education." Indonesian Journal of English Language Teaching and Applied Linguistics 7, no. 2 (2023): 343-358. https://eric.ed.gov/?id=EJ1391490]

Looking at the competitive advantages of Microsoft Copilot AI, this underscores the landscape of generative AI tools[footnoteRef:22] that are continuously evolving. This, in the process, emphasizes the significance of versatility, accuracy, integration, collaboration, and even real-world data learning[footnoteRef:23]. By ensuring that the insights from Copilot’s strengths are incorporated, ChatGPT can use this to position itself, ensure that it stays competitive, and continue providing valuable solutions to the developers[footnoteRef:24]. [22: Scappaticci, Chiara Silveira. "Artificial intelligence: how can Gen-AI tools support the current business models of the firms and add value?." PhD diss., 2023.] [23: Grassini, Simone. "Shaping the future of education: exploring the potential and consequences of AI and ChatGPT in educational settings." Education Sciences 13, no. 7 (2023): 692. https://www.mdpi.com/2227-7102/13/7/692] [24: Merali, Shabbir, and Ali Merali. "The Generative AI Revolution." https://www.ukonward.com/wp-content/uploads/2023/05/Generative-AI-Revolution-Final.pdf]